interrogate
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".
Skill by weiihann
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/weiihann-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.
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About
Use cases: testing, ml
Reviews
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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
- 65 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 Follow the [portable runtime contract](../pstack-omp/references/runtime.md) for reviewer panels, concurrent child execution, model choices, and questions. 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 concurrently through the host's task facility, one per entry on the `interrogate reviewers` line in `$PSTACK_CONFIG (or .pstack/config.md)`, labeled Reviewer A, B, and so on to match the entry count. If the configuration or that line is missing, use a diverse host-supported default panel drawn from `reviewer`, `planner`, `designer`, and `inherit-parent`. Each reviewer is read-only and receives the configured panel choice plus a standalone brief. If the host task facility rejects a configured entry, run that reviewer on the default panel choice of the same family and say so. Families go by model prefix (`claude-*`, `gpt-*`, `grok-*`) or canonical role. With no family match, use Reviewer A's default. If it rejects a default, pick the closest live role or model exposed by the host, preferring the same family and reasoning tier, record the substitution, and open a separate PR to update the configured value or default panel. Do not block the review on a rejected choice. Never treat an alias entry as a rejected choice or apply either fallback to it. `inherit-parent` and `auto` mean to omit an explicit model choice. 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`. ## 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 weiihann/pstack-omp, MIT.
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Signals
- Stars
- 2
- Forks
- 1
- Open issues
- 0
- Last commit (this folder)
- Oct 14, 2026 (15 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/weiihann/pstack-omp
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
interrogate skill. Agent Mart. https://agentmart.dev/skills/weiihann-interrogate (last updated 2026-10-24)