interrogate
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".
Skill by milantracy
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/milantracy-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.
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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
- 106 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 Spawn one reviewer per configured panel entry to adversarially review code changes. Each reviewer gets the same prompt and rubric in a fresh context. The adversarial signal comes from independent passes (and, where configured, different model tiers), 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 | |----------|---------------| | Reviewer A | `opus` | | Reviewer B | `opus` | | Reviewer C | `sonnet` | For each reviewer: - `subagent_type`: `general-purpose` - `model`: the configured `interrogate reviewers` entry, or the table default with no configured line. For an `auto` or `inherit-parent` entry, omit `model` so that reviewer runs on the parent model. - read-only: tell it in the prompt not to edit files (Claude Code has no readonly flag) If the Agent tool rejects a configured entry (the provider may not serve that alias), run that reviewer on the next tier down (`opus` → `sonnet` → `haiku`) and say so. Do not block the review on the model issue. Never treat an `inherit-parent` or `auto` entry as rejected or apply the 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+ reviewers independently are highest signal. 3. **Identify lone-reviewer findings**. Still worth reading, but weight accordingly. 4. **Deduplicate**. Different reviewers may describe the same issue differently. Merge these and note which reviewers 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 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?]
From milantracy/pstack, MIT.
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Signals
- Stars
- 0
- Forks
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- Open issues
- 0
- Last commit (this folder)
- Sep 29, 2026 (29 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/milantracy/pstack
- 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/milantracy-interrogate (last updated 2026-10-24)