self-learning
Rosetta MUST skill. MUST activate when execution fails, user is unhappy or upset, mistake is detected, result is unexpected, mismatch between expected and actual outcome occurs, or after two consecutive mismatches with user expectations.
Best use case
self-learning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Rosetta MUST skill. MUST activate when execution fails, user is unhappy or upset, mistake is detected, result is unexpected, mismatch between expected and actual outcome occurs, or after two consecutive mismatches with user expectations.
Teams using self-learning should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/self-learning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How self-learning Compares
| Feature / Agent | self-learning | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Rosetta MUST skill. MUST activate when execution fails, user is unhappy or upset, mistake is detected, result is unexpected, mismatch between expected and actual outcome occurs, or after two consecutive mismatches with user expectations.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
<self_learning> <process> On failure or mismatch: 1. STOP all changes immediately. 2. Identify root cause — not symptoms. 3. Ask 1-3 clarifying questions if ambiguous. 4. State understanding and conflicts in brief bullets. 5. Wait for explicit user confirmation. Memory: 6. Consult AGENT MEMORY.md during planning. 7. Init if missing; prefer agent memory over task memory. 8. Convert root causes into GENERALIZED, REUSABLE preventive rules — not incident-specific notes. 9. Store in AGENT MEMORY.md concisely and organized. 10. Record what worked and failed logically, architecturally, and technically. </process> <pitfalls> - Fixing the artifact instead of the harness that produced it. - Storing incident notes instead of generalizable rules. </pitfalls> </self_learning>
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