interrogate
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".
Skill by slappcard
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/slappcard-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
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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
- MIT
- Instructions
- 70 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. 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 in a single `subagents` call (`tasks: [...]`), one per entry on the `interrogate reviewers` line in the pstack model configuration in `~/.pi/agent/AGENTS.md`, labeled Reviewer A, B, and so on to match the entry count. If the rule or that line is missing, use the table defaults. | Subagent | Default model | |----------|---------------| | Reviewer A | `anthropic/claude-opus-5-5:xhigh` | | Reviewer B | `openrouter/x-ai/grok-4.7:xhigh` | Each reviewer gets `agent: "pstack-readonly"` and `model` set to its configured entry, or its table default when there is no configured line. Omit `model` for an `auto` or `inherit-parent` entry, so that reviewer runs on the parent model. If pi rejects a configured model, run that reviewer on its family's table default and say so. Families go by provider prefix: `anthropic/*`, `openrouter/x-ai/*`, `openrouter/openai/*`. With no family match, use Reviewer A's default. If it rejects a table default, spawn with the closest equivalent from the valid models (prefer the same family and thinking tier), and open a separate PR to update the default table. Do not block the review on the model issue. Never treat an alias entry as a rejected model or apply either fallback to it. 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`. Pick each subagent's thinking level for the job, per the Subagents section of the **poteto-mode** skill. ## 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 slappcard/pi-pstack-skills, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 18, 2026 (11 days ago)
- Last release
- Oct 18, 2026 (11 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/slappcard/pi-pstack-skills
- 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/slappcard-interrogate (last updated 2026-10-24)