debug
Investigation-first debugging — gather evidence, form confirmed root-cause hypothesis, hand off to fix mode with diagnosis file. TRIGGER when: user reports a…
Research docs, papers, or state of the art; provide source-backed recommendations and caveats.
$ npx -y skills add Borda/AI-Rig --skill research --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/researchContext preview
The summary Claude sees to decide when to auto-load this skill.
Research docs, papers, or state of the art; provide source-backed recommendations and caveats.
name: research description: Research docs, papers, or state of the art; provide source-backed recommendations and caveats.
Source-backed research for documentation, API migration, paper, or state-of-the-art questions.
{
"question": "required research question",
"mode": "docs|sota|paper|methodology|code-fidelity",
"constraints": [
"optional codebase, compute, version, or implementation constraints"
],
"done_when": "recommendations are source-backed with caveats and confidence"
}Run `create_run.py --skill research` per `../../shared/helper-cli-contract.md`.
Modes:
Write `<run-directory>/sources.md`:
| Source | Type | Date/version | Why reliable | Used for | | --- | --- | --- | --- | --- |
Source rules:
For `sota`, `paper`, `methodology`, or `code-fidelity`, read and apply `../../shared/specialist-orchestration.md` only when independent expertise improves correctness; otherwise do not load it. Write `<run-directory>/specialist-research-plan.md` with context packs for:
Do not send full papers, repositories, or all search results to every specialist. Give each only source excerpts, code files, questions needed for its axis.
Inspect `python PLUGIN_ROOT/shared/collect_diff.py --help`; collect `working-tree` scope into `<run-directory>/baseline`. Run topic scan separately; record unavailable paths/collection failures as evidence gaps.
**Structural context (optional)**: for `sota`/`code-fidelity` questions naming Python module/symbol, also probe codemap-py once: `python PLUGIN_ROOT/shared/codemap_adapter.py context --category analysis [--target <qname>] --out <run-directory>/codemap-context.json`. Per `../../shared/codemap-contract.md`, absence/incompatibility is non-fatal — continue with codebase mapping above. Persist result once here; any specialist context pack from step 03 includes `<run-directory>/codemap-context.json`, never fresh query.
Run `git diff --check` as argv command. Write its combined output to `<run-directory>/review.txt` and retain its exit status as review evidence; do not erase nonzero result.
Follow `../../shared/helper-cli-contract.md` and authoritative help. For research-only work, mark lint, format, types, tests inapplicable with concrete reasons; review requires non-empty `research.md`, `sources.md`, clean diff check. Write `RESEARCH_METADATA`, validate as `research`, promote only validated candidate.
Replace explicit skip with relevant command when research includes executable validation.
1. Missing question => fail. 2. No primary sources for high-impact/current claims => fail. 3. Recommendation not tied to constraints => fail. 4. Paper/code-fidelity claim without code or source reference => fail. 5. Result artifact missing => fail.
Required:
Conditional:
On source-protocol/recommendation-policy change, update calibration:
Before writing result candidate, follow `../../shared/final-handoff-contract.md`: render and bind `final-handoff.json`, `final.md`, and `final-handoff.validation.json`; after both validators and promotion pass, emit `final.md` verbatim.
Use `../../shared/quality-gates.md`.
Final chat follows shared frame with `Next steps`. `Outcome`: recommendation/support level. `Results`: exactly `Recommendation | Evidence | Decision | Caveat / next check`, one row/recommendation. Include source freshness and each gap/caveat's next check.
Minimum artifact payload template: `result-template.json`.
Practical agent workflows for Python, ML, and open-source maintenance. AI-Rig turns recurring work—scoping a change, reproducing a bug, reviewing a pull request, running an experiment, or checking release readiness—into explicit workflows with specialist
Repo: Borda/AI-Rig
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