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/rig-distill

Mine recorded runs for recurring failure patterns and propose durable assets with counted evidence.

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ai-rig
2948 skills16 agents2 MCP
Install
$ npx -y skills add Borda/AI-Rig --skill rig-distill --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/rig-distill

Context 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.

SKILL.md

rig-distill.SKILL.md
name: Rig Distill
description: Mine recorded runs for recurring failure patterns and propose durable assets with counted evidence.

Rig Distill

> Read [Evidence rules](../../references/evidence.md).

> This workflow proposes assets. It never installs, edits, or deletes one in the same run.

Modes

| 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 |

Workflow

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"`.

Rules

  • Do not delete, prune, archive, or rewrite run artifacts. Deletion belongs to `rig_doctor` with explicit confirmation.
  • Do not install an external asset, copy its files, or run its scripts. `external` mode is read-only.
  • Do not count the same run twice or inflate a pattern by splitting one signature into variants.
  • Do not propose a new skill for something a rule, a gate, or one reference paragraph already prevents.
  • Redact secrets, tokens, personal paths, and proprietary content from every quoted artifact.

Output

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.

Read more
Ships withai-rig

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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