description: Run the MEGA-Code skill extraction pipeline to analyze Claude Code sessions and generate reusable skills and strategies. argument-hint: [--project [@]] [--model ] [--poll-timeout ] [--include-claude] allowed-tools: Bash, Read, Write, Edit, AskUserQuestion disable-model-invocation: true

Run Skill Extraction Pipeline

Extract reusable skills and coding strategies from your Claude Code sessions.

⚠️ Important: Pipeline is Long-Running

The pipeline command blocks until the server finishes processing. The server runs the pipeline asynchronously and this client polls for completion.

DO NOT interrupt the command (Ctrl+C) while it is running. Silence with no new output for several minutes is completely normal during LLM inference (especially with gpt-5-mini or other reasoning models).

The default poll timeout is 20 minutes. For longer runs, use --poll-timeout:

--poll-timeout 3600 — wait up to 1 hour
--poll-timeout 0 — wait indefinitely (no timeout)

Setup

MEGA_DIR="${CLAUDE_PLUGIN_ROOT:-$(cat ~/.local/share/mega-code/plugin-root 2>/dev/null)}"
uv run --directory "$MEGA_DIR" python -m mega_code.client.check_auth

If the auth check fails (non-zero exit), show the output to the user and stop.

All commands below assume MEGA_DIR is set.

Flags

Flag	Behavior
(none)	Process current session only
`--project`	All sessions in current project
`--project @name`	Specific project by name prefix
`--session-id <uuid>`	Specific session
`--model <alias>`	LLM model (default: server picks best)
`--poll-timeout <seconds>`	Max seconds to poll for completion (default: 1200 = 20 min; 0 = indefinite)
`--include-claude`	Include related Claude Code sessions from the project

Project argument formats (all equivalent): @mega-code · mega-code · mega-code_b39e0992 · /path/to/project

Running the Pipeline

All variables must be in one single Bash call so $LOG and $MEGA_DIR stay in scope:

LOG="/tmp/mega-code-run-$(date +%Y%m%d-%H%M%S).log" && \
  echo "Pipeline log: $LOG" && \
  export CLAUDE_PROJECT_DIR="$PWD" && \
  uv run --directory "$MEGA_DIR" python -m mega_code.client.run_pipeline [FLAGS] 2>&1 | tee "$LOG"

Replace [FLAGS] with desired combination from the table above. Tell the user the log path so they can monitor with tail -f or check after completion.

Model Options

Alias	Provider
`gemini-3-flash`	Google
`gpt-5-mini`	OpenAI

When omitted, server selects based on configured LLM keys (priority: Gemini > OpenAI). Falls back to gemini-3-flash.

Pipeline Outputs

Skills & Strategies — saved to pending dirs for review/install
Lesson Learned documents — saved to ~/.local/share/mega-code/data/feedback/{project_id}/{run_id}/lessons/ (from sessions tagged lesson_learn)

Handling Active Pipeline (Exit Code 2)

If the pipeline command exits with code 2, a pipeline is already running. Parse the JSON output to get conflict.run_id and conflict.project_id.

Use the AskUserQuestion tool to present these options:

Question: "A pipeline is already running for this project (run_id: {run_id}). What would you like to do?"

Options:

"Stop it and start a new one"
"Wait for the existing run to finish"
"Leave it running — exit without action"

Option 1 — Stop and restart:

uv run --directory "$MEGA_DIR" python -m mega_code.client.cli pipeline-stop --run-id <RUN_ID>

Then re-run the pipeline command from "Running the Pipeline" section.

Option 2 — Wait for existing run:

uv run --directory "$MEGA_DIR" python -m mega_code.client.run_pipeline \
  --poll-existing <RUN_ID> --project <PROJECT_ID> [--poll-timeout <seconds>] 2>&1 | tee "$LOG"

Then follow the Post-Pipeline Workflow as normal.

Option 3 — Leave it running: Return immediately. Do not print anything or ask further questions.

Handling Server Timeout (Exit Code 3)

If the pipeline command exits with code 3, the pipeline exceeded the server's max runtime and was terminated. Parse the JSON output for timeout.error details.

Use the AskUserQuestion tool to present these options:

Question: "The pipeline timed out on the server ({error message}). What would you like to do?"

Options:

"Run again — start a fresh pipeline run"
"Do nothing — exit without action"

Option 1 — Run again: Re-execute the pipeline command from "Running the Pipeline" section.

Option 2 — Do nothing: Return immediately. Do not print anything or ask further questions.

Post-Pipeline Workflow (MANDATORY)

The pipeline prints a JSON object with additionalContext on completion. You MUST parse and follow the embedded workflow immediately — do NOT just report "pipeline complete".

Steps:

Parse run_id and project_id from the pipeline output JSON (additionalContext).
Run this command to get the detailed review workflow instructions:

uv run --directory "$MEGA_DIR" python -m mega_code.client.pending review \
  --run-id <RUN_ID> --project-id <PROJECT_ID>

Follow the printed instructions exactly — they contain the full review, install, and archive workflow.