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/rig-plan

Produce a frozen implementation plan with owned paths, configured gates, risks, and acceptance criteria.

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ai-rig
2948 skills16 agents2 MCP
Install
$ npx -y skills add Borda/AI-Rig --skill rig-plan --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-plan

Context preview

The summary Claude sees to decide when to auto-load this skill.

Produce a frozen implementation plan with owned paths, configured gates, risks, and acceptance criteria.

SKILL.md

rig-plan.SKILL.md
name: Rig Plan
description: Produce a frozen implementation plan with owned paths, configured gates, risks, and acceptance criteria.

Rig Plan

> Read [Evidence rules](../../references/evidence.md) before writing any factual claim.

Produce one plan that implementation can execute without re-deciding scope.

Input

  • Goal or issue.
  • Target paths, PR, or area, when known.
  • Constraints: compatibility, deadlines, review requirements.

Workflow

1. **Classify scope.** `TRIVIAL` under 3 files and 50 changed lines; `LOCAL` one subsystem; `BROAD` cross-subsystem or 8+ files; `HIGH_RISK` when auth, credentials, data, release, CI, or migrations are touched. State the classification and why. 2. **Collect evidence.** Read the actual source, tests, configuration, and consumers. Do not plan from filenames alone. Record each authoritative file in the run. 3. **Start the run.** Call `rig_evidence` with `action: "start"`, `skill: "rig-plan"`, and the target paths. Keep the returned `run_id`. 4. **Map the change.** List each affected component, its consumer, the owned path, and the check that proves it still works. 5. **Write the plan** to `<run-dir>/plan.md` with: goal, scope classification, numbered steps, owned paths, gates, risks, rollback, acceptance criteria, and explicit non-goals. 6. **Challenge the plan once.** Dispatch `rig-challenger` (native subagent when installed, otherwise `rig_dispatch`) with the plan, source digests, and acceptance criteria. Accept only findings that cite source. 7. **Freeze it.** Record the plan with `rig_evidence` (`action: "record"`, `plan_path`, and `reviewer` naming the independent challenger) and finalize.

Rules

  • A frozen plan is read-only once implementation starts. If source contradicts the plan, stop and report the contradiction; never rewrite the plan to match what was built.
  • Every step names its owned path. Two steps may not own the same path.
  • Gates come from `rig_gate` detection, never from invented commands.
  • If no configured gate exists, record a gate exemption and say what manual check replaces it.
  • Plans stay in the run directory. Do not ship plan text or private evidence as product content.

Output

Report the plan path, scope classification, digest, step count, gates, top risks, and any unresolved decision. Do not claim the change is implemented.

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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