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interrogate

Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".

Skill by n3m6

IndexedGPL-3.0★ 19
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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/n3m6-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 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.

Use cases: testing, ml, agent-building

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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.

Tools
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Secrets
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Install scripts
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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
GPL-3.0
Metadata
opencode/autoinvoke: false
Instructions
95 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 model to adversarially review code changes. Each model gets the same prompt and rubric. Independent reviewers receive the same evidence and rubric. Model diversity adds signal when genuinely available; same-model runs are independent reviews, not a multi-provider panel.

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

Read [OpenCode runtime](../../references/opencode-runtime.md). Use the configured `interrogate-reviewer` role and `reviewers` count, default three independent runs inheriting the parent. A configured role list supplies one reviewer per entry unless the user specified a different count. Launch reviewers within actual concurrency limits. Each gets a read-only assignment; use enforced restrictions when available and disclose when the brief is the only restriction. No code edits or external comments are authorized by this review.

If independent delegation is unavailable or prohibited, perform a direct adversarial review and identify the missing independent panel in the verdict. Do not manufacture model agreement or label a self-review as independent. Name the actual model used for each reviewer when known.

Read [references/reviewer-prompt.md](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](references/rubric.md)
4. The code-quality lens from [references/code-quality-review.md](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](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?]

From n3m6/opencode-config, GPL-3.0.

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Stars
19
Forks
2
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Last commit (this folder)
Oct 6, 2026 (22 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
GPL-3.0
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

interrogate skill. Agent Mart. https://agentmart.dev/skills/n3m6-interrogate (last updated 2026-10-24)