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contribute-knowledge-to-cogni

Contributes verified, durable knowledge to a central Cogni node hub.

Install

mkdir -p .claude/skills/contribute-knowledge-to-cogni && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16769" && unzip -o skill.zip -d .claude/skills/contribute-knowledge-to-cogni && rm skill.zip

Installs to .claude/skills/contribute-knowledge-to-cogni

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Umbrella skill for contributing durable knowledge to a Cogni node hub. Triggers when an agent has — or is about to research — context worth compounding for future agents/humans, AND the knowledge is durable enough to survive the syntropy bar. Routes to the right sub-skill by content shape (falsifiable prediction → `edo-loop`; visual for humans → `dolt-human-visuals`; AI-readable text → direct contribution). Use whenever you'd otherwise drop a research finding into a chat log or PR description that should outlive the session. RARE by design — most agent context dies with the session; only what compounds earns an entry.
625 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Advanced

Key capabilities

  • contribute durable knowledge to a Cogni node hub
  • refine existing knowledge entries
  • cite relationships between atoms
  • write new atomic knowledge entries
  • route contributions by content shape
  • track work items with citations

How it works

The skill guides agents through a hierarchy of actions to contribute knowledge, prioritizing staying silent, recalling existing knowledge, refining, citing, and then writing new atomic entries, routing by content shape.

Inputs & outputs

You give it
research finding or durable context
You get back
new or refined knowledge entry in the Cogni node hub

When to use contribute-knowledge-to-cogni

  • Contribute research
  • Update knowledge base
  • Share findings

About this skill

contribute-knowledge-to-cogni — route any knowledge contribution

Knowledge entries are precious. The right question is rarely "what do I write?" — it's "should I write anything, or refine what already exists?"

ONE PRINCIPAL · ONE OPEN CONTRIBUTION · refine via /commits — never fork

This is the rule the rest of the skill hangs on. Get it wrong and you sprawl the inbox with N single-commit branches for one unit of work (the noob failure).

  • One principal. Reuse your saved API key. Do not register a fresh agent per write — each registration is a new principal, and the inbox fills with orphan one-commit contributions nobody can attribute to one author.
  • One open contribution at a time. A principal holds a single open contrib/* branch. Everything you contribute this session lands on it.
  • First write creates the branch; every write after that appends. POST /contributions always forks a new branch — call it exactly once. All subsequent edits (a new entry or a refinement) go to POST /contributions/{id}/commits. Re-POSTing /contributions is the fracturing bug: it does not compound, it spawns a second branch.
  • Start something genuinely unrelated? Close the current one first (POST /contributions/{id}/close), then create. Don't run two open branches.

Asymmetry to remember: the EDO endpoints (/api/v1/edo/*) auto-compound onto your one open contribution server-side. The raw /contributions endpoint does not — you compound it yourself by using /commits.

Action hierarchy (mirrors knowledge-syntropy-expert)

Walk top-to-bottom. Most agent work stops at step 1.

  1. STAY SILENT. Is this context: ephemeral (dies with session), routine work-item state, an in-PR finding, an obvious factual lookup, OR something an existing entry already says? → write nothing. Knowledge entries are precious; sprawl is the failure mode. ≥80% of contributable-feeling moments belong here.
  2. RECALL — both planes. (a) The merged plane: /knowledge?mode=browse filtered by domain, or core__knowledge_search. (b) Your own open contribution branch: GET /contributions?state=open for the id, then GET /contributions/{id}/diff to read the entries already on it. Branch-local entries do not appear in /knowledge?domain= (it returns merged-main only) — so an agent who recalls the merged plane alone will re-discover, re-author, or outright deny knowledge it wrote minutes ago on its own branch. Read the branch before you write or before you answer "does X exist / is it linked." Cite the siblings you find; append rather than fork.
  3. REFINE. Found a related entry that's slightly off, stale, or bloated? Sharpen it in place via an op: update edit. Shorter + sharper + raises confidence. This is the most valuable knowledge move; most contribution work should look like this.
  4. CITE. Your claim is a relationship between existing atoms or an example of one? Add a citation edge — supports, contradicts, extends, supersedes. Or write a sibling atom that cites the parent. Never inline "companion to X" prose. Cite across planes freely: an entry on your open branch may cite one already merged to main (cross-plane) — this resolves correctly and the edge becomes live in main's DAG when your contribution merges. (Before bug.5024 this silently 500'd; it now works, so don't avoid citing merged atoms from a long-lived compounding branch.) Work-item links are also citations: use citationType: "tracks" to connect exactly one work item (task.*, bug.*, spike.*, story.*, or subtask.*) with one knowledge entry already present on main; both endpoints are validated before the edge is accepted.
  5. WRITE ATOMIC. No existing atom fits AND the claim earns its keep → file new entry. See routing below for which entry type / sub-skill. Nearly always cite at least one existing entry in the same edit (supports/extends/contradicts/supersedes) — a new atom should compound onto the graph, not land as an island. RECALL almost always surfaces a parent or sibling to link; a brand-new entry with zero edges is the silent failure mode that keeps the hub a flat document store instead of a compounding DAG.
  6. EXTEND. Anti-pattern. Don't bloat an existing atom to cover more cases — write a sibling, cite the parent.

Routing by content shape

After RECALL confirms a new write is genuinely needed, pick exactly one path:

Content shapeAudienceEntry typeSub-skill
Falsifiable prediction that resolves in a later session and shapes future agent actionagenthypothesis / decision / outcome (atomic chain)edo-loop
Visual artifact (diagram, scorecard, roadmap, status grid, design diff) for human reviewhumanhtml (sandboxed iframe)dolt-human-visuals
Atomic factual claim with provenance, recallable by future agent searchagentobservation / finding / conclusion / rule / scorecard / skill / guidedirect (this skill)

One entry, one shape. Don't mix — a "scorecard with embedded prediction" is two entries, one cites the other. But both still land as edits on your one open contribution — separate shape ≠ separate branch.

EDO vs knowledge entry vs spec — what truth goes where

Three durable homes. Pick by the shape of the truth, not by which is easiest.

You have…HomeWhy
As-built fact about how the system works right now — architecture, a contract, an invariant a future agent needs as ground truthdocs/spec/* in the repo (refine an existing spec; ship it in the PR)SPECS_ARE_AS_BUILT. Versioned with the code, reviewed in the PR that makes it true. Not in the hub.
Atomic learning with provenance — "we found X", a rule, a scorecard — not a prediction, not architectureknowledge entry (this skill)Recallable by agent search, confidence-rated, compounds in the Dolt hub.
Falsifiable prediction that resolves in a later session, is contestable, and changes what the next agent doesEDO chain (edo-loop)Time-bound belief → action → outcome; confidence recomputes when the outcome lands.

EDO linked to a spec — when your prediction is about a spec'd subsystem:

  • The spec stays the as-built description (what the system does).
  • The EDO carries the time-bound belief (whether a change moves a metric).
  • Wire them, don't merge them: hypothesize.content references the spec id; the decide row's source_ref points at the PR that changes the spec'd behavior; the outcome reads the deployed system at sha:<deployed>.
  • Don't fold the prediction into the spec (specs aren't predictions). Don't restate the spec inside the EDO (cite it).

Tie-breakers:

  • Tempted to write a docs/spec/knowledge-* doc for a one-off learning? Almost always wrong — refine a hub entry. Specs are for durable as-built contracts, not findings.
  • Tempted to file a finding for a prediction because EDO feels heavy? If it's falsifiable + session-separated + contestable, it's an EDO or it's silence — not a finding.

Text entry types render their content as GFM markdown in the human UI (structure it — see "Format the content field"). html is reserved for visual artifacts markdown can't express.

Picking the right node

Cogni nodes own niche hubs. Pick by primary subject:

  • operator (https://cognidao.org / https://test.cognidao.org) — cross-cutting infrastructure, knowledge platform itself, syntropy, deploy + flight, work-item lifecycle, governance. Default when in doubt.
  • poly (poly.cognidao.org) — Polymarket CLOB, copy-trade mirror, wallet provisioning, market-data analytics.
  • resy (resy.cognidao.org) — reservation knowledge.
  • Other nodes — see each node's charter.

If a claim is genuinely cross-node (e.g. "Doltgres WITH RECURSIVE works at 1k rows"), file once on operator and cite from per-node hubs as they need it. Don't duplicate.

Picking the right domain

domain is a registered FK on every entry (DOMAIN_FK_ENFORCED_AT_WRITE). Pick from existing — register a new one ONLY if no existing domain fits and the new one will accumulate ≥5 entries.

Common operator-node domains (seeded): meta, infrastructure, prediction-market, governance, reservations.

If unsure → use meta (knowledge about the knowledge system itself) or the closest existing match. Register new via `POST /api/v1/knowledge/domain


Content truncated.

When not to use it

  • when context is ephemeral or routine work-item state
  • when an existing entry already covers the knowledge
  • when the contribution would create a new branch instead of appending

Limitations

  • requires one principal and one open contribution at a time
  • does not allow re-POSTing /contributions to avoid fracturing
  • new atoms should nearly always cite at least one existing entry

How it compares

This skill enforces a structured, hierarchical approach to knowledge contribution, emphasizing refinement and citation over creating new entries, which prevents knowledge sprawl compared to ad-hoc contributions.

Compared to similar skills

contribute-knowledge-to-cogni side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
contribute-knowledge-to-cogni (this skill)01moCautionAdvanced
qmd262moReviewIntermediate
deep-reading-analyst19moNo flagsBeginner
knowledge-base16moReviewBeginner

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