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/grinv/mal-mcp/self-learningnpx skills add Grinv/mal-mcp --skill self-learninggit clone --depth 1 https://github.com/Grinv/mal-mcpWhat 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 | $0.00041 | $0.00239 |
| Opus 5 | $0.00020 | $0.00120 |
| Sonnet 5 | $0.00008 | $0.00048 |
| Haiku 4.5 | $0.00004 | $0.00024 |
Grade A, and why
self-learning 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 2d 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.
This is a copy
100% identical to self-learning — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Self-learning — close the gap in the skill that missed it
For each confirmed finding from an audit/check pass:
- Identify which skill's checklist should have caught this — don't stop at fixing the code.
- If an existing bullet already covers this general class of check, sharpen/specialize it with the missed detail — don't add a new bullet next to it.
- Only add a brand-new bullet if nothing existing is even adjacent.
- Keep additions to 1-2 lines. No walkthroughs, no multi-sentence examples — these files compound over many passes; a short parenthetical example is fine, a paragraph is not.
- If a skill file is drifting toward bloat (bullets turning into short essays), that's a signal to prune/merge, not to keep appending — periodically re-read the whole file and tighten instead of only adding.
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.
- 2d ago First seen · 22 lines · 41 tokens per session scan A cee11dd5c939
self-learning is a skill published in the GitHub repository Grinv/mal-mcp (2 stars, last pushed 9d ago), licensed MIT. It adds 41 tokens to every session and 239 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to self-learning, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
live-audit
Audit anilist-mcp-server — build/test/lint gate, live MCP tool edge-case sweep (input validation, not-found paths, mutations with capture/revert), source-level code review, and docs/metadata consistency. Use when asked to test/audit the published or just-fixed anilist-mcp-server package, hunt for bugs/edge cases, or…
prompt-check
Live-test every MCP Prompt in src/prompts.ts through the real MCP protocol (not a static read) across every argument combination. Use when a prompt is added or its argument-handling logic changes, or as part of a live-audit pass.
tool-description-check
Self-check a new or edited MCP tool description/field .describe() text before committing — verify every behavioral claim against live testing, check for contradictions with sibling tools, and score against Glama's Tool Definition Quality Score (TDQS) rubric. Use whenever a tool description or schema field description…
release
Cut a release of anilist-mcp-server — draft CHANGELOG entries, check docs/metadata consistency, then bump/tag/push. Use when asked to release, cut a version, or publish a new version of this package.
fixture-accuracy-check
Make sure a mocked-fetch test fixture mirrors AniList's real GraphQL response shape, not just whatever fields make the current code pass. Use before writing or changing a fixture in src/tests/.test.ts.
docs-consistency-check
Check README/manifest.json/server.json/CHANGELOG.md/AGENTS.md and docs/.md for drift against the actual registered tools and source. Use after adding, renaming, or removing a tool, or as part of a live-audit pass.