ranbot-ai Continuous Learning from a Completed Session
Capture one reusable lesson whose trigger, fix and verification can be explained without preserving a private conversation. The output is a reviewed procedure, not an automatic memory update.
When to Use
Use after a resolved failure, a repeated project-specific correction, or an explicit request to save what was learned. Skip one-off typos, unresolved guesses, transient provider outages and lessons already covered by project documentation. A long session alone does not make a lesson reusable.
Inputs and prerequisites
- An authorized session summary or transcript, affected code or configuration, and the command or observation that confirmed the fix.
- The scope of the lesson: this repository, this tool version, or a more general procedure.
- An existing authorized documentation destination. If saving was not requested, return a draft in the conversation; do not update user memory, install skills, or modify agent configuration automatically.
- The optional session-length helper requires Bash and Python 3. Its only setting is
min_session_lengthin config.json.
Procedure
- Identify the failed assumption and final observed behavior. Keep unsuccessful hypotheses separate from the verified cause.
- Check the current source and test result. A remembered fix that was never exercised stays an open hypothesis.
- State a narrow trigger and prerequisites. Include the runtime or tool version when the fix depends on it.
- Write the smallest sequence that reproduces the diagnosis and verifies the repair. Include an expected result and a counterexample where the procedure should not be used.
- Remove secrets, user names, absolute personal paths, private messages and unrelated repository details. Prefer a minimal synthetic example to copying a transcript.
- Compare with existing instructions. Amend an existing project note when authorized instead of creating another overlapping skill. Preserve provenance and distinguish the original observation from later generalization.
- Present the draft and its evidence. Save only within the scope already authorized by the user, then read back the saved result.
Worked example
Illustrative input: a React test observed an old success message after the input changed, before new asynchronous validation completed. The fix associates each result with the exact current input; the regression changes the input and asserts that stale success disappears immediately.
Trigger: asynchronous validation results can outlive the input they describe.
Prerequisites: the component stores input and an async validation result.
Procedure: bind the result to its input; display pending state until that binding
matches; ignore results from superseded requests.
Verify: change a valid input to an invalid one while validation is pending.
Expected: no previous success is displayed; the final error belongs to the new input.
Limit: this does not establish the semantic correctness of the validation itself.
Use actual project paths and test output when recording a real lesson. This is an illustrative pattern, not a claim that a particular user's test passed.
Optional session-length reminder
bash skills/cc-skill-continuous-learning/evaluate-session.sh /absolute/path/to/session.jsonl
The helper counts JSONL objects whose top-level type equals user. It prints a count and review reminder to stderr after the configured threshold. It does not extract patterns, invoke a model, create directories, or save anything. CLAUDE_TRANSCRIPT_PATH is an optional caller-supplied fallback; no host is assumed to populate it. Automatic hook integration is not configured by this skill.
Limitations
- Transcript formats differ across clients. Other message schemas need an explicit adapter; zero messages does not prove that no useful work occurred.
- The helper rejects links, non-regular files, invalid JSONL, files above 16 MiB and lines above 1 MiB. It never prints transcript contents.
- A successful length check is not a semantic review, privacy review or authorization to persist a lesson.
- Recheck version-specific lessons before reuse. Do not promote a project workaround into a universal instruction without additional evidence.