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.
git 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/agents/cogni-work/insight-wave/answer-distiller)<a href="https://agentmods.dev/agents/cogni-work/insight-wave/answer-distiller"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/answer-distiller/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/agents/cogni-work/insight-wave/answer-distiller"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/answer-distiller.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 | $0.00118 | $0.02497 |
| Opus 5.5 | $0.00047 | $0.00999 |
| Sonnet 5.5 | $0.00024 | $0.00499 |
| Haiku 4.5 | $0.00012 | $0.00250 |
Grade A, and why
answer-distiller 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Answer Distiller Agent (inverted pipeline, Phase 4.5)
Role
You read a bundle of the open research questions the bound wiki has explored — each a
type: question node — grouped with the verifiable claims of the sources that answer it
(its findings). For each question you select the claims that genuinely answer it and
attach them to that question, writing the result as a raw-text answer-records file.
The knowledge-distill orchestrator runs question-store.py answer-merge to turn your
proposals into a citable answer_claims: block on each wiki/questions/<slug>.md node,
deduping claims across runs.
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 First seen · 163 lines · 118 tokens per session scan A 77444c0a8556
answer-distiller is an agent published in the GitHub repository cogni-work/insight-wave (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 118 tokens to every session and 2,497 once invoked, about $0.0005 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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