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 ralfyishere/rules-with-receipts --skill extract-approachgit clone --depth 1 https://github.com/ralfyishere/rules-with-receiptsWrote 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/ralfyishere/rules-with-receipts/extract-approach)<a href="https://agentmods.dev/skills/ralfyishere/rules-with-receipts/extract-approach"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/extract-approach/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/ralfyishere/rules-with-receipts/extract-approach"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/extract-approach.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 | $0.00112 | $0.01373 |
| Opus 5.5 | $0.00045 | $0.00549 |
| Sonnet 5.5 | $0.00022 | $0.00275 |
| Haiku 4.5 | $0.00011 | $0.00137 |
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 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract Approach
Purpose
Hard-won knowledge evaporates when the session ends. This skill converts a solved problem into a durable asset: a short note future sessions can read in 30 seconds and apply. It is the persistence half of learning — self-improvement-loop corrects course within a session; this skill writes the reusable residue across sessions.
When to use this skill
After (not during) solving something non-trivial, when at least one of these is true:
- The solution created a new pattern worth reusing (an approach, a decision rule, a fixture design).
- The work exposed a recurring failure mode (yours, the model's, or the system's).
- The outcome changes how future work should be done (a tool quirk discovered, a wrong assumption corrected, a workflow that repeated for the second time).
When NOT to use this skill
- Trivial or routine tasks — a note nobody will need is clutter, and clutter kills the index.
- Knowledge the repo already records (code comments, git history, existing docs, existing skills). Don't duplicate; link.
- Mid-task — finish and verify first. Notes about unverified solutions record guesses.
Operating procedure
- Check the bar. Does this meet one of the three criteria above? If not, stop — no note.
- Check for an existing note or skill covering the same ground (
ls .claude/learnings/, skim titles). If one exists, update it instead of creating a near-duplicate. - Write the note to
.claude/learnings/YYYY-MM-DD-short-title.md(kebab-case title, today's date) using_template.md:
# <Short title>
- **Problem:** what was broken/needed, one line.
- **Context:** where this applies (project, tool, situation), one line.
- **What worked:** the approach that solved it, 1-3 lines.
- **What failed / almost failed:** the dead ends or near-misses, 1-2 lines.
- **Decision rule:** "Next time X, do Y" — mechanical enough to follow cold.
- **Verification:** how to confirm the approach worked, one line.
- **Related skills:** [links to skills this touches]
- **Disposition:** learning-note | promote-to-skill | update-skill:<name> | update-snippet
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 · 85 lines · 0 tokens per session scan A 573f1febe1e8
Extract Approach is a skill published in the GitHub repository ralfyishere/rules-with-receipts (2 stars, last pushed 3mo ago), licensed MIT. It adds 112 tokens to every session and 1,373 once invoked, about $0.0004 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 2026-08-31.
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