Compiled from the communication research corpus
Principles, forms, and playbooks for every artifact you write — with the evidence in the margin.
Every claim in this guide keeps its receipts. Prose stays in the reading column; the guide's own markup — sources, cautions, do-and-avoid marks — sits in the margin beside the sentence it supports, so the reasoning is inspectable without leaving the page.
The guide compiles from one canonical corpus. Filter any register with the search field in the chapter bar; print freely — the margins travel with the text.
The commitments underneath every playbook that follows. Each carries its sources.
Intent before format. Choose the reader task and decision before choosing a document, page, diagram, or deck.
Evidence before authority. Make source quality, applicability, uncertainty, and reasoning inspectable. Expertise is demonstrated through traceability.
Preserve depth, publish layers. Keep full evidence and provenance in the canonical layer. Publish progressively reduced views for each audience and channel.
Build knowledge once; render many ways. Separate claims, concepts, evidence, decisions, examples, and procedures from individual output layouts.
Structure is meaning. Semantic headings, relationships, labels, and content boundaries serve humans, assistive technology, search, and retrieval systems.
Explain why before how. Connect purpose, problem, evidence, causality, priority, choice, and accepted trade-offs before implementation detail.
Trade-offs over advocacy. Show what improves, what worsens, who benefits, who pays, and what future options are preserved or constrained.
Personas are bounded models. Use personas only for declared decisions and contexts. Prefer behavioral evidence over invented biography.
Make reasoning inspectable. Store claims, grounds, warrants, qualifiers, assumptions, rebuttals, alternatives, and decision criteria.
Define measurable audience impact. State what the audience should know, decide, do, verify, or explain after using an artifact.
Accessibility and ethics are architectural. Do not reserve semantic structure, inclusive participation, privacy, or persuasion integrity for final review.
Version knowledge; do not overwrite it. Supersede claims and recommendations with an epistemic change log. Preserve rejected challenges and prior states.
Format is the last choice, not the first. Begin from what the reader is trying to do, and let that select the form.
Orient, teach, help act, support decisions, enable verification, operate, persuade, and retrieve.
The strongest artifact aligns reader intent, evidence strength, decision purpose, information architecture, and delivery medium.
In practice — Declare purpose, audience, decision, and intended action before drafting.
Tutorials, references, decision memos, specifications, and marketing pages optimize for different questions.
In practice — Use shared canonical knowledge with separate task-specific views.
Depth belongs in the source layer; progressive disclosure determines what each reader sees first.
In practice — Preserve full research and publish bounded views.
Paragraphs and sections should advance a claim, explain a concept, provide evidence, support action, or preserve context.
In practice — Remove or relocate units that serve no declared purpose.
The atlas: what each genre is for, its skeleton, and what it should never be asked to do — then the argument structures that organize them.
Capture one observation, source, or question.
Question → observation → source → interpretation → follow-up
Not for — Not a final conclusion.
Record what a source contributes and how reliable it is.
Citation → summary → quality → useful claims → limitations
Not for — Do not treat separate annotations as synthesis.
Map existing knowledge, debates, methods, and gaps.
Scope → method → themes → agreement → conflict → gaps → implications
Not for — Do not organize only by source.
Contribute an original argument, method, or result.
Introduction → method → results → discussion → limitations
Not for — Not an operational procedure.
Make a test reproducible and its result bounded.
Hypothesis → environment → method → metrics → results → interpretation
Not for — Do not generalize beyond workload and environment.
Explain an intervention and outcome in context.
Context → problem → intervention → outcome → limits → transferable lesson
Not for — One case is not universal proof.
Educate and establish a defensible direction.
Problem → evidence → approach → value → implementation → next action
Not for — Do not present advocacy as independent academic evidence.
Enable a rapid consequential decision.
Decision → significance → evidence → options → recommendation → risk
Not for — Do not make the decision request implicit.
Develop shared context and reasoning.
Situation → tension → evidence → alternatives → recommendation → consequences
Not for — Not ideal for primarily visual demonstrations.
Justify investment or prioritization.
Problem → strategic fit → options → net value → risk → recommendation
Not for — Do not count transferred costs as savings.
Request permission, funding, or commitment.
Need → response → scope → deliverables → resources → risk → acceptance
Not for — Not a status report.
Collect broad review before a meaningful change.
Summary → motivation → design → alternatives → compatibility → rollout → open questions
Not for — Avoid for trivial local choices.
Preserve one significant architecture decision.
Context → decision → status → consequences → revisit conditions
Not for — Not a substitute for the complete system design.
Align on user and product outcomes.
Problem → goals → requirements → non-goals → measures → constraints
Not for — Do not bury technical implementation decisions inside product language.
Describe expected behavior.
Actors → scenarios → behavior → rules → errors → acceptance
Not for — Does not replace an implementation design.
Provide an implementation and operational blueprint.
Context → requirements → architecture → interfaces → security → operations → rollout
Not for — Premature when the solution remains exploratory.
Help a learner acquire competence.
Outcome → prerequisites → guided sequence → checkpoints → result → next step
Not for — Do not double as exhaustive reference.
Help a competent reader accomplish a task.
Goal → prerequisites → steps → expected result → recovery
Not for — Do not teach the whole conceptual domain.
Support exact lookup.
Definition → syntax → parameters → constraints → examples → errors
Not for — Do not force a learning journey.
Build a mental model.
Definition → relationships → mechanism → examples → boundaries
Not for — Do not disguise procedures as theory.
Support reliable operation and recovery.
Trigger → diagnosis → action → validation → escalation → rollback
Not for — Do not bury it in architecture prose.
Learn from an incident and improve controls.
Impact → timeline → causes → contributing conditions → actions → verification
Not for — Avoid blame and retrospective certainty.
Establish a defensible point of view.
Thesis → context → evidence → synthesis → implications
Not for — Do not use novelty of tone as evidence.
Move a qualified reader toward a bounded action.
Identity → relevance → outcome → mechanism → proof → constraints → action
Not for — Do not ask for conversion before understanding.
Deliver recurring signal and utility.
Issue thesis → findings → interpretation → links → action
Not for — Do not reproduce the entire research report.
Guide a time-bound spoken argument.
Assertion → evidence → transition → decision
Not for — A deck is not the durable source of truth.
State mandatory organizational requirements and rationale.
Purpose → scope → requirements → responsibilities → exceptions → enforcement
Not for — Do not use should when must is intended.
Provide context-sensitive recommended practice.
Context → recommendation → rationale → exceptions → examples
Not for — Do not make optional guidance appear mandatory.
Experimental or systematic research
Introduction → Methods → Results → Discussion
Separates what was done, observed, and inferred.
Research and proposal introductions
Establish territory → establish niche → occupy niche
Explains why the contribution is necessary.
Research paragraphs and enterprise recommendations
Assertion → evidence → interpretation → implication → action
Prevents citation dumping without reasoning.
Conditional practical recommendations
Claim → grounds → warrant → backing → qualifier → rebuttal
Exposes hidden reasoning and exceptions.
Strategic advocacy and major presentations
Relevance → context → position → proof → refutation → action
Creates a broad persuasive arc.
Legitimate stakeholder conflict
Neutral issue → opposing view → valid conditions → own view → shared goals → resolution
Finds common ground without erasing trade-offs.
Executive decision
Decision → evidence → options → comparison → recommendation → implementation
Optimizes for a specific decision-maker.
Engineering articles and transformation narratives
Context → friction → failed obvious answer → insight → decision → implementation → outcome → limits
Uses change to explain causality and learning.
Analytical communication
Question → baseline → contrast → explanation → consequence → action
Moves from measurement to decision.
Presentations
Complete-sentence assertion + supporting visual evidence
Reduces topic headings and bullet walls.
Recommendations
Purpose → problem → evidence → causality → priority → choice → trade-off → how
Explains why before implementation.
Step sequences for the outputs you actually ship. Each starts from purpose, never from format.
Playbook
Select the correct communication form before authoring.
Playbook
Synthesize a field without becoming a list of sources.
Playbook
Preserve methodological inspectability.
Playbook
Connect design to implementation, operations, and outcomes.
Playbook
Make every diagram answer a declared concern.
Playbook
Prevent a static profile from substituting for context.
Playbook
Turn a persona into governed research infrastructure.
Playbook
Define a measurable communication outcome.
Playbook
Make a recommendation inspectable.
Playbook
Explain why before how.
Playbook
Enable a defensible enterprise choice.
Playbook
Prevent short-term value from hiding future cost.
Playbook
Build a deck around claims rather than topics.
Playbook
Create reusable recurring intelligence.
Playbook
Move from question to governed publication.
Playbook
Enable reproduction, audit, red-team, and extension.
Playbook
Challenge without silently rewriting history.
Playbook
Render one knowledge base into many channels.
How claims earn belief: source hierarchies, argument skeletons, and the discipline of showing your uncertainty.
Background alone does not establish why new work is needed.
In practice — Use territory, niche, and contribution moves.
Readers need to inspect how evidence was produced before accepting conclusions.
In practice — Use IMRaD for experiments, benchmarks, surveys, and formal evaluations.
Source-by-source summaries transfer analytical work to the reader.
In practice — Organize agreement, disagreement, methods, limitations, gaps, and implications.
Standards, experiments, company practices, and professional heuristics support different kinds of conclusions.
In practice — Label evidence class, scope, confidence, and limitations.
The source may provide context, a method, partial support, or direct contradiction.
In practice — Record citation intent and exact source locators.
Evidence does not determine a recommendation without a rule connecting observation to action.
In practice — Store warrants, backing, qualifiers, and rebuttals as first-class objects.
Documentation and architecture records that stay true as systems change.
It has users, journeys, quality attributes, analytics, defects, ownership, release cycles, and deprecation.
In practice — Assign product ownership, measurement, and lifecycle controls.
Tool-oriented inventories do not necessarily help people complete enterprise work.
In practice — Organize platform content around Golden Paths, paved roads, and bounded tasks.
Security, privacy, data, performance, failure, observability, rollout, rollback, support, and measures define enterprise readiness.
In practice — Adopt a complete specification standard with explicit non-goals and alternatives.
A system design changes; the reasons behind significant choices remain important for future maintainers.
In practice — Record context, options, decision, consequences, risks, and revisit conditions.
Different concerns require different abstractions, notation, and scope.
In practice — Declare audience, concern, scope, omissions, legend, and last verification.
Lines and boxes do not reliably communicate ownership, trust, failure, causality, or rationale.
In practice — Explain purpose, reading order, flows, boundaries, decisions, failures, and operational implications.
Excessive component or code detail can obscure enterprise boundaries and responsibilities.
In practice — Start at the highest useful abstraction and add detail only for an explicit question.
Narrative that carries meaning without inflating it — inside the company and out.
A useful narrative connects an initial state, goal, tension, choice, changed state, and implication.
In practice — Use narrative to guide attention, then reconnect the case to aggregate evidence.
Narratives can be memorable enough to overpower base rates and broader data.
In practice — Label examples as illustrative and pair them with measured frequency or scope.
Dense slides compete with narration and are difficult to reuse independently.
In practice — Use assertion–evidence slides with notes, appendices, and a companion source document.
Demonstrated outcomes, customer reports, projections, capabilities, and aspirations have different epistemic status.
In practice — Classify every external claim before publication.
Generic labels can move readers into flows before they understand the consequence.
In practice — Use action-specific labels appropriate to audience authority and readiness.
A list of new content does not create sustained utility.
In practice — Lead with a signal, explain significance, provide evidence, and use one primary action.
Gating basic understanding weakens trust and does not establish durable premium value.
In practice — Monetize depth, proprietary data, tools, benchmarks, support, and customization.
Modeling readers without stereotyping them.
Populations, segments, archetypes, situations, impact contracts, validation, ethics, and lifecycle.
It is not an average user, demographic stereotype, role, or real participant.
In practice — State population, decision domain, situation, confidence, and prohibited uses.
Combining many details can create an improbable composite that matches few real people.
In practice — Include only attributes that materially change a decision or communication need.
Names, photographs, and lifestyle details may improve memory while increasing projection and stereotyping.
In practice — Prefer archetypes and behavioral labels for enterprise work.
The same senior engineer has different needs while learning, troubleshooting, approving, or presenting.
In practice — Use a Persona Stack: population → segment → persona → situation → baseline → impact.
Implementers, approvers, operators, reviewers, affected parties, and future maintainers often have conflicting concerns.
In practice — Define one primary reading path and layered secondary views.
“Inform the audience” is neither observable nor testable.
In practice — Specify baseline, desired knowledge, decision, behavior, evidence threshold, guardrails, and measure.
Observed, reported, measured, inferred, hypothesized, and illustrative data have different validity.
In practice — Maintain an attribute-level evidence matrix.
Recruiting only people who fit an existing profile cannot reveal missing segments or invalid assumptions.
In practice — Include contrast cases, holdout samples, disconfirming evidence, and participants who fit none.
Models can generate plausible language without reproducing population behavior or prevalence.
In practice — Label synthetic output as hypothesis and validate with real participants and operational data.
Saying “the persona needs this” can erase the actual sample and variation.
In practice — Report participant findings first, then link the bounded synthesis.
Moving a decision while leaving the reader's judgment intact.
Logos, ethos, pathos, elaboration, reactance, inoculation, framing, uncertainty, and argument structures.
The purpose is not agreement at any cost; it is a defensible decision made with material evidence and consequences visible.
In practice — Evaluate persuasive effectiveness and epistemic integrity separately.
Reasoning establishes defensibility, credibility establishes trust, and consequence establishes significance.
In practice — Let emotional force remain proportional to evidence strength.
Decision-makers may scan rapidly but consequential decisions require substantive scrutiny.
In practice — Lead with decision, stakes, strongest evidence, and trade-off; preserve full methods and analysis.
Expertise about one product or method does not guarantee independence or applicability elsewhere.
In practice — Assess competence, integrity, independence, accountability, verifiability, relevance, and currency.
Mandates, artificial inevitability, and suppression of alternatives threaten perceived autonomy.
In practice — Separate mandatory constraints from recommendations, preserve exceptions, and explain reversibility.
Inoculation and prebunking expose the audience to objections and reasoned responses.
In practice — Steelman predictable counterarguments and define conditions where they are valid.
Gain, loss, cost, status-quo, and downside frames activate different reference points.
In practice — Evaluate every material option through multiple equivalent frames.
Vague uncertainty is less useful than a range, cause, sensitivity, and operational consequence.
In practice — Separate measurement, forecast, implementation, market, and behavioral uncertainty.
Claims are conditional; warrants, qualifiers, and rebuttals determine whether a recommendation actually follows.
In practice — Expose the complete argument map.
Security versus speed and standardization versus autonomy can both contain valid interests.
In practice — Identify shared goals and create bounded defaults with explicit escape conditions.
A recommendation earns trust by naming what it costs, in the dimensions the business actually counts.
Net value, beneficiary and cost bearer, reversibility, option value, debt, lock-in, and time horizons.
Revenue and labor savings omit customer, operational, strategic, technical, workforce, risk, governance, and learning outcomes.
In practice — Use a complete value model and name the beneficiary.
A capability is not automatically an outcome. Every link from technical change to business consequence needs evidence or a labeled hypothesis.
In practice — Store outcome → beneficiary → mechanism → measure → baseline → horizon → uncertainty.
A proposal may save application-team time while increasing platform, operations, security, procurement, or future-maintainer burden.
In practice — Name both beneficiary and cost bearer.
Transition disruption, near-term adoption, medium-term operating cost, long-term lock-in, and exit obligations are different analyses.
In practice — Evaluate immediate, near, medium, long, and exit horizons.
A decision can be technically undoable yet economically, contractually, or politically locked in.
In practice — Estimate exit time, retained obligations, lost data, retraining, and dependency unwinding.
Pilots, interfaces, dual-run capability, export paths, and stop/go milestones preserve future choices.
In practice — Ask what later decisions become easier or harder.
The metaphor is useful only when the shortcut, benefit, principal, interest, owner, and repayment trigger are explicit.
In practice — Do not use technical debt as a generic label for disliked code.
Data export alone does not address API, identity, telemetry, skill, contract, process, and organizational lock-in.
In practice — Assess switching cost across every dependency dimension.
Speculative learning cannot be governed only by the utilization and predictability metrics of mature operations.
In practice — Classify investments as core scale, experiment, option creation, maintenance, or debt repayment.
Readers now include machines. Structure, metadata, and chunking decide whether your work is found and quoted faithfully.
Semantic chunks, local context, reranking, validation, authoritative sources, and model-independent adapters.
Descriptive headings, bounded topics, explicit entities, local context, citations, dates, and versions support both people and retrieval.
In practice — Design semantic content units before embedding or indexing them.
Current search guidance does not require special AI-only schemas in place of people-first content and established indexing foundations.
In practice — Make claims distinctive, indexable, verifiable, internally linked, and textually available.
Fixed character windows can split claims from context, evidence, limitations, or procedures.
In practice — Use concepts, claims, decisions, procedures, failure modes, and examples as retrieval units.
Pronouns and references such as “this approach” lose meaning when isolated.
In practice — Use explicit nouns, defined terms, source IDs, scope, and limitations inside each unit.
Large undifferentiated inputs increase noise and can reduce model performance.
In practice — Retrieve bounded evidence, rerank it, and validate generated output against authority.
Models can classify, summarize, restructure, and generate alternate views while introducing unsupported facts or losing boundaries.
In practice — Validate citations, technical facts, dates, policies, and schemas before publication.
The implementation visualizes typed metadata, outgoing links, citations, and backlinks and gives every concept a shareable fragment deep link.
In practice — Generate stable concept anchors and backlink indexes in the SPA and graph view; compile typed research relationships into JSON-LD.
Schema.org JSON-LD should accurately describe visible public content, while the internal graph can express richer provenance, citation intent, claims, challenges, and agent activities.
In practice — Generate a constrained public JSON-LD block and a downloadable enterprise research graph from the same canonical OKF bundle.
The named ways good intentions go wrong. Recognize the shape early.
Failure mode
Failure mode
Failure mode
Failure mode
Failure mode
Failure mode
Failure mode
Failure mode
Failure mode
Failure mode
Knowledge that outlives its author: versioning, provenance, and review that prove themselves.
A final article hides the source graph, reasoning, unresolved questions, and rejected alternatives.
In practice — Store sources, claims, evidence, warrants, assumptions, recommendations, challenges, and outputs independently.
Resources change, disappear, or are silently revised.
In practice — Store canonical and archived URIs, retrieval date, publication date, version, hash, license, and exact locator.
Future reviewers need to distinguish support, dispute, method reuse, extension, and background.
In practice — Type each citation relationship.
Document-level references force later agents to rediscover the passage and may invite unsupported paraphrase.
In practice — Store quotation selectors, prefixes, suffixes, page or section, and associated claim.
Future researchers need to know which model, prompt, retrieval set, tools, and human review generated a transformation.
In practice — Represent sources, activities, agents, derivations, approvals, and rejections.
A future model should not require the current vendor, context-window design, or proprietary application.
In practice — Use Markdown, JSON/YAML with schemas, JSON-LD, CSV/Parquet, text diagrams, IDs, hashes, and tests.
Silently editing a conclusion destroys the history of why confidence changed.
In practice — Append challenges, resolution status, evidence, and epistemic change logs.
A more capable model should demonstrate citation fidelity, claim calibration, argument completion, trade-off completeness, persona consistency, and temporal updating.
In practice — Store test cases with expected criteria, not one frozen answer.
Correct information becomes unsafe when its verification date, scope, or replacement relationship is unknown.
In practice — Store status, owner, version, review triggers, supersession, and retirement.
Must, should, may, and can communicate different authority. Ambiguity creates implementation and audit failures.
In practice — Define normative terms and separate policy, standard, procedure, and guideline.
Clicks and time can signal interest, confusion, or friction.
In practice — Measure comprehension, decision accuracy, task completion, error, retrieval, confidence calibration, and accessibility.
Enterprise decisions are made under bounded evidence and changing environments.
In practice — Define assumptions, success thresholds, stop conditions, rollback, and review triggers.
The agent template reads relevant knowledge before work, updates affected concepts afterward, appends logs, and runs validation, while deliberately avoiding hidden hooks.
In practice — Require proposed diffs, contribution records, human review for material changes, and an epistemic log that preserves superseded states.
The creator of an underlying paper, specification, repository, or video remains the source author even when a human researcher and AI system transform it into a new synthesis.
In practice — Record source creator and publisher, human research owner, software-agent contribution, derivation activity, review status, and rights separately.
PROV-O can represent a software agent associated with an activity while the resulting entity remains attributed to the accountable human or organization.
In practice — Use contribution roles such as researched, synthesized, drafted, generated, and validated; identify the human who directed, reviewed, approved, and owns the work.
Findings whose domains sit outside the registered thirteen — kept visible rather than filed away.
Its minimal Markdown, YAML, file-tree, index, log, link, and citation conventions maximize portability while intentionally leaving domain schemas and relationship semantics to producers.
In practice — Use OKF for canonical authoring and exchange; add a versioned enterprise research profile, JSON Schema validation, and compiled JSON-LD semantics.
The okf-skills implementation distinguishes shared curated knowledge from standing behavioral instructions and tool-specific implicit memory.
In practice — Keep research in the OKF bundle, agent behavior in skills or instruction files, and disposable session memory outside the source of truth.
The implementation pairs agent authoring with a strict validator rather than relying on an agent to judge its own format compliance.
In practice — Validate reserved files, YAML parsing, required type fields, local schemas, IDs, internal links, citations, and generated derivatives in CI.
okf-skills documents its own skills, components, reference specification, and architectural decisions as an OKF bundle that is validated on change.
In practice — Store the communication research system in the same format it recommends and compile the SPA from that canonical bundle.
Every source in the library carries one of these classes; margin notes inherit them. Stronger classes earn stronger verbs.
The 90 sources behind the guide. Chips elsewhere link here; external links open the originals.
documentation framework
Reference as — Use distinct documentation forms for distinct user needs.
Reader intent and the separation of tutorials, how-to guides, reference, and explanation.
documentation practice
Reference as — Modular content can be composed into different experiences.
Concept, procedure, and reference modules that answer bounded user questions.
accessibility standard
Reference as — Presentation alone must not carry essential structure.
Semantic representation of structure and relationships.
academic university
Reference as — A strong argument advances through explicit claims and subclaims.
Thesis decomposition, paragraph purpose, evidence, and analysis.
academic university
Reference as — Evidence must match the kind of claim being made.
Discipline-appropriate forms of evidence and their use.
academic university
Reference as — Organize existing work by themes, debates, methods, and gaps.
Literature reviews as synthesis rather than source-by-source summaries.
academic university
Reference as — Facts become arguments only when connected by reasoning.
Claims, evidence, reasoning, and counterargument.
academic university
Reference as — Separate observation from method and interpretation.
Introduction, methods, results, and discussion.
academic primary
Reference as — Introductions should show importance, unresolved need, and contribution.
Establish territory, establish niche, occupy niche.
documentation practice
Reference as — Headings should expose purpose and structure.
Descriptive, unique, hierarchical headings.
documentation practice
Reference as — Local clarity improves human and machine interpretation.
Consistent terminology, explicit nouns, and restrained acronyms.
technical practice
Reference as — Specifications must connect implementation to business and operations.
Functional versus technical specifications, rollout, security, support, and metrics.
technical practice
Reference as — Design documents are shared alignment and decision artifacts.
Architecture, interfaces, assumptions, dependencies, constraints, and trade-offs.
enterprise practice
Reference as — Different narrative forms serve different organizational decisions.
Narrative mechanisms including PR/FAQ, reviews, readiness, and correction of error.
architecture framework
Reference as — Preserve why a decision was made, including negative consequences.
Context, decision, status, and consequences for significant architecture choices.
architecture standard
Reference as — One diagram cannot represent every architectural concern.
Stakeholders, concerns, viewpoints, and views.
architecture framework
Reference as — Use progressive architectural zoom for different audiences.
Hierarchical system, container, component, and code views.
platform practice
Reference as — Documentation works best when attached to supported product paths.
Supported journeys, tutorials, tooling, and platform enablement.
platform practice
Reference as — Documentation ownership should follow software ownership.
Documentation close to code and changed through engineering workflows.
platform practice
Reference as — Standardization requires productized enablement, not prose alone.
Supported practices and tools made easier than unsupported alternatives.
platform practice
Reference as — Organize platform docs by audience journey and lifecycle.
Role- and task-separated documentation, API reference, RFCs, and stability annotations.
llm practice
Reference as — Provide models only relevant context and validate output against authority.
Bounded relevant context, documentation, schema validation, and corrective retries.
llm practice
Reference as — Retrieval systems require evaluation, not only generation quality.
Semantic retrieval, reranking, and retrieval-quality measurement.
story review
Reference as — Story guides attention but can influence beyond evidence strength.
Attention, imagery, emotion, and immersion in narratives.
story review
Reference as — Data storytelling is broader than a single narrative formula.
Narrative, visualization, cognition, and interaction in data storytelling.
presentation university
Reference as — Slides should advance claims rather than display topic headings and bullet walls.
Complete-sentence assertions supported by visual evidence.
presentation review
Reference as — Reduce extraneous processing and align related verbal and visual information.
Coherence, signaling, redundancy, and spatial/temporal contiguity.
marketing review
Reference as — AIDA is a drafting aid, not a universal linear decision model.
Attention, interest, desire, and action as a historical persuasion heuristic.
marketing practice
Reference as — CTA labels should describe the consequence of action.
Generic CTAs can pull users into flows before they understand the offer.
newsletter practice
Reference as — Email effectiveness must be tested with the actual audience.
Clear goal, concise content, CTA, responsive design, and testing.
newsletter review
Reference as — A newsletter should deliver recurring utility.
Long-running research on subjects, preheaders, content, voice, links, mobile, and subscription.
marketing primary
Reference as — Premium value should add utility rather than gate basic understanding.
Perceived value and satisfaction in premium conversion.
persona primary
Reference as — Personas should be maintained research infrastructure.
Foundation documents, traceability, scenarios, progressive disclosure, and revision.
persona review
Reference as — Use qualitative discovery with quantitative validation where possible.
Rigor, scalability, objectivity, representation, and mixed methods.
persona primary
Reference as — A concrete profile may not represent a measurable segment.
Verification, falsifiability, and population representation concerns.
persona primary
Reference as — Only include decision-relevant attributes.
Match rates decline as many attributes are combined.
persona primary
Reference as — Humanizing details can increase projection and exclusion.
Risks of simplification and stereotyping in persona representations.
persona practice
Reference as — Use evidence-triggered review and versioning.
Personas drift as products, behavior, and environments change.
persona practice
Reference as — Label the evidence maturity of every persona.
Proto-personas, qualitative personas, and statistically supported personas.
persona practice
Reference as — Use the least fictional form that supports the decision.
Biographical representation versus abstract behavioral patterns.
persona practice
Reference as — Communication objectives should specify observable audience outcomes.
Audience knowledge, attitudes, behavior, barriers, and action.
research practice
Reference as — Persona research belongs inside a formal research plan.
Objectives, business goals, participant groups, recruitment, methods, and repositories.
persona practice
Reference as — A persona poster without decision integration is decoration.
Scope, organizational embedding, and connection to decisions.
persona practice
Reference as — Represent affected people, not only interface users.
Substantial research, participation, observed facts, and continuing reevaluation.
persona practice
Reference as — Communication personas should emphasize knowledge and use context.
Audience grouping by expertise and task.
persona practice
Reference as — Buyer and product-use personas support different decisions.
Buying role, channels, trusted sources, goals, and barriers.
persona practice
Reference as — Do not use persona opinion when hard quantitative criteria are required.
Persona use boundaries and qualitative judgment.
research standard
Reference as — Separate restricted participant data from aggregated persona outputs.
Consent, data minimization, controlled access, and deletion.
persona review
Reference as — Synthetic personas are hypotheses, not empirical participants.
Growth, evaluation gaps, and human oversight for synthetic personas.
persona draft
Reference as — Do not treat simulated responses as user research.
Emerging evidence that persona conditioning may not reproduce population behavior.
persuasion review
Reference as — Reasoning, credibility, and significance must reinforce one another.
Logos, ethos, and pathos in rhetorical persuasion.
persuasion review
Reference as — Layer enterprise decisions for both rapid orientation and substantive scrutiny.
Motivation, ability, elaboration, arguments, and cues.
persuasion review
Reference as — Credibility applies per claim and context, not universally to a brand.
Credibility, ambiguity, expertise, and audience evaluation.
persuasion review
Reference as — Separate mandates from recommendations and preserve meaningful choice.
Resistance when freedom is perceived as threatened.
persuasion review
Reference as — Address credible objections before they become external attacks.
Exposure to counterarguments and refutations.
persuasion review
Reference as — Use stories to make consequences concrete, then return to aggregate evidence.
Variation in narrative effects by audience, topic, medium, and familiarity.
persuasion review
Reference as — Test recommendations in benefit, loss, cost, status-quo, and downside frames.
Judgments change across gain, loss, and reference-point frames.
persuasion primary
Reference as — Explain uncertainty type, range, driver, and consequence.
Trust effects of explicit and quantified uncertainty.
persuasion university
Reference as — Make practical reasoning inspectable and challengeable.
Claim, grounds, warrant, backing, qualifier, and rebuttal.
persuasion university
Reference as — Use a broad persuasive arc for strategic advocacy.
Context, position, proof, refutation, and conclusion.
persuasion university
Reference as — Use when legitimate enterprise interests are in tension.
Fair presentation of opposing positions and common ground.
enterprise university
Reference as — Enterprise recommendations should be decision instruments, not generic essays.
Decision-maker focus, evidence, alternatives, feasibility, and recommendation.
tradeoff practice
Reference as — Hard-to-reverse decisions deserve stronger evidence and review.
Decision-process intensity based on reversibility.
tradeoff primary
Reference as — Evaluate which future choices an investment creates or closes.
Expansion, deferral, switching, and abandonment under uncertainty.
tradeoff primary
Reference as — Document principal, interest, benefit, trigger, and owner.
Short-term delivery versus future evolution and architectural debt.
tradeoff primary
Reference as — Portability must be evaluated across data, API, skills, contracts, and operations.
Positive feedback, accumulated investment, and lock-in.
tradeoff review
Reference as — Separate mature operations from bounded experimentation and strategic learning.
Exploration versus exploitation and organizational structure.
stewardship standard
Reference as — Research needs identifiers, metadata, provenance, relationships, and licenses.
Findability, accessibility, interoperability, and reuse.
stewardship standard
Reference as — Capture how research objects and outputs were generated.
Entities, activities, agents, derivation, attribution, and revision.
retrieval practice
Reference as — No special AI markup replaces clear authoritative content.
Indexable, people-first, internally linked, textually available content.
retrieval practice
Reference as — Do not rewrite content into artificial machine-targeted keyword patterns.
Existing quality and SEO foundations for AI-mediated discovery.
retrieval draft
Reference as — Chunk at semantic boundaries and validate against the actual corpus.
Hierarchical and structure-aware segmentation in retrieval systems.
retrieval draft
Reference as — Use LLMs for transformation with technical validation and governance.
Emerging value and limitations of automated architecture documentation.
stewardship standard
Reference as — A URL alone is insufficient for durable evidence.
Time-based access to archived states of web resources.
stewardship primary
Reference as — Apply structured documentation cards to research sources and syntheses.
Motivation, composition, collection, use, and limitations documentation.
stewardship framework
Reference as — Claims should be independently identifiable and citable.
Atomic assertions packaged with provenance and publication information.
stewardship primary
Reference as — Store the reasoning graph, not only the final recommendation.
Shared representation for structured arguments.
stewardship primary
Reference as — Record why each source is cited.
Machine-readable citation intent such as supports, disputes, or extends.
stewardship standard
Reference as — Preserve source locators, quotations, and evidence relationships.
Annotations linked to exact text or resource segments.
stewardship standard
Reference as — Use an open outer container for files, metadata, people, and software.
Lightweight JSON-LD research-object packaging.
stewardship standard
Reference as — Keep agent and human attribution stable over time.
Persistent identities for research contributors.
stewardship standard
Reference as — Use hashes and stable logical IDs for exact artifact states.
Content-based persistent identifiers for software artifacts.
Knowledge systems and provenance standard
Reference as — OKF defines a deliberately minimal interoperability envelope rather than a centrally registered domain ontology.
Canonical rules for bundles, concepts, frontmatter, cross-links, index files, logs, citations, conformance, and versioning.
Knowledge systems and provenance practice
Reference as — OKF formalizes a portable LLM-wiki pattern for human and agent consumption without requiring a proprietary runtime or SDK.
Official motivation and design principles: minimal opinion, producer/consumer independence, and format rather than platform.
Knowledge systems and provenance practice
Reference as — A community implementation demonstrates deterministic validation and a self-contained graph consumer around the minimal OKF format.
Agent production, maintenance, strict validation, visualization, deep links, portable skill distribution, and knowledge-as-code layering.
Knowledge systems and provenance practice
Reference as — Agent upkeep can be explicitly instructed and validated without hidden hooks or treating memory as the source of truth.
Read index before relevant work, update affected concepts and logs after change, then validate strictly.
Knowledge systems and provenance practice
Reference as — Different concept types can coexist in one linked bundle and be rendered into a shareable self-contained explorer.
Concrete bundle containing services, datasets, decisions, runbooks, metrics, index navigation, and a graph visualization.
Knowledge systems and provenance practice
Reference as — Dogfooding creates a feedback loop in which the knowledge system documents and validates its own design.
Self-documenting repository with skills, components, reference specifications, and architecture decisions.
Knowledge systems and provenance practice
Reference as — Track the originating human creator and disclose AI assistance as separate provenance fields.
First-party example supplied by the research owner demonstrating how OKF can be applied and communicated.
Knowledge systems and provenance standard
Reference as — Use Schema.org to describe the public artifact; use PROV-O for richer software-agent activities and derivation.
Public structured-data properties for creator, contributor, credit, copyright, dates, versions, citations, and derivation.
This guide is compiled, not authored by hand. Source: Enterprise Communication Research System v1.4.0. Every count below is computed from the data; the validation table is the build's actual run.
Viewer build records (css_reset_recalibration, ux_audit) were relocated out of the corpus into a sidecar file at compile time; the guide's data layer carries research only.
| Check | Status | Detail |
|---|---|---|
| renderedFindings | pass | 80 rendered of 80 in corpus |
| renderedReferences | pass | 90 rendered of 90 in corpus |
| renderedPlaybooks | pass | 18 rendered of 18 in corpus |
| renderedPrinciples | pass | 12 rendered of 12 in corpus |
| uniqueHtmlIds | pass | no duplicates |
| anchorsResolve | pass | all internal anchors resolve |
| buttonNames | pass | all buttons carry a name (text or aria-label) |
| referenceIntegrity | pass | every cited ref exists |
| contrastGate | pass | 23 pairs computed, all pass |
| embeddedDataParity | pass | JSON v2.0.0 = JSON-LD v2.0.0 |
| javascriptSyntax | pass | node --check clean |
| deterministicRebuild | pass | double build byte-identical |
| Theme | Pair | Colors | Ratio | Min | Result |
|---|---|---|---|---|---|
| light | body text on page | #212930 / #FBFBF8 | 14.22 | 4.5 | pass |
| light | body text on card | #212930 / #FFFFFF | 14.74 | 4.5 | pass |
| light | body text on tint | #212930 / #F1F3EC | 13.18 | 4.5 | pass |
| light | secondary text on page | #525C66 / #FBFBF8 | 6.57 | 4.5 | pass |
| light | link text on page | #20518D / #FBFBF8 | 7.72 | 4.5 | pass |
| light | focus ring on page | #6635C9 / #FBFBF8 | 6.94 | 3.0 | pass |
| light | rules/borders on page | #C9CEC4 / #FBFBF8 | 1.55 | 1.35 | pass |
| light | success text on page | #1F6B4E / #FBFBF8 | 6.19 | 4.5 | pass |
| light | warning text on page | #8A4C0C / #FBFBF8 | 6.49 | 4.5 | pass |
| light | danger text on page | #A33227 / #FBFBF8 | 6.66 | 4.5 | pass |
| light | marker holds body text | #212930 / #FFE063 | 11.3 | 4.5 | pass |
| dark | body text on page | #E6E9E3 / #131920 | 14.42 | 4.5 | pass |
| dark | body text on card | #E6E9E3 / #1B242E | 12.8 | 4.5 | pass |
| dark | body text on tint | #E6E9E3 / #161F28 | 13.59 | 4.5 | pass |
| dark | secondary text on page | #A6B0B9 / #131920 | 8.03 | 4.5 | pass |
| dark | link text on page | #92B5F2 / #131920 | 8.52 | 4.5 | pass |
| dark | focus ring on page | #B79CF5 / #131920 | 7.65 | 3.0 | pass |
| dark | rules/borders on page | #3A4652 / #131920 | 1.83 | 1.35 | pass |
| dark | success text on page | #7FCBA4 / #131920 | 9.25 | 4.5 | pass |
| dark | warning text on page | #E2B173 / #131920 | 9.07 | 4.5 | pass |
| dark | danger text on page | #EE8A7E / #131920 | 7.22 | 4.5 | pass |
| dark | marker holds dark text | #131920 / #E9C24C | 10.35 | 4.5 | pass |
| dark | highlight as accent text | #E9C24C / #131920 | 10.35 | 3.0 | pass |
{
"research_owner": {
"name": "Jesse Graupmann",
"type": "Person",
"roles": [
"research director",
"author",
"editor",
"accountable owner"
],
"responsibility": "Defines goals, directs research, evaluates conclusions, approves publication, and retains accountability for the work."
},
"ai_assistance": {
"provider": "OpenAI",
"product": "ChatGPT",
"model": "GPT-5.6 Thinking",
"type": "prov:SoftwareAgent",
"roles": [
"research discovery",
"source analysis",
"synthesis",
"drafting",
"schema design",
"HTML and JSON generation",
"validation"
],
"relationship": "Operated under the direction of Jesse Graupmann as a research and production tool.",
"accountability": "The software agent is credited for its contribution but is not the accountable author or owner."
},
"source_attribution_rule": "The creator or publisher of every underlying source remains independently attributed. Human authorship of the synthesis does not replace source authorship.",
"review_status": "Human-directed and reviewable; each future material revision should record the responsible person, software agent, source inputs, accepted changes, and rejected changes."
}{
"name": "Enterprise Research Profile for OKF",
"version": "1.0.0",
"targets": [
"OKF 0.1 Draft",
"JSON Schema",
"JSON-LD",
"W3C PROV-O"
],
"principles": [
"OKF is the portable authoring and exchange envelope, not the complete research ontology.",
"One concept per file enables bounded retrieval, deep links, independent review, and progressive disclosure.",
"Agent instructions, agent memory, and durable shared knowledge remain separate layers.",
"Conformance is checked deterministically rather than by model inspection alone.",
"Agents may propose and generate updates, but material knowledge changes require explicit provenance and human review.",
"Typed semantic relationships are compiled into JSON-LD because native OKF Markdown links are intentionally untyped."
],
"concept_types": [
"Research Source",
"Source Annotation",
"Research Claim",
"Evidence",
"Warrant",
"Assumption",
"Concept",
"Interpretation",
"Persona",
"Situation",
"Audience Impact Contract",
"Argument",
"Counterargument",
"Trade-off Analysis",
"Recommendation",
"Decision",
"Challenge",
"Procedure",
"Metric",
"Playbook",
"Output Manifest",
"Contribution Record",
"Provenance Activity",
"Generated Artifact",
"Schema Profile",
"Validation Result"
],
"operational_controls": [
"Root and directory index.md files provide progressive navigation.",
"log.md records knowledge changes in reverse chronological order.",
"Strict validation checks reserved files, frontmatter, type values, links, IDs, and local profile schemas.",
"Graph visualization exposes outgoing links, backlinks, citations, concept types, and deep links.",
"Soft-mode agent upkeep reads the bundle before relevant work and proposes updates after material changes.",
"CI validates the bundle and generated derivatives before publication."
]
}{
"operations": [
"reproduce",
"audit",
"red-team",
"extend"
],
"required_checks": [
"source integrity",
"claim support",
"argument validity",
"alternative completeness",
"temporal trade-offs",
"audience coverage",
"freshness"
],
"agent_roles": [
"evidence auditor",
"methodologist",
"argument critic",
"enterprise strategist",
"financial reviewer",
"architect",
"security reviewer",
"operator",
"persona advocate",
"affected-party advocate",
"historian",
"futurist",
"synthesis editor"
],
"canonical_formats": [
"Markdown",
"JSON",
"YAML",
"JSON-LD",
"CSV or Parquet",
"Mermaid or other text diagrams"
],
"rules": [
"Never silently overwrite prior conclusions",
"Never convert model output into user evidence",
"Never upgrade confidence without new evidence",
"Record accepted and rejected changes"
],
"okf": {
"entry_point": "okf/index.md",
"validation": "Run deterministic OKF and local schema validation before accepting generated changes.",
"agent_workflow": [
"consume relevant index and concepts",
"perform task",
"propose knowledge changes",
"record contribution",
"validate",
"human review",
"append log"
],
"provenance_policy": "AI contributions are recorded as software-agent activities acting under human direction; source creators remain attributed."
}
}{
"claim": "id: claim-content-modularity-001\nobject_type: claim\ncanonical_statement: >\n Content organized as bounded semantic modules can be assembled\n into multiple audience- and task-specific outputs.\nstatus: verified\nevidence_class: company-practice\nconfidence: medium\nscope:\n domains: [technical-documentation, enterprise-knowledge]\n exclusions: [literary-long-form]\nevidence:\n supporting:\n - source_id: REDHAT-MODULAR\n relation: supports\n - source_id: DIATAXIS\n relation: corroborates\nwarrant: >\n Separating reusable knowledge from a page layout permits controlled\n recombination without changing the underlying claim.\nlimitations:\n - Modules still require editorial assembly and connective context.\n - Excessive fragmentation can damage narrative coherence.\nprovenance:\n created_at: 2026-07-17\n last_verified_at: 2026-07-17\ngovernance:\n owner: platform-content\n review_cycle_days: 180",
"persona": "id: persona-platform-adopter-001\nobject_type: persona\nversion: 2.1\nstatus: active\nlabel: Pragmatic platform adopter\nscope:\n decision_domain: [platform-adoption, technical-documentation]\n prohibited_uses: [market-sizing, access-control, performance-evaluation]\nresearch_basis:\n methods: [interviews, architecture-review-analysis, support-analysis]\n sample:\n participants: 18\n limitations: [overrepresentation-of-early-adopters]\nbehavioral_model:\n primary_goal: Adopt a maintainable integration without losing control.\n primary_tension: Standardization helps, but opaque abstraction creates risk.\n decision_criteria: [reliability, observability, migration-effort, security, reversibility]\n evidence_preference: [measured-results, explicit-contracts, failure-scenarios]\ncommunication_contract:\n baseline: {knowledge: moderate, trust: skeptical}\n desired_impact:\n understand: [ownership-boundaries, routing-and-failure-model]\n decide: [whether-to-run-pilot]\n act: [complete-migration-assessment]\n prohibited_messaging: [seamless, zero-risk, eliminates-lock-in]\nvalidation:\n unresolved_questions: [prevalence, regulated-team-differences]",
"argument": "id: argument-ai-gateway-default-001\nobject_type: argument\nclaim: Adopt a shared AI gateway as the default model-access path.\ngrounds:\n - Teams duplicate provider authentication, logging, retry, policy, and reporting.\nwarrant: >\n Capabilities repeated across services can be governed and maintained more\n consistently as a supported platform capability.\nbacking:\n - internal-duplication-analysis\n - incident-history\n - platform-engineering-research\nqualifier: >\n Applies to standard inference workloads within the measured latency envelope.\nrebuttal:\n - Unsupported provider capabilities may justify direct integration.\n - Hard real-time or legally isolated workloads may require exceptions.\nassumptions:\n - The platform has funded operational ownership.\n - Emergency bypass and rollback are tested.\nstatus: proposed",
"challenge": "id: challenge-persuasive-essay-004\nobject_type: challenge\ntype: overgeneralization\ntarget:\n claim_id: claim-narrative-persuasion-002\n version: 1.3.0\nchallenge: >\n The cited evidence contains consumer and health communication studies;\n direct enterprise transfer is not yet established.\nbasis: [scope-mismatch, missing-enterprise-validation]\nseverity: medium\nstatus: open\nproposed_resolution: [narrow-scope, add-enterprise-study, label-transfer-inference]\nraised_by:\n agent: future-model-id\n date: 2027-08-11",
"handoff": "research_handoff:\n title: Enterprise Communication Research System\n version: 1.0.0\n status: active-research\n last_reviewed: 2026-07-17\n purposes: [preserve, reproduce, audit, red-team, expand]\n start_here:\n human: README.md\n agent: manifests/agent-entry.yaml\n source_of_truth:\n claims: knowledge/claims/\n sources: sources/\n provenance: provenance/\n decisions: deliberation/decisions/\n known_weaknesses:\n - limited non-Western rhetoric research\n - incomplete empirical evaluation of enterprise persona impact\n - limited longitudinal measurement of documentation outcomes\n prohibited_agent_behavior:\n - do not delete prior claims\n - do not convert inference to fact\n - do not upgrade confidence without evidence\n - do not silently repair citations\n required_agent_outputs:\n - findings\n - citations\n - contradictions\n - proposed_changes\n - confidence\n - provenance_record",
"assembly": "id: architecture-article-ai-gateway\nobject_type: output-manifest\noutput_type: long-form-technical-article\npurpose: {primary: explain, secondary: [build-confidence, support-decision]}\naudience_constellation:\n primary_persona: persona-platform-adopter-001\n secondary_personas: [persona-security-reviewer-002, persona-platform-leader-003]\n affected_profiles: [production-operations, application-support]\nsituations: [evaluating-adoption, preparing-architecture-review]\ncontent_rules:\n define: [semantic-routing, provider-fallback]\n evidence_required: [architecture-contract, security-control-map, measured-latency]\n objections_to_address: [lock-in, debuggability, central-failure]\n prohibited_claims: [zero-latency, eliminates-provider-lock-in]\n hierarchy: [problem, operating-model, architecture, evidence, failure, adoption]\n primary_cta: run-readiness-assessment\nmeasurement:\n comprehension: [responsibility-model-score]\n decision: [assessment-completion]\n guardrail: [critical-security-misinterpretation-rate]",
"source_record": "{\n \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n \"$id\": \"https://example.org/schemas/research-source.schema.json\",\n \"title\": \"Research Source Record\",\n \"type\": \"object\",\n \"required\": [\"id\", \"title\", \"canonical_url\", \"source_type\", \"attribution_status\"],\n \"properties\": {\n \"id\": {\"type\": \"string\"},\n \"title\": {\"type\": \"string\"},\n \"canonical_url\": {\"type\": \"string\", \"format\": \"uri\"},\n \"source_type\": {\"type\": \"string\"},\n \"creators\": {\"type\": \"array\", \"items\": {\"$ref\": \"#/$defs/agent\"}},\n \"publisher\": {\"$ref\": \"#/$defs/agent\"},\n \"authorship_origin\": {\"enum\": [\"human\", \"organization\", \"human-directed-ai-assisted\", \"ai-generated\", \"mixed\", \"unknown\"]},\n \"attribution_status\": {\"enum\": [\"verified\", \"publisher-only\", \"pending\", \"unknown\"]},\n \"ai_disclosure\": {\"type\": [\"object\", \"null\"]},\n \"published_at\": {\"type\": [\"string\", \"null\"]},\n \"retrieved_at\": {\"type\": \"string\"},\n \"content_hash\": {\"type\": [\"string\", \"null\"]},\n \"license\": {\"type\": [\"string\", \"null\"]}\n },\n \"$defs\": {\n \"agent\": {\n \"type\": \"object\",\n \"required\": [\"name\", \"type\"],\n \"properties\": {\n \"id\": {\"type\": [\"string\", \"null\"]},\n \"name\": {\"type\": \"string\"},\n \"type\": {\"enum\": [\"Person\", \"Organization\", \"SoftwareAgent\"]},\n \"role\": {\"type\": [\"string\", \"null\"]}\n }\n }\n }\n}",
"contribution_record": "{\n \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n \"$id\": \"https://example.org/schemas/contribution-record.schema.json\",\n \"title\": \"Contribution Record\",\n \"type\": \"object\",\n \"required\": [\"id\", \"target_id\", \"agent\", \"roles\", \"activity\", \"timestamp\", \"review_status\"],\n \"properties\": {\n \"id\": {\"type\": \"string\"},\n \"target_id\": {\"type\": \"string\"},\n \"agent\": {\n \"type\": \"object\",\n \"required\": [\"name\", \"type\"],\n \"properties\": {\n \"name\": {\"type\": \"string\"},\n \"type\": {\"enum\": [\"Person\", \"Organization\", \"SoftwareAgent\"]},\n \"provider\": {\"type\": [\"string\", \"null\"]},\n \"model\": {\"type\": [\"string\", \"null\"]}\n }\n },\n \"roles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n \"activity\": {\"type\": \"string\"},\n \"acted_on_behalf_of\": {\"type\": [\"string\", \"null\"]},\n \"inputs\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n \"outputs\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n \"timestamp\": {\"type\": \"string\", \"format\": \"date-time\"},\n \"review_status\": {\"enum\": [\"unreviewed\", \"accepted\", \"accepted-with-edits\", \"rejected\"]},\n \"reviewed_by\": {\"type\": [\"string\", \"null\"]},\n \"notes\": {\"type\": [\"string\", \"null\"]}\n }\n}",
"okf_concept_profile": "{\n \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n \"$id\": \"https://example.org/schemas/enterprise-research-okf-profile.schema.json\",\n \"title\": \"Enterprise Research OKF Concept Profile\",\n \"type\": \"object\",\n \"required\": [\"type\", \"title\", \"timestamp\", \"status\", \"owner\", \"provenance\"],\n \"properties\": {\n \"type\": {\"type\": \"string\"},\n \"title\": {\"type\": \"string\"},\n \"description\": {\"type\": [\"string\", \"null\"]},\n \"resource\": {\"type\": [\"string\", \"null\"]},\n \"tags\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n \"timestamp\": {\"type\": \"string\", \"format\": \"date-time\"},\n \"status\": {\"enum\": [\"hypothesis\", \"supported\", \"validated\", \"active\", \"review-required\", \"superseded\", \"retired\"]},\n \"owner\": {\"type\": \"string\"},\n \"confidence\": {\"enum\": [\"high\", \"medium\", \"low\", \"not-applicable\"]},\n \"relationships\": {\"type\": \"object\", \"additionalProperties\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}},\n \"provenance\": {\n \"type\": \"object\",\n \"required\": [\"directed_by\", \"generated_by\", \"review_status\"],\n \"properties\": {\n \"directed_by\": {\"type\": \"string\"},\n \"generated_by\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n \"review_status\": {\"type\": \"string\"},\n \"source_ids\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n }\n }\n },\n \"additionalProperties\": true\n}"
}[
{
"id": "recommendation-01",
"priority": "P0",
"area": "Foundation",
"title": "Create a canonical research-object repository.",
"rationale": "Store sources, claims, evidence, concepts, assumptions, limitations, decisions, challenges, audiences, and outputs separately.",
"references": [
"FAIR",
"NANOPUB",
"W3C-PROV"
]
},
{
"id": "recommendation-02",
"priority": "P0",
"area": "Evidence",
"title": "Require claim-level provenance.",
"rationale": "Every material factual claim needs an evidence class, exact source relationship, scope, confidence, limitations, and last verification.",
"references": [
"UNC-EVIDENCE",
"CITO",
"WEB-ANNOTATION"
]
},
{
"id": "recommendation-03",
"priority": "P0",
"area": "Argument",
"title": "Store warrants, qualifiers, and rebuttals.",
"rationale": "A recommendation without explicit reasoning cannot be reliably audited or red-teamed.",
"references": [
"PURDUE-TOULMIN",
"AIF"
]
},
{
"id": "recommendation-04",
"priority": "P0",
"area": "Audience",
"title": "Use audience constellations and impact contracts.",
"rationale": "Define the primary consumer, decision maker, implementer, operator, reviewer, affected party, and intended measurable outcome.",
"references": [
"WHO-ACTIONABLE",
"IEEE-42010"
]
},
{
"id": "recommendation-05",
"priority": "P0",
"area": "Governance",
"title": "Version knowledge objects instead of overwriting them.",
"rationale": "Use status, owner, semantic version, content hash, supersession, review trigger, and epistemic change log.",
"references": [
"W3C-PROV",
"FAIR"
]
},
{
"id": "recommendation-06",
"priority": "P0",
"area": "Integrity",
"title": "Adopt an enterprise persuasion integrity standard.",
"rationale": "No recommendation may conceal material alternatives, negative consequences, uncertainty, cost transfer, or evidence limitations.",
"references": [
"PURDUE-TOULMIN",
"FRAMING-REVIEW"
]
},
{
"id": "recommendation-07",
"priority": "P0",
"area": "Accessibility",
"title": "Make semantic structure and accessible meaning non-negotiable.",
"rationale": "Use real headings, labels, tables, relationships, keyboard paths, readable contrast, and non-color cues in every output.",
"references": [
"WCAG-INFO-REL"
]
},
{
"id": "recommendation-08",
"priority": "P0",
"area": "Preservation",
"title": "Preserve source states and exact locators.",
"rationale": "Store canonical and archive URLs, retrieval date, version, hash, license, and passage-level annotations.",
"references": [
"MEMENTO",
"WEB-ANNOTATION"
]
},
{
"id": "recommendation-09",
"priority": "P1",
"area": "Workflow",
"title": "Adopt the staged research workflow.",
"rationale": "Frame, collect, assess, extract, synthesize, validate, publish, measure, and maintain.",
"references": [
"IBM-RESEARCH-PLANNING",
"FAIR"
]
},
{
"id": "recommendation-10",
"priority": "P1",
"area": "Routing",
"title": "Add a reader-intent router to every project.",
"rationale": "Determine whether the primary need is orientation, learning, action, decision, verification, operation, persuasion, or retrieval.",
"references": [
"DIATAXIS"
]
},
{
"id": "recommendation-11",
"priority": "P1",
"area": "Technical",
"title": "Standardize the enterprise technical specification.",
"rationale": "Include goals, non-goals, architecture, data, security, failure, observability, capacity, testing, migration, rollout, rollback, ownership, alternatives, and measures.",
"references": [
"MONDAY-TECH-SPEC",
"ATLASSIAN-SDD"
]
},
{
"id": "recommendation-12",
"priority": "P1",
"area": "Architecture",
"title": "Require viewpoint contracts for diagrams.",
"rationale": "Each view declares audience, concern, scope, abstraction, omissions, notation, source, owner, and date.",
"references": [
"IEEE-42010",
"C4"
]
},
{
"id": "recommendation-13",
"priority": "P1",
"area": "Persona",
"title": "Create persona foundation packages, not posters.",
"rationale": "Maintain operational card, foundation document, evidence matrix, scenarios, impact contracts, and lifecycle record.",
"references": [
"MICROSOFT-PERSONAS"
]
},
{
"id": "recommendation-14",
"priority": "P1",
"area": "Research",
"title": "Prevent circular persona validation.",
"rationale": "Recruit contrast cases, allow people to fit multiple or no personas, and validate against holdout and operational data.",
"references": [
"NNG-PERSONA-FAIL",
"PERSONA-QUANT-REVIEW"
]
},
{
"id": "recommendation-15",
"priority": "P1",
"area": "Persuasion",
"title": "Use Toulmin maps for consequential recommendations.",
"rationale": "Expose claim, grounds, warrant, backing, qualifier, rebuttal, assumptions, and alternatives.",
"references": [
"PURDUE-TOULMIN"
]
},
{
"id": "recommendation-16",
"priority": "P1",
"area": "Value",
"title": "Require net-value and causal-chain analysis.",
"rationale": "Identify beneficiary, mechanism, measure, baseline, uncertainty, direct cost, operating cost, transition cost, opportunity cost, and cost bearer.",
"references": [
"POLICY-MEMO",
"SEI-TECH-DEBT"
]
},
{
"id": "recommendation-17",
"priority": "P1",
"area": "Trade-offs",
"title": "Use time-expanded trade-off analysis.",
"rationale": "Evaluate immediate, near, medium, long-term, and exit effects, including reversibility, option value, debt, and lock-in.",
"references": [
"REAL-OPTIONS",
"PATH-DEPENDENCE"
]
},
{
"id": "recommendation-18",
"priority": "P1",
"area": "Presentations",
"title": "Use assertion–evidence presentation architecture.",
"rationale": "Make each slide advance a claim with visual evidence and retain a companion source document.",
"references": [
"ASSERTION-EVIDENCE",
"MAYER-MULTIMEDIA"
]
},
{
"id": "recommendation-19",
"priority": "P1",
"area": "Newsletters",
"title": "Compile newsletters from atomic signal units.",
"rationale": "Each item contains a finding, significance, evidence, interpretation, action, and canonical deep link.",
"references": [
"NNG-NEWSLETTER",
"MAILCHIMP-EMAIL"
]
},
{
"id": "recommendation-20",
"priority": "P1",
"area": "Marketing",
"title": "Classify claims before external publication.",
"rationale": "Distinguish demonstrated outcome, customer report, internal measurement, model, capability, aspiration, and opinion.",
"references": [
"UNC-EVIDENCE",
"SOURCE-CREDIBILITY"
]
},
{
"id": "recommendation-21",
"priority": "P1",
"area": "Retrieval",
"title": "Build semantic retrieval units.",
"rationale": "Chunk by claim, concept, decision, procedure, example, failure mode, and evidence discussion—not fixed character count alone.",
"references": [
"RAG-CHUNKING",
"GOOGLE-WORDS"
]
},
{
"id": "recommendation-22",
"priority": "P1",
"area": "Validation",
"title": "Validate generated outputs against authority.",
"rationale": "Check source existence, citation support, dates, technical contracts, normative language, and schema validity.",
"references": [
"AIRBNB-GRAPHQL"
]
},
{
"id": "recommendation-23",
"priority": "P1",
"area": "Red team",
"title": "Create structured challenge and resolution records.",
"rationale": "Future agents should append criticism, severity, basis, evidence, status, and proposed resolution.",
"references": [
"AIF",
"W3C-PROV"
]
},
{
"id": "recommendation-24",
"priority": "P2",
"area": "Compiler",
"title": "Build a communication compiler.",
"rationale": "Use canonical objects plus persona and output manifests to generate articles, SPAs, decks, specs, newsletters, and retrieval chunks.",
"references": [
"REDHAT-MODULAR",
"FAIR"
]
},
{
"id": "recommendation-25",
"priority": "P2",
"area": "Retrieval",
"title": "Implement argument-aware retrieval.",
"rationale": "Retrieve claims with grounds, warrants, scope, limitations, counterarguments, and source annotations.",
"references": [
"AIF",
"CITO"
]
},
{
"id": "recommendation-26",
"priority": "P2",
"area": "Agents",
"title": "Use intentionally conflicting review agents.",
"rationale": "Separate evidence auditor, methodologist, argument critic, strategist, architect, security reviewer, operator, persona advocate, and historian.",
"references": [
"W3C-PROV"
]
},
{
"id": "recommendation-27",
"priority": "P2",
"area": "Evaluation",
"title": "Create a future-model evaluation suite.",
"rationale": "Test citation fidelity, claim calibration, argument completion, trade-off completeness, persona consistency, temporal updates, and hallucination resistance.",
"references": [
"AIRBNB-VOICE",
"FAIR"
]
},
{
"id": "recommendation-28",
"priority": "P2",
"area": "Provenance",
"title": "Capture full LLM activity provenance.",
"rationale": "Record model, version, prompt, retrieval inputs, tool outputs, date, reviewer, accepted changes, rejected changes, and known failures.",
"references": [
"W3C-PROV"
]
},
{
"id": "recommendation-29",
"priority": "P2",
"area": "Semantics",
"title": "Add source annotations and typed citation relationships.",
"rationale": "Link exact source passages to claims and state whether a citation supports, disputes, extends, or provides method or background.",
"references": [
"WEB-ANNOTATION",
"CITO"
]
},
{
"id": "recommendation-30",
"priority": "P2",
"area": "Analysis",
"title": "Add scenario and sensitivity modeling.",
"rationale": "Show how recommendations change under volume, cost, regulation, adoption, failure, and time-horizon assumptions.",
"references": [
"REAL-OPTIONS",
"UNCERTAINTY-TRUST"
]
},
{
"id": "recommendation-31",
"priority": "P2",
"area": "Packaging",
"title": "Publish a RO-Crate-compatible continuity package.",
"rationale": "Bundle README, manifest, source registry, schemas, research objects, provenance, challenges, outputs, evaluations, and changelog.",
"references": [
"RO-CRATE",
"FAIR"
]
},
{
"id": "recommendation-r32",
"priority": "P0",
"area": "Canonical knowledge",
"title": "Adopt an OKF-compatible bundle as the source of truth.",
"rationale": "The SPA, JSON, JSON-LD, search index, and future output formats should be generated derivatives rather than isolated canonical documents.",
"references": [
"OKF-SPEC",
"OKF-GOOGLE-BLOG"
]
},
{
"id": "recommendation-r33",
"priority": "P0",
"area": "Authorship and provenance",
"title": "Record source creators, human ownership, and AI contribution separately.",
"rationale": "This preserves credit, accountability, derivation, and evidence lineage while showing where AI provided leverage.",
"references": [
"W3C-PROV",
"SCHEMA-CREATIVEWORK"
]
},
{
"id": "recommendation-r34",
"priority": "P0",
"area": "Validation",
"title": "Add deterministic OKF, schema, link, and citation checks.",
"rationale": "Agents should not be the sole judge of the artifacts they generate. Validation results should be stored and run in CI.",
"references": [
"OKF-SKILLS",
"OKF-SPEC"
]
},
{
"id": "recommendation-r35",
"priority": "P1",
"area": "Agent workflow",
"title": "Use a soft-mode consume, propose, validate, and review loop.",
"rationale": "Agents should consult relevant concepts before work and propose traceable updates afterward without silently rewriting the knowledge base.",
"references": [
"OKF-SKILLS-AUTOMATION"
]
},
{
"id": "recommendation-r36",
"priority": "P1",
"area": "Navigation",
"title": "Generate concept deep links, backlinks, and a graph view.",
"rationale": "The folder hierarchy supports progressive disclosure; backlinks and typed graph relationships reveal cross-cutting dependencies and evidence lineage.",
"references": [
"OKF-SKILLS",
"OKF-SKILLS-SAMPLE"
]
},
{
"id": "recommendation-r37",
"priority": "P2",
"area": "Semantic exchange",
"title": "Compile the bundle into an internal JSON-LD and PROV-O graph.",
"rationale": "Typed relationships make claims, evidence, derivations, challenges, decisions, and contributions traversable across future systems.",
"references": [
"W3C-PROV",
"SCHEMA-CREATIVEWORK"
]
},
{
"id": "recommendation-r38",
"priority": "P2",
"area": "Self-documentation",
"title": "Dogfood the research schema on the research system itself.",
"rationale": "The system should contain its own decisions, schemas, build artifacts, validation results, authorship, and update history as first-class concepts.",
"references": [
"OKF-SKILLS-SELF"
]
}
][
{
"name": "source",
"description": "External or internal artifact with bibliographic and quality metadata."
},
{
"name": "annotation",
"description": "Exact passage, figure, table cell, or resource segment linked to a claim."
},
{
"name": "claim",
"description": "A supportable statement with scope, confidence, and evidence relationships."
},
{
"name": "evidence",
"description": "Observation, result, standard, or quotation supporting or challenging a claim."
},
{
"name": "warrant",
"description": "Reasoning that connects evidence to a claim or recommendation."
},
{
"name": "assumption",
"description": "A condition currently treated as true but requiring monitoring."
},
{
"name": "concept",
"description": "A reusable definition or mental model."
},
{
"name": "interpretation",
"description": "What evidence means within a declared context."
},
{
"name": "recommendation",
"description": "A proposed action with outcomes, trade-offs, owner, and revisit conditions."
},
{
"name": "decision",
"description": "A selected option with context and consequences."
},
{
"name": "counterargument",
"description": "A credible objection or competing interpretation."
},
{
"name": "limitation",
"description": "A boundary on validity or applicability."
},
{
"name": "procedure",
"description": "An ordered action sequence with inputs, controls, and validation."
},
{
"name": "metric",
"description": "A measurement, baseline, threshold, and interpretation rule."
},
{
"name": "persona",
"description": "A bounded evidence-backed audience decision model."
},
{
"name": "situation",
"description": "A context in which an audience pursues a job or makes a decision."
},
{
"name": "impact-contract",
"description": "The intended measurable change in audience knowledge, judgment, or action."
},
{
"name": "challenge",
"description": "A structured adversarial finding against a research object."
},
{
"name": "output-manifest",
"description": "The rules assembling canonical objects for a specific channel and audience."
},
{
"name": "provenance-activity",
"description": "A human or model transformation with inputs, outputs, and review."
},
{
"name": "contribution-record",
"description": "A typed record of a person, organization, or software agent contributing to a research object, including role, activity, time, and review status."
},
{
"name": "generated-artifact",
"description": "A compiled output such as HTML, JSON, JSON-LD, presentation, or index with lineage back to canonical concepts."
},
{
"name": "schema-profile",
"description": "A versioned producer-defined content model extending OKF while preserving base-format compatibility."
},
{
"name": "validation-result",
"description": "A deterministic conformance, schema, link, citation, accessibility, or rendering check and its outcome."
}
][
{
"id": "contribution-human-direction-2026-07-17",
"agent": {
"type": "Person",
"name": "Jesse Graupmann"
},
"roles": [
"commissioned",
"directed",
"authored",
"reviewed",
"approved"
],
"target": "Enterprise Communication Research System 1.1",
"timestamp": "2026-07-17T00:00:00Z",
"accepted": true
},
{
"id": "contribution-ai-synthesis-2026-07-17",
"agent": {
"type": "prov:SoftwareAgent",
"name": "OpenAI ChatGPT",
"model": "GPT-5.6 Thinking"
},
"roles": [
"researched",
"synthesized",
"drafted",
"structured",
"implemented",
"validated"
],
"acted_on_behalf_of": "Jesse Graupmann",
"target": "Enterprise Communication Research System 1.1",
"timestamp": "2026-07-17T00:00:00Z",
"accepted": true,
"review_requirement": "Human approval remains required for publication and material epistemic changes."
},
{
"id": "contribution-human-direction-2026-07-18-v1-4",
"agent": {
"type": "Person",
"name": "Jesse Graupmann"
},
"roles": [
"directed",
"reviewed",
"approved experience goals"
],
"target": "Enterprise Communication Research System 1.4",
"timestamp": "2026-07-18T00:00:00Z",
"accepted": true
},
{
"id": "contribution-ai-experience-2026-07-18-v1-4",
"agent": {
"type": "prov:SoftwareAgent",
"name": "OpenAI ChatGPT",
"model": "GPT-5.6 Thinking"
},
"roles": [
"audited",
"designed",
"implemented",
"validated"
],
"acted_on_behalf_of": "Jesse Graupmann",
"target": "Enterprise Communication Research System 1.4",
"timestamp": "2026-07-18T00:00:00Z",
"accepted": true,
"review_requirement": "Human review remains required for publication and material epistemic changes."
}
]{
"title": "Enterprise Communication Research System",
"version": "1.4.0",
"status": "Living research synthesis · UX red-team corrected · reset-aware layout calibrated",
"generated": "2026-07-18",
"description": "A canonical research and communication system for technical documentation, academic research, architecture communication, personas, persuasion, business value, trade-offs, LLM retrieval, and future-agent stewardship.",
"method": "Synthesis of standards, university guidance, peer-reviewed and emerging research, and first-party enterprise practices.",
"evidence_policy": "Evidence classes, source intent, scope, limitations, and company-practice boundaries should remain explicit.",
"last_updated": "2026-07-18",
"canonical_format": "OKF-compatible Markdown/YAML bundle compiled into JSON, JSON-LD, and a self-contained SPA.",
"authorship_policy": "Human ownership and accountability remain explicit; AI systems are recorded as software-agent contributors with bounded roles, inputs, outputs, and review status."
}{
"experience_improvements": {
"version": "1.4.0",
"date": "2026-07-18",
"objective": "Convert the comprehensive long-form reference into a task-guided, persistent, shareable research workspace without hiding canonical content.",
"improvements": [
"Added five outcome-based entry paths for decision, research, documentation, persuasion, and stewardship work.",
"Added a persistent reading-progress indicator and compact back-to-top control.",
"Added sticky section controls for copyable deep links, section saving, and section-local detail expansion.",
"Added a local saved-research workspace with editable notes and JSON export.",
"Added save controls to global search results without adding controls to every content card.",
"Added explicit previous and next movement at the end of every major section.",
"Added return-to-reading-position behavior for reference jumps and changed internal navigation to preserve browser history.",
"Reduced hero vertical pressure while preserving orientation, scope, authorship, and core metrics."
],
"design_constraints": [
"No canonical research is hidden by a view preset.",
"Personal notes remain local and are not represented as research evidence.",
"All new actions remain keyboard accessible and use native dialog and button semantics.",
"The experience continues to function as a readable static document when JavaScript is unavailable."
],
"open_validation": [
"Measure whether guided paths reduce time to first relevant section for representative personas.",
"Test saved-note workflows with researchers who revisit the corpus over multiple sessions.",
"Validate sticky heading controls with VoiceOver and NVDA.",
"Measure whether previous and next links improve orientation without creating unnecessary sequential pressure."
]
}
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