harvest-proposal-pipeline

name	harvest-proposal-pipeline
description	Automate ICE-Crawler ingestion → registry update → proposal stub creation. Use when you want a full harvest loop that ends with a ready-to-review skill idea.

Harvest → Proposal Pipeline

This skill runs the whole flow when you say so:

1. ICE-Crawler orchestrator ingests a repo (Frost→Glacier→Crystal→Residue).
2. The extraction registry (ice-crawler-harvester/extractions) is appended automatically.
3. A summary stub + optional proposal JSON is generated so you can approve the next skill.

Helper script: scripts/harvest_pipeline.py

Usage

cd <skills_repo>\skills\harvest-proposal-pipeline
python scripts\harvest_pipeline.py <repo_url> --candidate <skill-name> --max-files 80 --max-kb 256

• repo_url — cloneable Git URL (raw/tree/blob URLs are fine; orchestrator normalizes).
• --candidate (optional) — proposed skill name. When supplied, a stub JSON is written under skills/extraction-proposer/proposals/.
• --max-files / --max-kb — bounds for Glacier selection + per-file size.
• Set ICE_CRAWLER_ROOT to the local path of your ICE-Crawler clone before running this script.

What it does

1. Runs python -m engine.orchestrator … inside $env:ICE_CRAWLER_ROOT with timestamped state/runs/run_<ts>.
2. Loads artifact_manifest.json to count files.
3. Writes/updates skills/ice-crawler-harvester/extractions/index.jsonl with: repo, run_dir, manifest path, file count, summary path.
4. Creates/updates skills/ice-crawler-harvester/extractions/<repo-slug>/SUMMARY.md, including an auto-generated “Auto-detected Candidates” section with suggested algorithms/tools.
5. If --candidate is provided, saves skills/extraction-proposer/proposals/<skill-name>.json prefilled with provenance, candidate description, notable file list, and suggested skill structure.

After the run

• Console output highlights:
  • Run folder: the ICE-Crawler fossil (state/runs/run_<timestamp>).
  • Summary: path to SUMMARY.md (includes auto-detected candidate bullets).
  • Proposal stub: JSON file to review in extraction-proposer/proposals/ (if --candidate supplied).
• Once you approve a proposal, copy it into extraction-proposer/catalog/ and mark the status (approved, rejected, etc.) so the catalog becomes the definitive list of green-lit algorithms.
• Open the summary/proposal, add any extra notes, and decide whether to execute the build using skill-creator.

Safety & Notes

• Requires Python + git on PATH (same prerequisites as ICE-Crawler).
• Registry + proposal folders must exist (created by earlier skills).
• Script fails fast if orchestrator errors or the manifest is missing.
• You stay in control: nothing becomes a skill until you approve/edit the generated proposal.

Invoke this skill whenever you want a one-command harvest that comes back with a ready-to-review idea.