Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/cogni-work/insight-wave/knowledge-verifynpx skills add cogni-work/insight-wave --skill knowledge-verifygit clone --depth 1 https://github.com/cogni-work/insight-waveWrote 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.
[](https://agentmods.dev/skills/cogni-work/insight-wave/knowledge-verify)<a href="https://agentmods.dev/skills/cogni-work/insight-wave/knowledge-verify"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/knowledge-verify.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00155 | $0.10439 |
| Opus 5 | $0.00077 | $0.05220 |
| Sonnet 5 | $0.00031 | $0.02088 |
| Haiku 4.5 | $0.00015 | $0.01044 |
Grade A, and why
knowledge-verify 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 521 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Verify
Phase 6 of the inverted pipeline. Reads <project>/output/draft-vN.md + <project>/.metadata/citation-manifest.json, dispatches wiki-verifier once per round to score every citation against each cited page's pre_extracted_claims:, and loops with revisor on unsupported deviations — capped at 2 revisor iterations per references/inverted-pipeline.md Phase 6.
Verifier verdicts: verbatim / paraphrase (evidence-aligned) and synthesis (informational, for claim_id: null citations to synthesis pages) go to verified[]. Only unsupported goes to deviations[] — that is the revisor's trigger. The loop terminates either when deviations[].verdict == "unsupported" is empty OR when revision_round == 2 is reached, whichever fires first.
This is the zero-network claim-alignment gate. The wiki has every source body verbatim under wiki/sources/<slug>.md with pre_extracted_claims: in frontmatter (the ingest phase wrote them at ingest time). The verifier does string-match scoring against those claims — no WebFetch, no re-extraction, no claims.json store.
verify-vN.json shape (written per round by wiki-verifier):
{
"schema_version": "0.1.1",
"draft_version": 1,
"revision_round": 0,
"verified": [
{"id": "cit-001", "draft_position": "02:03", "wiki_slug": "eu-ai-act-article-6", "claim_id": "clm-001", "verdict": "paraphrase", "grounded": true}
],
"deviations": [
{"id": "cit-023", "draft_position": "03:07", "wiki_slug": "bitkom-gpai-position", "claim_id": "clm-004", "verdict": "unsupported", "grounded": false, "reason": "claim_text_misaligned", "note": "..."}
],
"counts": {"verbatim": 4, "paraphrase": 28, "synthesis": 2, "unsupported": 3, "total": 37},
"grounding_metrics": {"grounded": 28, "ungrounded": 6, "unscored": 3, "grounding_rate": 0.824}
}
Schema 0.1.1 is additive over 0.1.0: each verdict entry carries a per-citation grounded signal (true/false/null), and verify-store.py merge sums them into the headline grounding_metrics block (grounding_rate = grounded / (grounded + ungrounded), or null when nothing is scorable). A 0.1.0 reader simply ignores both — the draft↔excerpt grounding rate is pure observability, never a verify gate.
Read ${CLAUDE_PLUGIN_ROOT}/references/inverted-pipeline.md §"Phase 6 — knowledge-verify" and references/claim-at-ingest.md once to anchor on the contract.
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.
- 3d ago First seen · 521 lines · 155 tokens per session scan A cedd6a3eab26
knowledge-verify is a skill published in the GitHub repository cogni-work/insight-wave (12 stars, last pushed 3d ago), licensed Apache-2.0. It adds 155 tokens to every session and 10,439 once invoked, about $0.0008 per session on Opus 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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.
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…