Borrowing it
Nothing to install: this file belongs to bb1nfosec/skim. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bb1nfosec/skim/main/.claude/commands/distill-analyze.mdgit clone --depth 1 https://github.com/bb1nfosec/skimWrote 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/commands/bb1nfosec/skim/distill-analyze)<a href="https://agentmods.dev/commands/bb1nfosec/skim/distill-analyze"><img src="https://agentmods.dev/badge/commands/bb1nfosec/skim/distill-analyze/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/commands/bb1nfosec/skim/distill-analyze"><img src="https://agentmods.dev/badge/commands/bb1nfosec/skim/distill-analyze.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 | $0.00000 | $0.00064 |
| Opus 5 | $0.00000 | $0.00032 |
| Sonnet 5 | $0.00000 | $0.00013 |
| Haiku 4.5 | $0.00000 | $0.00006 |
Grade A, and why
distill-analyze 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 9d 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.
What it actually says
Analyze the current project for context waste patterns (lock files, build artifacts, minified assets, etc).
python3 -m core.context_analyzer --path .
List every HIGH and MEDIUM severity pattern found, the tokens wasted, and the exact .llmignore rule to add for each.
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.
- 9d ago First seen · 8 lines · 0 tokens per session scan A 9260af70a3fc
distill-analyze is a command published in the GitHub repository bb1nfosec/skim (5 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 64 tokens. 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 2026-08-31.
Other commands, from other repositories
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export-closedloop-learnings
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pull-learnings
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remember
Store, retrieve, search, and manage persistent memory across sessions.
capture
Model-driven lesson capture — read recent work in a role, distill genuine insights, append tagged bullets to pending.md. The real capture path; complements the Stop-hook's cheap regex fast-lane.
role-promote
Autonomously reconcile a role's memory — merge pending + recent learnings into ROLE.md, dedupe and declutter. The auto-promotion step fired when a role is stale (>7 days) with pending activity.