agentleFS
EvanBattenEvanBatten/pstack-for-cc/skills/interrogate/SKILL.md

interrogate

Skill2 starsChanged 46 days agoMITView on GitHub

Use for \"interrogate\", \"adversarial review\", \"multi-model review\", \"challenge this\", \"stress test this code\", \"find blind spots\", or \"tear this apart\". Multiple LLM reviewers challenge changes from independent angles.

73 other projects use a version of this, 39 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use ketchup-video, principle-explain-the-number and 8 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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  • ianclearySkill

    principle-explain-the-number

    Apply before you trust, report, or act on a number you measured: a speedup, a regression, a throughput, a latency, or an eval result. Find what limits it, and rule out that it measured something other than the work you think.

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  • ianclearySkill

    poteto-help

    Guides users through pstack setup, /poteto-mode, and picking the skill, playbook, or principle for a task. Type /poteto-help with a question.

    Also in 14 projects that use this

  • Xia-AtaraxiaSkill

    benchmark-checklist

    Vets performance measurements for the limiter, tuning, physical limits, errors, repeatability, relevance, and proof that timed work happened. It applies to 'check this benchmark', 'report this speedup', measured regressions, and choosing a library or configuration from numbers. It reports spread and gaps before drawing a conclusion. Not for general testing policy or choosing test oracles - use principle-testing.

    Also in 37 projects that use this

  • instructureSkill

    ketchup

    Rebuild context after /clear by analyzing uncommitted changes across Canvas LMS and all plugin repositories

    Also in 24 projects that use this

  • drafaelSkill

    in-your-own-words

    Restate the user's goals and the problem they are trying to solve to check understanding. Manual invocation only; use when the user explicitly invokes in-your-own-words or requests this understanding check. Do not solve the task or rewrite the assistant's last answer.

    Also in 24 projects that use this

The file

We split this skill into the rules it gives an agent

8 rules. 0 are common, 6 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 73 edited versions we read keep these.

  • 6

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If the user points at specific files or a diff, use that

    line 17
  2. Distinctive:

    If on a feature branch, run git diff main...HEAD (or the appropriate base branch) for the full changeset

    line 18
  3. Distinctive:

    If the user's message references recent work, gather the relevant files

    line 19
  4. Distinctive:

    Before spawning reviewers, state the intent explicitly.

    line 25
  5. Distinctive:

    Write one clear paragraph.

    line 32
  6. Very specific:

    Read references/reviewer-prompt.md and fill in the template with:

    line 49
  7. Distinctive:

    Note disagreements: If one reviewer flags something and another explicitly says the opposite, that's useful context for the verdict.

    line 65
  8. Very specific:

    Read references/lead-judgment.md for the full framework.

    line 71

“Common” is measured: kept by at least 6 in 10 of the 73 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Interrogate

Spawn one reviewer per configured entry to adversarially review code changes. Each reviewer gets the same prompt and rubric. The adversarial signal comes from independent reviewers in fresh contexts, not assigned personas. No reviewer runs below the parent model.

The deliverable is a synthesized verdict. Do NOT auto-apply changes.

Step 1, Determine Scope

Identify what to review from context:

  • If the user points at specific files or a diff, use that
  • If on a feature branch, run git diff main...HEAD (or the appropriate base branch) for the full changeset
  • If the user's message references recent work, gather the relevant files

Package the diff (or file contents) plus any surrounding context files the reviewers need to understand the code.

Step 2, State the Intent

Before spawning reviewers, state the intent explicitly. Derive this from:

  • The user's message
  • Commit messages
  • PR description if one exists
  • The code itself

Write one clear paragraph. If you're unsure about the intent, ask the user before proceeding.

Step 3, Spawn Reviewers

Launch all reviewers in a single message using the Agent tool. Use the interrogate reviewers list from ~/.claude/pstack-models.md when present, one reviewer per entry, extending or shrinking the Reviewer A/B labels below to the configured entry count. Otherwise use the table defaults.

SubagentDefault model
Reviewer Ainherit-parent
Reviewer Binherit-parent

For each reviewer:

  • subagent_type: pstack-reader
  • model: the configured interrogate reviewers entry, or the table default with no configured line

If a model slug is rejected as unresolvable when you try to spawn the subagent, check the valid slugs in the Agent tool's error message, pick the closest equivalent (prefer the highest-reasoning tier of the same family), spawn with the valid slug, and open a separate PR to update the configured value or default table. Do not block the review on the slug issue. If the configured value is inherit-parent or auto, omit model instead. Never treat those aliases as broken slugs or enter this fallback for them.

Read references/reviewer-prompt.md and fill in the template with:

  1. The stated intent
  2. The diff or file contents
  3. The review rubric from references/rubric.md
  4. The code-quality lens from references/code-quality-review.md

The same filled template goes to all reviewers, so every reviewer applies the code-quality lens.

Step 4, Synthesize

As results come back, build a unified picture:

  1. Parse all findings from the reviewers
  2. Weigh evidence over vote count. Reviewers on the same model make correlated mistakes, so two of them agreeing is weaker evidence than two vendors agreeing. A finding with a cited line or a failing input outranks one that more reviewers raised without either.
  3. Identify lone-reviewer findings. Still worth reading. Judge each by its evidence.
  4. Deduplicate. Different reviewers may describe the same issue differently. Merge these and note which reviewers raised it.
  5. Note disagreements. If one reviewer flags something and another explicitly says the opposite, that's useful context for the verdict.

Step 5, Lead Judgment

You are the lead reviewer, a pragmatic senior engineer, not a neutral aggregator.

Read references/lead-judgment.md for the full framework.

Categorize every finding using these buckets:

  • Act on. Real issues affecting correctness, security, or maintainability given the actual goals. These would block a real PR.
  • Consider. Legitimate points, but you're not sure they outweigh the cost of addressing them right now. Worth the user's attention.
  • Noted. Technically valid but not actionable. Context-dependent, premature optimization, or low-impact given the current stage.
  • Dismissed. Wrong, nitpicky, or missing context. Brief explanation why.

For each finding, include:

  • Which reviewer(s) raised it
  • The category (act on / consider / noted / dismissed)
  • A one-line rationale for the categorization

Output Format

Present the verdict in this structure:

Intent

[The stated intent paragraph from Step 2]

Reviewers

  • Reviewer [label]: [model name], [N findings] (one bullet per reviewer)

Act On

[Findings that should be addressed. For each: description, which reviewers raised it, why it matters.]

Consider

[Findings worth thinking about. For each: description, which reviewers raised it, tradeoff involved.]

Noted

[Valid but low-priority. Brief list.]

Dismissed

[Rejected findings with brief rationale.]

Agreement Map

[Where did reviewers agree, where did they diverge, and what does the pattern of agreement/disagreement tell us?]

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