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.zipInstalls 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.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
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 /contributionsalways forks a new branch — call it exactly once. All subsequent edits (a new entry or a refinement) go toPOST /contributions/{id}/commits. Re-POSTing/contributionsis 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/contributionsendpoint 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.
- 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.
- RECALL — both planes. (a) The merged plane:
/knowledge?mode=browsefiltered by domain, orcore__knowledge_search. (b) Your own open contribution branch:GET /contributions?state=openfor the id, thenGET /contributions/{id}/diffto read the entries already on it. Branch-local entries do not appear in/knowledge?domain=(it returns merged-mainonly) — 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. - REFINE. Found a related entry that's slightly off, stale, or bloated? Sharpen it in place via an
op: updateedit. Shorter + sharper + raises confidence. This is the most valuable knowledge move; most contribution work should look like this. - CITE. Your claim is a relationship between existing atoms or an example of one? Add a
citationedge —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 tomain(cross-plane) — this resolves correctly and the edge becomes live inmain'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: usecitationType: "tracks"to connect exactly one work item (task.*,bug.*,spike.*,story.*, orsubtask.*) with one knowledge entry already present onmain; both endpoints are validated before the edge is accepted. - 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. - 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 shape | Audience | Entry type | Sub-skill |
|---|---|---|---|
| Falsifiable prediction that resolves in a later session and shapes future agent action | agent | hypothesis / decision / outcome (atomic chain) | edo-loop |
| Visual artifact (diagram, scorecard, roadmap, status grid, design diff) for human review | human | html (sandboxed iframe) | dolt-human-visuals |
| Atomic factual claim with provenance, recallable by future agent search | agent | observation / finding / conclusion / rule / scorecard / skill / guide | direct (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… | Home | Why |
|---|---|---|
| As-built fact about how the system works right now — architecture, a contract, an invariant a future agent needs as ground truth | docs/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 architecture | knowledge 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 does | EDO 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.contentreferences the spec id; thedeciderow'ssource_refpoints at the PR that changes the spec'd behavior; theoutcomereads the deployed system atsha:<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
findingfor 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.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| contribute-knowledge-to-cogni (this skill) | 0 | 1mo | Caution | Advanced |
| qmd | 26 | 2mo | Review | Intermediate |
| deep-reading-analyst | 1 | 9mo | No flags | Beginner |
| knowledge-base | 1 | 6mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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