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
- Yeachan-Heo/oh-my-claude-sisyphustheir own version, first seen Nov 8, 2026
- caidanw/skillsedited, first seen Nov 8, 2026
- thensls/nsls-builder-toolkittheir own version, first seen Nov 8, 2026
- Yeachan-Heo/oh-my-claudecodetheir own version, first seen Nov 8, 2026
- endqwerty/endstackedited, first seen Nov 8, 2026
- MammadovEshgin/clean-code-skilltheir own version, first seen Nov 8, 2026
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.
tristanphvnSkillketchup-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
eryzerzSkillmake-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
instructureSkillketchup
Rebuild context after /clear by analyzing uncommitted changes across Canvas LMS and all plugin repositories
Also in 24 projects that use this
drafaelSkillin-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
TheOnlyFusionCubeSkillmaintain-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
TheOnlyFusionCubeSkillcreate-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
Other versions73 projects use a version of this.
Word for word · 0
None found.
Edited · 38
- moeru-ai/xsai★ 66740% same
- uzairansaruzi/p3-stack★ 12678% same
- dsebban/skills★ 7126% same
- overextended/skills★ 2228% same
- Scino/fstack★ 1864% same
- caidanw/skills★ 1145% same
- kkgogogo17/pi-pstack★ 965% same
- 0xrsydn/pstack-pi★ 666% same
- stainless-code/lossless★ 417% same
- anis-dr/skills★ 350% same
28 more not listed
Wrote their own version · 35
- Yeachan-Heo/oh-my-claude-sisyphus★ 41ksame name
- Yeachan-Heo/oh-my-claudecode★ 41ksame name
- backnotprop/pstack★ 3.8ksame name
- savannahfeder/agentbox★ 376same name
- jtaroreh/agystack★ 128same name
- TheOnlyFusionCube/potetos-for-everyone★ 21same name
- creedants/pstack-t3★ 18same name
- painhardcore/pstack★ 18same name
- korallis/skills-collection★ 14same name
- mariusflorescu/pstack-t3★ 13same name
25 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository49 other files it gives its agents.
- architectSkill
- arenaSkill
- automate-meSkill
- benchmark-checklistSkill
- blast-radiusSkill
- broSkill
- correctSkill
- create-verification-skillSkill
- figure-it-outSkill
- howSkill
- maintain-verification-skillSkill
- no-commentsSkill
- poteto-helpSkill
- poteto-modeSkill
- principle-attack-the-premiseSkill
- principle-boundary-disciplineSkill
- principle-build-the-leverSkill
- principle-encode-lessons-in-structureSkill
- principle-exhaust-the-design-spaceSkill
- principle-experience-firstSkill
- principle-explain-the-numberSkill
- principle-fix-root-causesSkill
- principle-foundational-thinkingSkill
- principle-guard-the-context-windowSkill
- principle-laziness-protocolSkill
- principle-make-operations-idempotentSkill
- principle-migrate-callers-then-delete-legacy-apisSkill
- principle-minimize-reader-loadSkill
- principle-model-the-domainSkill
- principle-never-block-on-the-humanSkill
- principle-outcome-oriented-executionSkill
- principle-prove-it-worksSkill
- principle-redesign-from-first-principlesSkill
- principle-separate-before-serializing-shared-stateSkill
- principle-sequence-verifiable-unitsSkill
- principle-subtract-before-you-addSkill
- principle-test-behavior-not-implementationSkill
- principle-type-system-disciplineSkill
- recallSkill
- reflectSkill
- setup-pstackSkill
- show-me-your-workSkill
- swarmSkill
- tddSkill
- teachSkill
- technical-writingSkill
- typescript-best-practicesSkill
- unslopSkill
- whySkill
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
- Distinctive: line 18
If the user points at specific files or a diff, use that
- Distinctive: line 19
If on a feature branch, run git diff main...HEAD (or the appropriate base branch) for the full changeset
- Distinctive: line 20
If the user's message references recent work, gather the relevant files
- Distinctive: line 26
Before spawning reviewers, state the intent explicitly.
- Distinctive: line 33
Write one clear paragraph.
- Very specific: line 48
read-only: enforced by pstack:read-only (no Edit/Write).
- Very specific: line 52
Read references/reviewer-prompt.md and fill in the template with:
- Distinctive: line 68
Note disagreements: If one model flags something and another explicitly says the opposite, that's useful context for the verdict.
- Very specific: line 74
Read references/lead-judgment.md for the full framework.
“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.
Files
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.
| Subagent | Default model / effort |
| Reviewer A | sonnet / medium |
| Reviewer B | haiku / low |
| Reviewer C | sonnet / medium |
For each reviewer:
subagent_type:pstack:read-onlymodelandeffort: resolve eachinterrogate reviewerspair from the live delegation-kit table when present, otherwise the rule or table defaults. Pass both explicitly. Normalizeauto,inherit-parent, andinherittosonnet/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:
- The stated intent
- The diff or file contents
- The review rubric from
references/rubric.md - 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:
- Parse all findings from the reviewers
- Identify consensus. Findings raised by 2+ models independently are highest signal.
- Identify lone-model findings. Still worth reading, but weight accordingly.
- Deduplicate. Different models may describe the same issue differently. Merge these and note which models raised it.
- 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?]
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

