interrogate
Challenge a code change with independent adversarial reviewers and consolidate evidence-backed findings.
Skill by MachineLearning-Nerd
Chalk Labs
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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/machinelearning-nerd-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
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
- Secrets
- None asked for
- Install scripts
- None found
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
- Challenge a code change with independent adversarial reviewers and consolidate evidence-backed findings. Use for interrogate or adversarial review.
- License
- MIT
- Instructions
- 27 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Interrogate Read ../pstack-harness/SKILL.md. Identify the exact diff, base, and review scope. State the change's intent from the user's request, commit messages, PR description, and code. If intent remains uncertain after inspection, ask the user. Read references/reviewer-prompt.md, references/rubric.md, and references/code-quality-review.md. Fill the same reviewer template for every arm with the intent, scoped artifact, rubric, and code-quality lens. Review relevant lenses without forcing those that do not apply. Resolve `interrogate reviewers` and spawn one independent read-only reviewer per arm. Omit model overrides and use only supported effort fields. Do not supply the lead's suspected findings as the answer. A same-model panel is independent review, not model diversity. Reviewers cite files, triggers, impact, and evidence. Assess each brief under [the effort contract](../pstack-harness/SKILL.md#task-difficulty-and-effort). Wait for all arms or report the failed/missing arms explicitly. Confirm findings against the artifact, deduplicate, and report disagreement. Do not count inline self-review as an independent verdict. Read references/lead-judgment.md and put each finding in Act on, Consider, Noted, or Dismissed, with the reviewers that raised it and a reason. Give the intent, each reviewer's actual identity and finding count, and an agreement map. Report agreement among independent instances of the same model. This panel does not test agreement across models. Editing requires an implementation request separate from the review itself.
From MachineLearning-Nerd/pstack-dual, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 15, 2026 (15 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Check pstack installation and configure role effort and panel size for Codex and Claude Code.
SkillAFreshness: AClaude Code +9 - 0.561score
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Cite this page
interrogate skill. Agent Mart. https://agentmart.dev/skills/machinelearning-nerd-interrogate (last updated 2026-10-24)