extraction-proposer
Scan ICE-Crawler extraction logs, pick promising algorithms/tools, and emit skill creation proposals (name, scope, source files, next steps).
Best use case
extraction-proposer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Scan ICE-Crawler extraction logs, pick promising algorithms/tools, and emit skill creation proposals (name, scope, source files, next steps).
Teams using extraction-proposer 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/extraction-proposer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How extraction-proposer Compares
| Feature / Agent | extraction-proposer | 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?
Scan ICE-Crawler extraction logs, pick promising algorithms/tools, and emit skill creation proposals (name, scope, source files, next steps).
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.
SKILL.md Source
# Extraction Proposer
Use this skill after ICE-Crawler runs to convert harvested fossils into concrete skill proposals. It expects the extraction registry under `../ice-crawler-harvester/extractions` (relative to this skill folder) and writes proposal specs under `proposals/`.
Reference: [`references/registry-workflow.md`](references/registry-workflow.md)
## Prerequisites
- ICE-Crawler run metadata appended to `extractions/index.jsonl`.
- Per-repo notes in `extractions/<repo-slug>/SUMMARY.md` (preferred) with code pointers.
- jq or Python available for filtering JSONL (optional but useful).
## Workflow
1. **Review registry**
- Open `extractions/index.jsonl` to find recent entries. Use `jq` or Python to filter by tags, repo, or notable files.
- Inspect corresponding `SUMMARY.md` files for algorithm descriptions and code paths.
2. **Select a candidate**
- Criteria examples: unique algorithm, reusable CLI, monitoring utility, scaffolding snippet, etc.
- Note the run folder (`state/runs/<run_id>`) and manifest path for provenance.
3. **Extract details**
- List the files from `artifact_manifest.json` (or the trimmed subset copied into `extractions/<slug>/`).
- Summarize what the algorithm/tool does, triggers, dependencies, and why it deserves a skill.
4. **Create a proposal**
- Use the template below to write `proposals/<candidate>.json` (create `proposals/` if missing):
```jsonc
{
"ts": "2026-02-24T16:40:00Z",
"skill_name": "triadic-selector",
"description": "Deterministic triadic-balanced file selector for repository harvesting pipelines.",
"source_repo": "https://github.com/...",
"run_dir": "state/runs/run_20260224_113500",
"manifest": "state/runs/run_20260224_113500/artifact_manifest.json",
"notable_files": ["engine/glacier_selector.py", "docs/triadic_strategy.md"],
"summary_path": "extractions/triadic-selector/SUMMARY.md",
"proposed_skill_structure": {
"SKILL.md": ["workflow", "parameters", "safety"],
"references/triadic.md": ["derivation", "examples"],
"scripts/selector_demo.py": "optional CLI"
},
"next_actions": [
"Copy selector code into scripts/",
"Write SKILL.md instructions",
"Add references"
]
}
```
5. **Hand off**
- Once a proposal JSON is ready, use `skill-creator` (or manual process) to implement the actual skill described.
- Update `extractions/<repo-slug>/SUMMARY.md` with the proposal link so the registry stays synchronized.
## Tips
- Keep proposals small and focused; one algorithm/tool per spec.
- Always cite the original run folder and manifest for traceability.
- If multiple skills can emerge from a single repo, create separate proposals referencing the same run.
- When a skill is built, link back to the proposal JSON for provenance.
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