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.
/plugin marketplace add cogni-work/insight-wave/plugin install cogni-knowledgeWrote 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-ingest-source)<a href="https://agentmods.dev/skills/cogni-work/insight-wave/knowledge-ingest-source"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/knowledge-ingest-source/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.
<a href="https://agentmods.dev/skills/cogni-work/insight-wave/knowledge-ingest-source"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/knowledge-ingest-source.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00174 | $0.06464 |
| Opus 5.5 | $0.00070 | $0.02586 |
| Sonnet 5.5 | $0.00035 | $0.01293 |
| Haiku 4.5 | $0.00017 | $0.00646 |
Grade A, and why
knowledge-ingest-source 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.
How it starts
The opening of the file, as written. The whole thing — 404 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Ingest — Single Source
The standalone single-source surface: deposit ONE source directly into the
bound wiki, with no research run (no knowledge-plan → knowledge-curate
→ knowledge-fetch scaffold, no fetch-manifest.json). A user drops one
input — a URL (web page or PDF), a local file (.docx/.html/.txt),
pasted text, a local PDF, or a local interview note — into their
bound base and it lands as a type: source page (or a type: interview page in
wiki/interviews/ for an interview note) carrying pre_extracted_claims:,
indexed and backlinked exactly like a research-ingested source. A URL stores
via fetch_method: webfetch; every local input stores honestly via
fetch_method: direct (the additive non-web method in fetch-cache.py's
VALID_FETCH_METHODS) — see Step 1 for why a local source is never stored as a
webfetch lie.
The mechanism reuses the research write path byte-for-byte: it populates
the shared fetch-cache, then dispatches the source-ingester agent (which reads
the cached body via fetch-cache.py fetch, dispatches claim-extractor, and
writes the page atomically with its Phase-3 pre-write integrity assertion),
then runs the same backlink_audit.py + wiki_index_update.py +
config_bump.py post-write lockstep knowledge-ingest Step 4 runs. The
source-ingester takes an additive PAGE_TYPE parameter (default source, so
a URL / file deposit is byte-identical to the research path; interview for a
local interview note → wiki/interviews/). The only single-source-specific
work is before the ingester: acquire the source body into the cache —
fetching a URL, or normalizing a local file (.docx/.html/.txt via the
vendored convert_to_md.py) / reading a local PDF (Read tool) / capturing
pasted text, all stored via fetch_method: direct — and dedup the source
against existing wiki pages so a collision routes to diff-before-write instead
of a blind overwrite.
Read ${CLAUDE_PLUGIN_ROOT}/references/inverted-pipeline.md §"Phase 4 —
knowledge-ingest" and references/claim-at-ingest.md once if you have not —
the claim-shape and write contracts are shared.
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.
- 1mo ago Changed · +1 lines a78bbb7902e8
- 1mo ago First seen · 403 lines · 174 tokens per session scan A ec8f1480b0cd
knowledge-ingest-source is a skill published in the GitHub repository cogni-work/insight-wave (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 174 tokens to every session and 6,464 once invoked, about $0.0007 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.
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