interrogate
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".
Skill by chinlung
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/chinlung-interrogate
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: testing, ml
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/interrogate
What's inside
- Skill name
interrogate- Description
- 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.
- License
- MIT
- Instructions
- 72 lines in SKILL.md · view on GitHub
Files (5)
- SKILL.md
- code-quality-review.md · references/
- lead-judgment.md · references/
- reviewer-prompt.md · references/
- rubric.md · references/
SKILL.md (first lines)
# Interrogate In Pi, read [the Pi adapter](../../docs/harness/pi.md) once per session before you follow this skill. It maps the Cursor names in pstack skills to Pi. Spawn one reviewer per configured model to adversarially review code changes. The adversarial signal comes from model diversity, not assigned personas. The deliverable is a synthesized verdict. Do NOT auto-apply changes. ## Step 1, 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 with any surrounding context files the reviewers need to understand the code. ## Step 2, Intent Before spawning reviewers, state the intent in one clear paragraph from the user's message, commit messages, the PR description if one exists, and the code. If you're unsure about the intent, ask the user before proceeding. ## Step 3, Spawn Reviewers Launch all reviewers in a single message with the `subagent` tool, one per entry on the `interrogate reviewers` line in the pstack role map (the `pstack_models` tool returns it), labeled Reviewer A, B, and so on to match the entry count. If the rule or that line is missing, use the table defaults. | Subagent | Default model | |----------|---------------| | Reviewer A | the role map's entry | | Reviewer B | the role map's entry | Each reviewer gets `agent: "reviewer"`, whose tools are read-only, and `model` set to its configured entry, or its table default when there is no configured line. Omit `model` for an `auto` or `inherit-parent` entry, so that reviewer runs on the parent model. If the `subagent` tool rejects a configured entry, run that reviewer on its family's table default and say so. A family is the model's vendor lineage (`claude`, `gpt`, `gemini`, …), not its provider, as the Pi adapter defines it, so one model reached through two providers is one family. A rejected entry runs on the same role's other entry. With no family match, use the role map's first entry. If it rejects a table default, spawn with the closest equivalent from the valid slugs in its error message (prefer the same family and reasoning tier), 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 same template for every reviewer with the stated intent, the diff or file contents, the rubric from `references/rubric.md`, and the code-quality lens from `references/code-quality-review.md`. Pick each subagent's reasoning level and fast or standard speed for the job, per the Subagents section of the **poteto-mode** skill. ## Step 4, Synthesize As results come back, build a unified picture. Parse every reviewer's findings. Merge findings that describe the same issue differently, and note which models raised each one. Findings two or more models raised independently are the highest signal. Read a lone model's finding, but weight it accordingly. Note disagreements. If one model flags something and another explicitly says the opposite, that is 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. Put every finding in one of four buckets, with the model(s) that raised it and a one-line rationale. - **Act on**. Real issues affecting correctness, security, or maintainability given the actual goals. They would block a real PR. - **Consider**. Legitimate, but you're not sure they outweigh the cost of addressing them now. Worth the user's attention. - **Noted**. Technically valid but not actionable. Context-dependent, premature optimization, or low-impact at this stage. - **Dismissed**. Wrong, nitpicky, or missing context, with a brief reason. ## Output Format ### Intent > [The stated intent paragraph from Step 2] ### Reviewers - Reviewer [label]: [model name], [N findings] (one bullet per reviewer) ### Act On [Each: description, which models raised it, why it matters.] ### Consider [Each: description, which models raised it, the tradeoff.] ### Noted [Brief list.] ### Dismissed [Each with a brief rationale.] ### Agreement Map [Where models agreed and diverged, and what that pattern tells us.]
From chinlung/pi-pstack, MIT.
More from chinlung/pi-pstack
- 0.561score
setup-pstack
chinlung
Configure which models pstack uses per role and at what reasoning budget.
SkillAFreshness: AClaude Code +9 - 0.541score
architect
chinlung
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.
SkillAFreshness: AClaude Code +9 - 0.541score
how
chinlung
Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which…
SkillAFreshness: AClaude Code +9 - 0.541score
create-verification-skill
chinlung
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform.
SkillAFreshness: AClaude Code +9 - 0.536score
maintain-verification-skill
chinlung
Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature…
SkillAFreshness: AClaude Code +9 - 0.536score
arena
chinlung
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
SkillAFreshness: AClaude Code +9 - 0.536score
automate-me
chinlung
Use for "automate me", "create/update/refresh my -mode skill", "turn/capture my preferences or working style into a skill", or wanting agents to follow how…
SkillAFreshness: AClaude Code +9 - 0.533score
no-comments
chinlung
Spawn Comment Sicko, fix accepted findings, and offer encodings for claimed constraints.
SkillAFreshness: AClaude Code +9
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 22, 2026 (8 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/chinlung/pi-pstack
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from chinlung
- 0.561score
setup-pstack
chinlung
Configure which models pstack uses per role and at what reasoning budget.
SkillAFreshness: AClaude Code +9 - 0.541score
architect
chinlung
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.
SkillAFreshness: AClaude Code +9 - 0.541score
how
chinlung
Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which…
SkillAFreshness: AClaude Code +9 - 0.541score
create-verification-skill
chinlung
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform.
SkillAFreshness: AClaude Code +9 - 0.536score
maintain-verification-skill
chinlung
Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature…
SkillAFreshness: AClaude Code +9 - 0.536score
arena
chinlung
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
SkillAFreshness: AClaude Code +9
Cite this page
interrogate skill. Agent Mart. https://agentmart.dev/skills/chinlung-interrogate (last updated 2026-10-24)