knowledge-compose

knowledge-compose is a skill for Claude Code from cogni-work/insight-wave. It costs 205 tokens per session (13,996 once invoked), scanned A, original, Apache-2.0.

A pipeline step that turns an already collected research wiki into a structured report draft. A research wiki is a set of saved source pages and summaries; it can also use a manifest that maps sources to questions.

In plain words
What is it for?
Use it to compose reports from ingested web sources or from an existing wiki, check that evidence is cited, and save the resulting draft.
Why use it?
It assembles scattered research into one cited draft while supporting a wiki-only mode that does not browse the web again.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions CLAUDE.md.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the cogni-knowledge plugin — 23 skills, 16 agents shipped together

Good fit Use it to compose reports from ingested web sources or from an existing wiki, check that evidence is cited, and save the resulting draft.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add cogni-work/insight-wave
Claude Code
/plugin install cogni-knowledge

Made for: Claude Code.

Or install cogni-knowledge, the plugin that ships this one along with the rest of its 23 skills, 16 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for knowledge-compose

README.md
[![agentmods](https://agentmods.dev/badge/skills/cogni-work/insight-wave/knowledge-compose/github.svg)](https://agentmods.dev/skills/cogni-work/insight-wave/knowledge-compose)
Your own site
<a href="https://agentmods.dev/skills/cogni-work/insight-wave/knowledge-compose"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/knowledge-compose/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for knowledge-compose

Your own site · 80×15
<a href="https://agentmods.dev/skills/cogni-work/insight-wave/knowledge-compose"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/knowledge-compose.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 205 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 13,996 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00205 $0.13996
Opus 5.5 $0.00082 $0.05598
Sonnet 5.5 $0.00041 $0.02799
Haiku 4.5 $0.00020 $0.01400

Measured 1mo ago against content hash 2c6bba55ef56, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

knowledge-compose scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

cogni-knowledge/skills/knowledge-compose/SKILL.md · 470 lines

How it starts

The opening of the file, as written. The whole thing — 470 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Knowledge Compose

Phase 5 of the inverted pipeline. Reads the per-project plan.json + ingest-manifest.json + the populated wiki at <binding.wiki_path>/wiki/, dispatches wiki-composer once, and verifies the output files land on disk.

The default --source web path composes from this run's web-ingested sources (the ingest-manifest.json Phase 4 produced). --source wiki is the wiki-only rung — a full structured, verified report grounded only in the already-populated wiki (wiki/sources/*.md + wiki/syntheses/*.md + distilled pages) and the .cogni-knowledge/fetch-cache/, with no web crawl: it preserves the retired research-report --source wiki capability so that capability does not vanish when cogni-research is archived.

The wiki-only mode does not change the composer or the verify/finalize tail at all. Instead, Step 0 synthesizes an ingest-manifest.json from the bound wiki's wiki/sources/*.md pages — mapping each source to the current plan's sub-questions via the shared wiki-grounding discovery primitive (scripts/wiki-source-manifest.py) — so the dispatched wiki-composer reads a normal manifest and runs byte-for-byte identically whether that manifest came from a web crawl or from the wiki. The composer does not branch on source mode and needs no new input. (See --source in Parameters and the Step 0 pre-flight.)

The composer reads wiki/index.md + selected wiki/sources/*.md (lazily, claims under pre_extracted_claims:) + prior wiki/syntheses/*.md. It also reads two optional knowledge-distill surfaces, topic-matched and lazily: the distilled wiki/{concepts,entities}/*.md pages (claims under distilled_claims:, ids dcl-NNN) and the type: question nodes at wiki/questions/*.md — first-class pages each recording one research question the base has already explored, with ## Findings [[links]] to the sources that answered it (answers under an optional answer_claims: block, ids acl-NNN).

Both surfaces serve both as narrative framing and as citable cross-source evidence, under one shared rule: when ≥2 distinct sources converge (≥2 sources agreeing on a fact the distilled page captures; ≥2 distinct sources in a question node's backlinks[]), the composer cites the distilled page or question node directly via its dcl-NNN/acl-NNN claim id, so the convergence carries epistemic weight rather than a row of stacked source markers. Below that bar — a single-source answer, or a page/node carrying no claim block yet — the surface stays framing-only: the composer reads it for orientation but cites the backing source page (an inline citation to a claim-less node would score unsupported). The verifier scores each such citation against that claim's text. Both surfaces are optional and fail-soft: with neither present the composer composes from sources + syntheses alone.

The composer then writes:

  • <project>/output/draft-v{N}.md — the draft, with clickable numbered [N] inline citations (wikilinks confined to the reference list).
  • <project>/.metadata/citation-records-v{N}.txt — one raw-text record per citation (the composer writes this; it never hand-builds JSON). This skill then runs citation-store.py build to serialize and validate <project>/.metadata/citation-manifest.json (schema 0.1.1, one {id, draft_position, draft_sentence, wiki_slug, claim_id, url} entry per citation). Escaping is owned by json.dumps, never the LLM — a straight " in a draft_sentence would otherwise break a hand-built manifest's json.loads and kill the verify phase.

A writer-outline-v{N}.json is persisted by the composer's Phase 1 before any draft Write attempt — this is the outline-recovery contract. If the composer crashes between outlining and drafting, re-running this skill detects the leftover outline and re-dispatches the composer with RESUME_FROM_OUTLINE=true so only Phase 2 runs.

citation-manifest.json shape (consumed by the wiki-verifier):

Read the full file on GitHub · 470 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +1 lines 2c6bba55ef56
  2. 1mo ago Changed · -18 lines 7c831032c59d
  3. 1mo ago First seen · 487 lines · 205 tokens per session scan A fbcac0937ae6

Subscribe to this mod's changes

knowledge-compose is a skill published in the GitHub repository cogni-work/insight-wave (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 205 tokens to every session and 13,996 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…

vercel/next.js · 123 tokens