procedural-distiller

# Procedural Distiller Use this skill after a task has succeeded and the session contains enough signal to preserve. The goal is to extract procedural knowledge, not to summarize the conversation. ## Triggering Rules Run the distillation flow when all of the following are true: 1. The task is finished successfully. 2. The session contains at least 5 relevant tool calls across `exec`, `read`, `write`, `edit`, or `browser`. 3. At least one of these applies: - The user explicitly asks to remember the workflow. - The task involved environment setup, debugging, or a multi-step repair. - Compaction risk is high and the trace contains concrete parameters worth preserving. Do not run while the task is still active. Skip trivial sessions dominated by a single read or a one-line answer. ## Distillation Workflow 1. Read the recent trace and keep only relevant tool events. 2. Separate successful steps from failed attempts. 3. Preserve concrete commands, file paths, parameter values, and code snippets that materially contributed to the outcome. 4. Convert the result into three sections: - `Success Pattern` - `Failure Triggers` - `Snippets` 5. Write a learned skill under `skills/learned/learned-<task-slug>/`. 6. Persist a utility score and source metadata in `memory.json`. Read [references/trace-format.md](references/trace-format.md) only if the incoming trace shape is unclear. ## Execution Notes - Favor exact commands over abstract summaries. - Keep failed steps only when they teach a future agent what to avoid. - Collapse repetitive probes into one representative line. - If a write or edit step changed the final behavior, include the smallest useful snippet. - If a trace already contains a user rating, store it. Otherwise default to `3` and let a future caller update `memory.json`. ## Local CLI Run the bundled script directly: ```bash python distill_logic.py --trace /path/to/trace.json --task "repair build cache" --output-root /path/to/skills ``` Useful flags: - `--utility-score 4` - `--learned-root learned` - `--min-tool-calls 5` - `--max-events 20` - `--force` ## Output Contract The generated learned skill must contain: - `SKILL.md` with valid frontmatter (`name`, `description`) and procedural sections - `agents/openai.yaml` for UI metadata - `memory.json` with `utility_score`, source task details, and generation metadata ## Stop Conditions Stop and do not emit a learned skill when: - the trace is marked unsuccessful - there are too few relevant tool calls - the trace lacks enough detail to reconstruct a reusable procedure