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 skills add josherau/claude-operating-core --skill extract-approachgit clone --depth 1 https://github.com/josherau/claude-operating-coreWrote 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/josherau/claude-operating-core/extract-approach)<a href="https://agentmods.dev/skills/josherau/claude-operating-core/extract-approach"><img src="https://agentmods.dev/badge/skills/josherau/claude-operating-core/extract-approach.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 5.1 | $0.00116 | $0.01159 |
| Opus 5 | $0.00058 | $0.00580 |
| Sonnet 5 | $0.00023 | $0.00232 |
| Haiku 4.5 | $0.00012 | $0.00116 |
Grade A, and why
extract-approach 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 8d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract Approach — the reasoning recorder
Every hard solve contains two products: the fix, and the approach that found the fix. The fix ships; the approach evaporates when the session ends. This skill saves the approach — the more valuable half — as a permanent note any future model reads before touching similar work.
Why this exists: frontier models get repriced and retired. An answer helps once; a written-down approach upgrades every answer after it, at any model tier. The recipe requires top-tier judgment to author but only ordinary intelligence to follow.
Setup (one-time)
Pick a learnings directory in your knowledge base (e.g. {KNOWLEDGE_BASE}/learnings/) and keep a ## Learnings index — one line per lesson — in a file your sessions always load (CLAUDE.md is the natural place). Everything below assumes those two locations exist; adjust paths to your setup.
Trigger test (all three must hold)
- It was actually hard. At least one of: multiple failed attempts before the solve; a non-obvious insight was the turning point; a judgment call between 2+ viable approaches; a quality bar or heuristic was invented on the spot.
- It will recur. The problem shape (not the exact problem) will plausibly show up again in the user's work.
- The approach is transferable. You can state a recipe someone else could follow without this session's context.
If any fail, don't write a note — noise kills the learnings system faster than gaps do. Routine bug fixes, config edits, and lookups never qualify.
Timing: capture immediately at solve time, while the dead ends are still in context — never batched to session end. Logging mistakes the moment they happen is the same discipline; batching loses the details that matter.
What to capture
Write ONE file to {KNOWLEDGE_BASE}/learnings/{kebab-case-slug}.md. The format is: one-line summary, then Problem shape, Why it mattered, How to apply:
{One-line summary: the transferable heuristic, stated so it stands alone in an index.}
**Problem shape:** {What class of problem this was — described abstractly enough that a future session pattern-matches it BEFORE knowing the solution. 1-2 sentences.}
**Why it mattered:** {What was at stake, what failed first and WHY each dead end failed — the dead ends are half the value; they prune a future model's search tree. Include the turning-point insight and what made it non-obvious.}
**How to apply:** {The recipe. Numbered steps a mid-tier model could follow cold: what to check first, what signal discriminates between causes, what order to try things, what to never do. If a quality bar was invented, state it as pass/fail criteria.}
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.
- 8d ago First seen · 66 lines · 116 tokens per session scan A 451397da5e98
extract-approach is a skill published in the GitHub repository josherau/claude-operating-core (3 stars, last pushed 28d ago), licensed MIT. It adds 116 tokens to every session and 1,159 once invoked, about $0.0006 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 2026-08-31.
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