From scratchydisk-skills
Iterates devil's-advocate review on a plan or spec until a round finds no real bugs. Each round: surface concerns, apply fixes inline, commit, repeat. Min 2 rounds, max 4. Stops when only nits/cosmetic issues remain. Use when refining a written artifact before execution — not for one-shot review (use /devils-advocate for that).
How this skill is triggered — by the user, by Claude, or both
Slash command
/scratchydisk-skills:devils-advocate-loopThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are running iterative devil's-advocate review on a written artifact (plan, spec, design doc) until it's stable. Each round you wear two hats: the reviewer (challenge it) and the implementer (apply fixes). Between rounds you commit the diff. You stop when a round finds nothing real left to fix.
You are running iterative devil's-advocate review on a written artifact (plan, spec, design doc) until it's stable. Each round you wear two hats: the reviewer (challenge it) and the implementer (apply fixes). Between rounds you commit the diff. You stop when a round finds nothing real left to fix.
Announce at start: "I'm using the devils-advocate-loop skill — running rounds until a clean pass."
/devils-advocate instead and stop after one report./devils-advocate, this skill applies the fixes inline. Read the file, Edit it, commit the result.docs(plan): DA round N — <one-line summary> (or docs(spec): DA round N — ... etc., matching what's being fixed).For each round (round numbers are 1-indexed counting from your first round; honour any prior rounds the user mentions):
State briefly what the artifact gets right. If you can't articulate this, your review is probably off-base — read the artifact again before continuing.
Use the bundled references (relative to this skill's directory):
references/questioning-frameworks.md — pre-mortem, inversion, Socratic probing, Six Thinking Hats, Five Whysreferences/blind-spots.md — security, scalability, data lifecycle, integration points, failure modes, concurrency, env gaps, observability, deployment, edge cases, multi-tenancyreferences/ai-blind-spots.md — happy path bias, scope acceptance, confidence without correctness, pattern attraction, reactive patching, context rot, library hallucination, security as afterthought, over-abstractionLoad only the references you need; don't dump them into context unnecessarily.
Format each concern:
Concern: <one-line summary>
Severity: Critical | High | Medium | Low
Framework: <which framework surfaced this>
What I see:
<specific issue with file:line references where possible>
Why it matters:
<consequence if not fixed>
What to do:
<specific, actionable fix>
Honest severity matters. Don't inflate.
For every Critical + High + actionable-Medium concern:
You may also surface Low issues but do not fix them — note them in the round's report for the user's awareness.
Single commit at the end of the round. Stage the modified artifact(s) only — don't sweep in unrelated changes.
git add <files modified during this round>
git commit -m "docs(plan): DA round N — <one-line summary of what got fixed>"
If you're working in a worktree, the branch is whatever the user is currently on — don't switch.
After the last round, output a single concise summary (≤ 400 words):
| Round | Critical | High | Medium | Low | Commit |
|---|---|---|---|---|---|
| 1 | 1 | 2 | 1 | 0 | abc123 |
| ... |
Guides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.
npx claudepluginhub scratchydisk/claude-skills --plugin scratchydisk-skills