debug
Investigation-first debugging — gather evidence, form confirmed root-cause hypothesis, hand off to fix mode with diagnosis file.…
Mine recorded runs for recurring failure patterns and propose durable assets with counted evidence.
$ npx -y skills add Borda/AI-Rig --skill rig-distill --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/rig-distillContext preview
The summary Claude sees to decide when to auto-load this skill.
Mine recorded runs for recurring failure patterns and propose durable assets with counted evidence.
name: Rig Distill description: Mine recorded runs for recurring failure patterns and propose durable assets with counted evidence.
> Read [Evidence rules](../../references/evidence.md).
> This workflow proposes assets. It never installs, edits, or deletes one in the same run.
| Mode | Evidence base | Use it for | | -- | -- | -- | | `runs` | This project's `.opencode-rig/runs/` artifacts (default) | Repeated failures, gate misses, and manual workarounds | | `external` | A named plugin, repository, or skill inventory supplied read-only | Deciding whether an outside asset is worth adopting |
1. **Start the run.** `rig_evidence` with `action: "start"`, `skill: "rig-distill"`, and the artifact root or the external inventory path. 2. **Build the index.** Read every `run.json`; summarize counts by skill, status, and stop reason. Exclude the in-progress distill run itself. For `external`, inventory names and purposes first, without judging quality. 3. **Extract signatures.** Normalize each failure to a stable signature: gate id, finding dimension, loop stop reason, or missing-asset name. Never key on a line number, timestamp, or run id. 4. **Apply the evidence threshold.** A proposal needs at least three occurrences across two or more runs, or one security or critical finding with a systemic cause. Below the threshold, report the pattern as noise and stop. 5. **Classify the gap.** Skill gap, role gap, gate gap, rule gap, reference gap, or tooling gap. Check the existing `rig.json` roster and shipped cards first; a proposal that duplicates an existing asset is rejected with the name of the asset that already covers it. 6. **Cost the proposal.** State maintenance cost, blast radius, and the workflow that would own it. Prefer extending an existing asset over adding a new one. 7. **Falsify each proposal.** Ask whether the pattern predates the current plugin version, whether it is one project's quirk, and whether a gate or a doc line would fix it more cheaply. Keep only proposals that survive. 8. **Finalize.** Record proposals and rejections with `action: "record"`, then `action: "finalize"`.
The evidence index, the patterns that met the threshold, proposals with counts and evidence run ids, rejected proposals with the reason, maintenance cost, and the handoff to `rig-plan` for each accepted proposal.
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
Investigation-first debugging — gather evidence, form confirmed root-cause hypothesis, hand off to fix mode with diagnosis file.…
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