debug
Investigation-first debugging — gather evidence, form confirmed root-cause hypothesis, hand off to fix mode with diagnosis file.…
Build a source-backed research brief with labeled claims, contradictions, and explicit limits.
$ npx -y skills add Borda/AI-Rig --skill rig-research --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/rig-researchContext preview
The summary Claude sees to decide when to auto-load this skill.
Build a source-backed research brief with labeled claims, contradictions, and explicit limits.
name: Rig Research description: Build a source-backed research brief with labeled claims, contradictions, and explicit limits.
> Read [Evidence rules](../../references/evidence.md).
> This workflow reads and cites. It never changes product code; a change that follows from the research goes to `rig-plan`.
| Mode | Use it for | Required output | | -- | -- | -- | | `docs` | API contracts, configuration, and migration behavior | Answer per question with version and retrieval date | | `sota` | Compare approaches, libraries, or designs before choosing one | Criteria table, weighted trade-offs, recommendation | | `paper` | Analyze a paper's method, data, metrics, and threats | Claim table, method summary, reproduction cost | | `code-fidelity` | Check whether an implementation matches its paper or spec | Claim-to-code map with missing and divergent items |
1. **Frame the decision.** Write the question the research must answer and the decision it informs. A brief that does not change a decision is not worth the run. 2. **Start the run.** `rig_evidence` with `action: "start"`, `skill: "rig-research"`, and the paths the answer will affect. 3. **Rank sources.** Primary: official documentation at the exact version, source code, the paper itself, specifications. Secondary: maintainer release notes, issue threads, benchmarks with published method. Reject unattributed summaries and content farms. 4. **Record every claim as one of three states.** `sourced` with the URL or identifier and retrieval date, `inferred` with the reasoning chain, or `unknown`. A claim nobody can check is `unknown`, not a fact. 5. **Cross-check decision-changing claims.** Use at least two independent sources, or one primary source plus a reproduction. When sources disagree, report the contradiction and its likely cause; never average it away. 6. **Bind the sources.** `rig_evidence` with `action: "record"` and `inputs` for local sources, `limits` for paywalls, login walls, unreachable hosts, or version drift, and `decision` for the recommendation. 7. **Verify independently.** Dispatch `rig-scientist` with the claims and sources. It must fail a claim that the cited source does not support. Record it with `reviewer`. 8. **Finalize.** `rig_evidence` with `action: "finalize"` and the honest confidence.
The decision and recommendation, a claim table with state, source, and retrieval date, contradictions, threats to validity, limits, confidence, and the next workflow (`rig-plan`, `rig-investigate`, or `rig-optimize`).
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
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