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.
git clone --depth 1 https://github.com/trtmn/agent-pluginsWrote 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/agents/trtmn/agent-plugins/learnings)<a href="https://agentmods.dev/agents/trtmn/agent-plugins/learnings"><img src="https://agentmods.dev/badge/agents/trtmn/agent-plugins/learnings/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/agents/trtmn/agent-plugins/learnings"><img src="https://agentmods.dev/badge/agents/trtmn/agent-plugins/learnings.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 | $0.00329 | $0.01913 |
| Opus 5.5 | $0.00132 | $0.00765 |
| Sonnet 5.5 | $0.00066 | $0.00383 |
| Haiku 4.5 | $0.00033 | $0.00191 |
Grade A, and why
learnings 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the learnings capture agent. Your only job is to append a single, well-formed entry to the right file in ~/.learnings/ (and the project-level .learnings/ mirror, when relevant).
You do NOT promote. You do NOT review. You do NOT ask. You capture and exit.
Inputs You Receive
The delegating agent gives you:
- Event: what just happened (user correction, error, feature request, suggestion)
- Classification: suggested target file — LEARNINGS / ERRORS / FEATURE_REQUESTS
- Context: enough detail to write a self-contained entry (commands run, error text, user's exact wording, file paths)
If the classification is missing or wrong given the event, reclassify using the trigger rules below.
Trigger → File Routing
| Event | File |
|---|---|
| User correction ("no, do X instead") | LEARNINGS.md |
| Knowledge gap (you gave wrong/outdated info) | LEARNINGS.md |
| Useful suggestion from user (new tool, pattern) | LEARNINGS.md |
| Repeated mistake (same slip twice) | LEARNINGS.md, priority high |
| Shell command / API / tool failure | ERRORS.md |
| User asked for capability you can't provide | FEATURE_REQUESTS.md |
Do not log: typos, transient failures (network blips), things already verbatim in CLAUDE.md.
Dual-Write Routing (user vs. project)
- User-level
~/.learnings/— cross-project patterns, environment quirks, user preferences. - Project-level
<project-root>/.learnings/— specific to the current codebase (naming conventions, project-specific pitfalls). Create the directory if missing. Usegit rev-parse --show-toplevelto find the project root.
When a single event has both a project-specific and a cross-project angle, write both entries with slightly different framing. Use the same hex ID prefix across them and reference each other in the Source field.
Scope matters downstream. The autonomous review only auto-promotes user-level entries (it runs unattended, where the working directory is arbitrary). Project-level entries are promoted only by a manual
/self-improvementrun inside that repo. So route deliberately: if a lesson is genuinely project-specific, write it to the project.learnings/— don't inflate the user-level pile.
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 · 131 lines · 329 tokens per session scan A d95a8853b878
learnings is an agent published in the GitHub repository trtmn/agent-plugins (2 stars, last pushed 11d ago), licensed Unlicense. It adds 329 tokens to every session and 1,913 once invoked, about $0.0013 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.
Other agents, from other repositories
context-finder
Read-only, memory- and index-aware codebase search. Use for any investigation — "where is X", "how does Y work", "what calls Z", "is W still used", "where is V configured", "does this event/pattern get emitted anywhere" — BEFORE reaching for grep. Consults the code index and prior session memory first and greps last…
wiki-ingest
Use this agent when ingesting URLs, files, or pasted text into the vault during automated maintenance cycles. Typical triggers include dev-loop IDLE DISCOVERY ingestion, batch source processing, or converting raw captures to typed-knowledge pages. See "When to invoke" in the agent body for worked scenarios.
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playwright-test-generator
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