interrogate

# Interrogate Spawn one reviewer per configured model to adversarially review code changes. Each model gets the same prompt and rubric. The adversarial signal comes from model diversity, 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 Task tool. **Other harnesses.** The spawns in this skill use Cursor's `Task` tool. In another harness, use its subagent tool: `Agent` in Claude Code (`subagent_type: general-purpose`), `task` in OpenCode (`subagent_type: general`), `spawn_agent` in Codex. Keep the prompt and the model. Drop parameters your tool doesn't have. If your harness has no subagent tool, as in Pi without an extension, run each reviewer yourself, one after another. Use the `interrogate reviewers` list from the pstack settings file when present (`~/.cursor/rules/pstack-models.mdc` in Cursor, `~/.agents/pstack-models.md` in other harnesses), one reviewer per entry, extending or shrinking the Reviewer A/B/C/D labels below to the configured entry count. Otherwise use the table defaults. | Subagent | Default model | |----------|---------------| | Reviewer A | `claude-fable-5-1-thinking-max` | | Reviewer B | `gpt-5.6-sol-max` | | Reviewer C | `grok-4.6-fast-xhigh` | | Reviewer D | `claude-opus-5-thinking-xhigh` | For each reviewer: - `subagent_type`: `generalPurpose` - `model`: the configured `interrogate reviewers` entry, or the table default with no configured line - `readonly`: `true` If a model slug is rejected as unresolvable when you try to spawn the subagent, check the valid slugs in the subagent tool's error message or your harness's model list, 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 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` 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?]