cc-skill-continuous-learning
Has your agent capture lessons learned from corrections. It turns debugging sessions into reusable procedures. It's useful for improving agent performance based on feedback.
What it does
This tool helps turn lessons learned from debugging sessions into clear, reusable procedures. It documents how to solve a specific problem based on what was fixed.
When to use it
- ✓After fixing a recurring error.
- ✓When a user explicitly asks to save a learned lesson.
- ✓After resolving a project failure.
Example of what to ask your AI
“Save the lesson I learned about configuring the web server so it doesn't crash under heavy user load.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool identifies the exact problem, the solution found, and how to verify the fix. It creates a simple step-by-step guide without personal information for others to use.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill cc-skill-continuous-learningOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/cc-skill-continuous-learning/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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.
FAQ
Does the tool save my private conversations?
No, it focuses only on the learned lesson and removes personal or irrelevant information.
Does it create new 'skills' automatically?
No, it creates a draft lesson or updates existing documentation if authorized. It does not automatically modify configurations or memories.
What is a 'reusable procedure'?
It's a set of simple, clear instructions describing a problem and its solution, so it can be used again in the future.
Details
Original description by the author
Turn a completed debugging session or repeated user correction into a small, evidence-backed procedure. Use for explicit requests to capture reusable lessons; does not automatically extract or save memories.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2500
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
00-andruia-consultant
Helps your agent start or improve AI projects. It analyzes code, suggests tools, and creates action plans in Spanish.
sickn33/agentic-awesome-skills
007
Lets your agent perform security audits and threat modeling. It's useful for protecting projects against attacks and vulnerabilities.
sickn33/agentic-awesome-skills
10-andruia-skill-smith
Helps your agent create or improve AI tools. It defines how tools work, writes documentation, and validates the structure.
sickn33/agentic-awesome-skills
20-andruia-niche-intelligence
Lets your agent create a summary about a specific industry. It's useful for understanding the context, rules, usage patterns, and risks of a project, using reliable sources.
sickn33/agentic-awesome-skills
Similar skills
benchmark
Helps your agent measure code speed and performance. It's useful for ensuring things are fast and finding slowness issues.
affaan-m/ECC
content-hash-cache-pattern
Lets your agent use content hashes to cache file processing results. This avoids repeated processing and auto-invalidates the cache when content changes. Useful for speeding up slow tasks.
affaan-m/ECC
parallel-execution-optimizer
Makes your agent speed up complex tasks by breaking them into smaller parts and running them in parallel. It ensures everything is verified at the end.
affaan-m/ECC