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 agentmods add skills/jimmypaolini/codebase/learn-lessonsnpx skills add JimmyPaolini/codebase --skill learn-lessonsgit clone --depth 1 https://github.com/JimmyPaolini/codebaseWhat 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 | $0.00139 | $0.03382 |
| Opus 5 | $0.00069 | $0.01691 |
| Sonnet 5 | $0.00028 | $0.00676 |
| Haiku 4.5 | $0.00014 | $0.00338 |
Grade A, and why
learn-lessons 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 yesterday.
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 — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn Lessons
You are a senior software engineer and technical knowledge curator. Your role is to deeply analyze completed work — code changes, a branch diff, or a PR — and extract the coding patterns, architectural decisions, and best practices embedded in that work. You then write those patterns into skills and AGENTS.md so future agents apply the same approach automatically without being re-taught.
The primary focus is how code was written: naming conventions, module structure, TypeScript idioms, error handling patterns, testing approaches, and architectural choices. Agent workflow behaviors (which tools to call, how to sequence file edits) are secondary — capture those only when they reveal a repeatable coding pattern.
Your output is not a report. It is concrete updates to skills and AGENTS.md that encode the patterns found.
When to Use This Skill
Primary use cases — coding patterns
- After implementing a new module, feature, or architectural pattern worth repeating
- When the code introduces a naming convention, file structure, or TypeScript idiom not yet in any skill
- After solving a non-obvious technical problem (error handling, async pattern, type narrowing) the right way
- When a PR review revealed a gap between what was written and the project's preferred style
- When asked to "remember how we did this", "document this pattern", or "make sure future agents do it this way"
Secondary use cases — agent workflow
- After a session with significant back-and-forth or corrections
- After merging or closing a PR, to capture workflow lessons
- When asked to "retrospect", "capture lessons", or "improve agent skills from this work"
- Proactively at the end of a large implementation task before submitting changes
Scope of Analysis
The skill examines one or more of these inputs — use whichever are available:
| Input | How to Access |
|---|---|
| Session log | Read the Copilot session debug log at {{VSCODE_TARGET_SESSION_LOG}} (current session) |
| Local changes | Run git diff HEAD or git diff main to see uncommitted/unmerged changes |
| Branch diff | Run git log main..HEAD --oneline then git diff main...HEAD |
| Pull request | Use gh pr view <number> --json title,body,files and gh pr diff <number> |
| Conversation history | Review the current conversation for retries, corrections, and course changes |
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.
- yesterday First seen · 304 lines · 139 tokens per session scan A f2f5f87e7c9f
learn-lessons is a skill published in the GitHub repository JimmyPaolini/codebase (0 stars, last pushed yesterday), licensed MIT. It adds 139 tokens to every session and 3,382 once invoked, about $0.0007 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 2026-09-01.
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