Tencent's skill-evolution harness: it rewrites a whole skill folder — SKILL.md, scripts and references together — and lands every decision as a real Git issue, PR and wiki entry you can review.
Self-Learning
Summary
A meta-skill that spots the moment your agent has just earned a hard-won golden path and writes it down as a reusable skill, so the next session starts already knowing the route.
Features
- Recognises a reusable golden path without being asked and harvests it on the spot
- Captures the procedure and the dead ends, not a one-off answer
- Triage routes each lesson to a skill, to memory, or to the bin
- Three-part promotion rule: a passing check, a named failure pattern, a ruled-out dead end
- Writes to SKILL.md, .cursor/rules/learned/, or AGENTS.md depending on the tool
- Installs across 70+ agents through the community `skills` CLI
Install This Skill
Add this skill to your favorite AI agent in a few steps.
Skill Content
Usage Instructions
Learn how to use this skill with different AI agents.
Example Usage
That took four attempts to get right — remember this so I don't have to re-explain the migration workflow next session.
Description
Every session where you debug something difficult, work out a deployment sequence, or rediscover where the credentials live, that knowledge evaporates when the session ends. The next session starts from zero and re-learns it. Self-Learning is a meta-skill that fixes the leak: it does not do the work, it captures how the work got done.
The loop
- Recognise the moment. A task that only worked after several attempts, a non-obvious command, a project fact you did not know up front, an operational workflow likely to recur — or you simply saying "remember this".
- Capture it unprompted. It acts on the cue immediately, picks the scope and name itself, and tells you afterwards. What gets captured is the procedure, plus a note on what did not work — because skipping a known dead end next session is often worth more than the win.
- Reuse. Next session the entry loads automatically, matched by skill description or because the instructions file is always read.
Two rules that keep it from becoming noise
Triage decides granularity. A multi-step reusable procedure becomes a skill or rule. A single fact or one-line correction goes to lightweight memory. A genuine one-off is skipped. Your config does not fill up with one-liners.
The promotion rule decides confidence. A skill is authoritative — the next session trusts it without re-deriving it — so a session is promoted to a skill only when all three hold: a passing check (a test passed, a clean exit, a green build; "seemed to work" does not count), a named failure pattern it avoids or diagnoses, and at least one concrete dead end ruled out. Miss any one and it stays a tentative memory note. This is what keeps confident-but-unverified guesses out of your standing instructions.
Where it writes
The loop is identical across tools; only the destination differs. Claude Code, Codex and other Agent Skills clients get a new skills/<name>/SKILL.md loaded by description matching. Cursor gets .cursor/rules/learned/<name>.mdc loaded by rule description or globs. Zed, Aider, Gemini CLI and anything else that reads standing instructions get an AGENTS.md entry.
Install with npx skills add kulaxyz/self-learning-skills (auto-detects your agents, -g for global), as a Claude Code plugin, or by copying the folder into place. MIT licensed.
Related Skills
Take an OpenSearch search application from requirements to a running cluster — BM25, dense and sparse vectors, hybrid retrieval, agentic search and RAG, with relevance evaluation built in.
Auth0's official agent skill: a router that detects your framework and intent, then loads the right Auth0 guidance for login, MFA, Organizations, tenant audits, debugging or provider migration.
Redis' own guidance for FT.CREATE schema design, FT.SEARCH / FT.AGGREGATE / FT.HYBRID, HNSW vector similarity and RAG retrieval pipelines.