Interrogate

---
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."
disable-model-invocation: true
---

# Interrogate

Spawn one reviewer per configured entry to adversarially review code changes. Each reviewer gets the same prompt and rubric. The adversarial signal comes from independent reviewers in fresh contexts, not assigned personas. No reviewer runs below the parent model.

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` list from `~/.claude/pstack-models.md` when present, one reviewer per entry, extending or shrinking the Reviewer A/B labels below to the configured entry count. Otherwise use the table defaults.

| Subagent | Default model |
|----------|---------------|
| Reviewer A | `inherit-parent` |
| Reviewer B | `inherit-parent` |

For each reviewer:
- `subagent_type`: `pstack-reader`
- `model`: the configured `interrogate reviewers` entry, or the table default with no configured line

If a model slug is rejected as unresolvable when you try to spawn the subagent, 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 the valid slug, and open a separate PR to update the configured value or default table. Do not block the review on the slug issue. If the configured value is `inherit-parent` or `auto`, omit `model` instead. Never treat those aliases as broken slugs or enter this fallback for them.

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 reviewer applies the code-quality lens.

## Step 4, Synthesize

As results come back, build a unified picture:

1. **Parse all findings** from the reviewers
2. **Weigh evidence over vote count**. Reviewers on the same model make correlated mistakes, so two of them agreeing is weaker evidence than two vendors agreeing. A finding with a cited line or a failing input outranks one that more reviewers raised without either.
3. **Identify lone-reviewer findings**. Still worth reading. Judge each by its evidence.
4. **Deduplicate**. Different reviewers may describe the same issue differently. Merge these and note which reviewers raised it.
5. **Note disagreements**. If one reviewer 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 reviewer(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?]