learn
Skill1 starsChanged 4 days agoMITView on GitHub
Use when a session or project wraps up, or the user says "learn", "what did we learn", "retro", "self-improve", "remember this for next time", or complains that the agent looped, retried, or burned too many tokens or too much time.
194 other projects use a version of this.
Adopted by
Most recently taken up by
- ubeann/skillstheir own version, first seen Nov 15, 2026
- markmhendrickson/neotomatheir own version, first seen Nov 15, 2026
- todoist/todoist-clitheir own version, first seen Nov 15, 2026
- Doist/todoist-aitheir own version, first seen Nov 9, 2026
- Doist/todoist-clitheir own version, first seen Nov 8, 2026
- HughYau/AcademicForgetheir own version, first seen Nov 8, 2026
Used with
Projects with it also use add-anydoc, add-clidash and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
TomGranotSkilladd-anydoc
Add local office-document-to-Markdown conversion to NanoClaw agent containers with the pinned Firecrawl AnyDoc CLI. Use when agents need to read attached Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, or text-based PDF files without uploading them to a hosted parser.
Also in 5 projects that use this
TomGranotSkilladd-clidash
Add clidash — a zero-dependency, read-only web dashboard that derives its tabs and tables at runtime from any CLI that lists resources as JSON. Ships pre-wired for NanoClaw's ncl CLI (agent groups, sessions, channels, users, roles), plus message-activity charts, a log tail, and a read-only file viewer for group skills/CLAUDE.md/profiles.
Also in 5 projects that use this
seek888Skillopen-gstack-browser
Launch GStack Browser — AI-controlled Chromium with the sidebar extension baked in. Opens a visible browser window where you can watch every action in real time. The sidebar shows a live activity feed and chat. Anti-bot stealth built in. Use when asked to "open gstack browser", "launch browser", "connect chrome", "open chrome", "real browser", "launch chrome", "side panel", or "control my browser". Voice triggers (speech-to-text aliases): "show me the browser".
Also in 11 projects that use this
Saqlain-811Skillios-sync
Regenerate the iOS debug bridge against the latest upstream gstack templates. (gstack)
Also in 6 projects that use this
MicrosoftSkilladd-codex
Use Codex (CLI + AppServer) as the full agent provider — planning, tool orchestration, native compaction, MCP tools, session resume — in place of the Claude Agent SDK. ChatGPT subscription or OPENAI_API_KEY. Per-group via agent_provider. Distinct from using OpenAI as an MCP tool (where Claude remains the planner).
Also in 6 projects that use this
MicrosoftSkilladd-deltachat
Add DeltaChat channel integration via @deltachat/stdio-rpc-server. Native adapter — no Chat SDK bridge. Email-based messaging with end-to-end encryption.
Also in 6 projects that use this
- +5 more from TomGranot
- +1 more from microsoft
Other versions194 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 194
- garrytan/gstack★ 141ksame name
- rohitg00/ai-engineering-from-scratch★ 78ksame name
- gavrielc/nanoclaw★ 31ksame name
- nanocoai/nanoclaw★ 31ksame name
- qwibitai/nanoclaw★ 31ksame name
- tigerless-labs/autoharness★ 11ksame name
- tw93/claude-health★ 7.5ksame name
- tw93/Waza★ 7.5ksame name
- nykooi1/vibe-wise★ 3.5ksame name
- HughYau/AcademicForge★ 2.6ksame name
184 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 0 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 169 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 12
Use the current conversation.
- Very specific: line 13
Read the homes once: project CLAUDE.md (and nested ones), the memory index MEMORY.md, and the description line of each skill in this plugin.
“Common” is measured: kept by at least 6 in 10 of the 169 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Learn
Turn this session into a few durable rules, each in the one place that will apply it next time. Keep this pass cheap.
1. Gather evidence
- Use the current conversation. If it starts from a summary, read only the compacted part of the session transcript (newest
.jsonlunder~/.claude/projects/<project>/) by grepping user messages. - Read the homes once: project
CLAUDE.md(and nested ones), the memory indexMEMORY.md, and thedescriptionline of each skill in this plugin. Open a skill body only when a lesson points at it.
2. Scan, highest value first
- Corrections: the user said no, stop, actually, or fixed your work.
- Waste: the same command, fix, file read, or subagent run three or more times; a review loop that ran rounds past the bar; a big read that a grep would have answered. Count it and fix the cause (missing info, vague stop rule, no check).
- Repeated asks: guidance the user gave twice.
- Confirmed approaches: something that worked and will be needed again.
- Failures: what broke, and what worked instead.
- Preferences: style, naming, tool choices the user stated.
3. Filter
Keep a lesson only if it is stable, non-obvious, actionable as one rule, and not already covered. It is covered when an existing rule, followed, would have prevented the problem. A rule that exists but was ignored needs to be sharper or moved, not copied. Drop one-offs, task state, and bugs fixed this session. If nothing survives, say so and stop.
4. Route each lesson to one home
| Home | Gets |
| Automated check (lint, hook, test) | Mechanical rules a tool can decide from the code. Write no prose rule for these. |
| A skill in this plugin | Anything that corrects or sharpens how a skill runs: a skipped step, a bad default, a missing stop rule. |
Project CLAUDE.md | Project decisions and pointers (commands, paths, conventions). Keep it short; it loads every session. |
| User memory | Personal preferences and gotchas with no skill home. |
Ties: skill beats CLAUDE.md and memory. Decisions go to CLAUDE.md, discovered quirks to memory. A multi-step procedure goes to a skill, with a one-line pointer in CLAUDE.md.
5. Show the plan, then write
- Print a table: lesson, exact file, append or edit in place, and the one-line rule text. Wait for approval. Write nothing before it.
- Apply only approved rows. Edit in place over appending, match the file's tone, state the rule, not the incident. Keep personal details out of files that are shared or published.
- Report what changed in one line per file.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

