interrogate

interrogate is a skill for Claude Code, Codex from Sma1lboy/rove. It costs 51 tokens per session (1,234 once invoked), scanned A, a copy of interrogate, MIT.

A multi-reviewer process for challenging code changes from several independent viewpoints. It compares the reviewers’ findings and produces a combined assessment without changing the code automatically.

In plain words
What is it for?
Use it to stress-test a diff, a pull request, or recent work, assess whether it meets its intended goal, and collect issues for a human to decide on.
Why use it?
It helps reveal blind spots that one reviewer might miss and separates widely agreed concerns from isolated suggestions.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/sma1lboy/rove/interrogate
Any agent
npx skills add Sma1lboy/rove --skill interrogate
Clone the repo
git clone --depth 1 https://github.com/Sma1lboy/rove

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for interrogate

README.md
[![agentmods](https://agentmods.dev/badge/skills/sma1lboy/rove/interrogate.svg)](https://agentmods.dev/skills/sma1lboy/rove/interrogate)
Your own site
<a href="https://agentmods.dev/skills/sma1lboy/rove/interrogate"><img src="https://agentmods.dev/badge/skills/sma1lboy/rove/interrogate.svg" alt="Measured on agentmods" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,234 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00051 $0.01234
Opus 5 $0.00026 $0.00617
Sonnet 5 $0.00010 $0.00247
Haiku 4.5 $0.00005 $0.00123

Measured 4d ago against content hash 903e3e29d3f0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

interrogate scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

89% identical to interrogate — 19 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/pstack/skills/interrogate/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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. Models differ in blind spots, priors, and reasoning patterns. Agreement across models is high-confidence signal; lone-model findings are worth reading but lower confidence.

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. What is this code trying to accomplish? Derive this from:

  • The user's message
  • Commit messages
  • PR description if one exists
  • The code itself

Write one clear paragraph. Reviewers challenge whether the work achieves the intent well, not whether the intent itself is correct. 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. Spawn one reviewer per Reviewer A/B/C/D label below. Vary the model between them where the Agent tool allows it, so the panel is not four copies of one model's blind spots.

Subagent Default model
Reviewer A claude-fable-5-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 Task 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 the full file on GitHub · 116 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 116 lines · 51 tokens per session scan A 903e3e29d3f0

Subscribe to this mod's changes

interrogate is a skill published in the GitHub repository Sma1lboy/rove (115 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 1,234 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to interrogate, differing in 19 lines, and is treated as a copy.

Related

Other skills, from other repositories