agentleFS
anhermonanhermon/pstack-claude/skills/interrogate/SKILL.md

interrogate

Skill0 starsChanged 38 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, 38 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use ketchup-video, make-bot-ui and 5 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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    ketchup-video

    Catch the user up like /keel:ketchup, with a short silent video of slides for what happened and the waiting calls as text under it. Use for /keel:ketchup-video.

    Also in 14 projects that use this

  • eryzerzSkill

    make-bot-ui

    Use when building a custom UI (page, dashboard, buttons) that should wake a Grok Bot over a webhook, when the user must provide a webhook sender key, or when exposing that UI on Tailscale.

    Also in 20 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

  • TheOnlyFusionCubeSkill

    maintain-formal-verification-skill

    Bring a project's formal verification back in line with the code after the code changes: find the commits that touched verified code, update the model, harness, or properties to describe the code as it is now, rerun, and say whether each failure is drift in the verification or a bug in the code. Use for /maintain-formal-verification-skill, \"is the model still true to the code\", or after merges and refactors that touched verified code without rechecking it.

    Also in 18 projects that use this

  • TheOnlyFusionCubeSkill

    create-formal-verification-skill

    Generate a project-local skill that formally verifies the code where a bug costs the most and tests reach the least, in any language, with whichever verification tool fits. Use for /create-formal-verification-skill, \"model-check this race\", \"prove this invariant\", or when concurrency, retries, state machines, parsers, or unsafe code need more than tests.

    Also in 19 projects that use this

  • +1 more from TheOnlyFusionCube

The file

We split this skill into the rules it gives an agent

9 rules. 0 are common, 6 are distinctive, and 3 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.

  • 3

    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 18
  2. Distinctive:

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

    line 19
  3. Distinctive:

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

    line 20
  4. Distinctive:

    Before spawning reviewers, state the intent explicitly.

    line 26
  5. Distinctive:

    Write one clear paragraph.

    line 33
  6. Very specific:

    read-only: enforced by pstack:read-only (no Edit/Write).

    line 48
  7. Very specific:

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

    line 52
  8. Distinctive:

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

    line 68
  9. Very specific:

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

    line 74

“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

Claude Code: read Cursor terms in this skill through claude-code/runtime.md (tools, subagents, models, transcripts).

Spawn one reviewer per configured model to adversarially review code changes. Each model gets the same prompt and rubric. The adversarial signal comes from model diversity, not assigned personas.

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 line in ~/.claude/rules/pstack-models.md, one reviewer per entry, extending or shrinking the Reviewer A/B/C labels below to the configured entry count. If the rule or that line is missing, use the table defaults.

SubagentDefault model / effort
Reviewer Asonnet / medium
Reviewer Bhaiku / low
Reviewer Csonnet / medium

For each reviewer:

  • subagent_type: pstack:read-only
  • model and effort: resolve each interrogate reviewers pair from the live delegation-kit table when present, otherwise the rule or table defaults. Pass both explicitly. Normalize auto, inherit-parent, and inherit to sonnet/medium. Fan-outs of 3+ use sonnet/haiku, never opus.
  • read-only: enforced by pstack:read-only (no Edit/Write); still say "do not modify files" in the prompt

If the Agent tool rejects a configured entry, run that reviewer on the table default of its family and say so. Families are Claude (opus, sonnet, haiku, fable, claude-*), codex, and gemini; see pstack's claude-code/runtime.md. With no family match, use Reviewer A's default. If it rejects a table default, 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 it, and open a separate PR to update the default table. Do not block the review on the slug issue. Never treat an alias entry as a rejected slug or apply either fallback to it.

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 model applies the code-quality lens.

Step 4, Synthesize

As results come back, build a unified picture:

  1. Parse all findings from the reviewers
  2. Identify consensus. Findings raised by 2+ models independently are highest signal.
  3. Identify lone-model findings. Still worth reading, but weight accordingly.
  4. Deduplicate. Different models may describe the same issue differently. Merge these and note which models raised it.
  5. Note disagreements. If one model 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 model(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 models raised it, why it matters.]

Consider

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

Noted

[Valid but low-priority. Brief list.]

Dismissed

[Rejected findings with brief rationale.]

Agreement Map

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

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