extract-skills-from-Codex-sessions
Automatically extract reusable skills from Codex session transcripts using LLM analysis and wire them into a Stop hook
Best use case
extract-skills-from-Codex-sessions is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Automatically extract reusable skills from Codex session transcripts using LLM analysis and wire them into a Stop hook
Teams using extract-skills-from-Codex-sessions 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/extract-skills-from-claude-code-sessions/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How extract-skills-from-Codex-sessions Compares
| Feature / Agent | extract-skills-from-Codex-sessions | 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?
Automatically extract reusable skills from Codex session transcripts using LLM analysis and wire them into a Stop hook
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.
Related Guides
SKILL.md Source
# Extract Skills from Codex Sessions Wire a Stop hook that fires when Codex ends a session. The hook runs `skill-extractor.py`, which reads the transcript, calls an LLM (via OpenRouter) to identify skill-worthy patterns, and writes a SKILL.md file if extraction succeeds. Use `openai` library for API calls, strip JSON fence artifacts with `.strip()`, and request concise content (<1500 tokens) to avoid truncation. Test end-to-end with synthetic skill-worthy conversations before deploying.
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