Borrowing it
Nothing to install: this file belongs to Sma1lboy/rove. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Sma1lboy/rove/main/.agents/skills/pstack/skills/interrogate/SKILL.mdgit clone --depth 1 https://github.com/Sma1lboy/roveWrote 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.
[](https://agentmods.dev/skills/sma1lboy/rove/interrogate)<a href="https://agentmods.dev/skills/sma1lboy/rove/interrogate"><img src="https://agentmods.dev/badge/skills/sma1lboy/rove/interrogate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sma1lboy/rove/interrogate"><img src="https://agentmods.dev/badge/skills/sma1lboy/rove/interrogate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.01041 |
| Opus 5.5 | $0.00020 | $0.00416 |
| Sonnet 5.5 | $0.00010 | $0.00208 |
| Haiku 4.5 | $0.00005 | $0.00104 |
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 28d 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.
This is a copy
89% identical to interrogate — 92 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.
How it starts
The opening of the file, as written. The whole thing — 102 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 Reviewers A, B, C and D in a single message with the Agent tool, subagent_type: general-purpose. Give them different model values from the ones the Agent tool accepts, so the panel is not four copies of one model's blind spots; with fewer models than reviewers, repeat one. Reviewers only read: say in each prompt that it must not edit files (the Agent tool has no read-only flag).
Read references/reviewer-prompt.md and fill in the template with:
- The stated intent
- The diff or file contents
- The review rubric from
references/rubric.md - 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.
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.
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.
- 28d ago Changed · -14 lines b9f084ce51c6
- 1mo ago First seen · 116 lines · 51 tokens per session scan A 903e3e29d3f0
interrogate is a skill published in the GitHub repository Sma1lboy/rove (178 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 1,041 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to interrogate, differing in 92 lines, and is treated as a copy.
Other skills, from other repositories
pr-standards
Enforce pull request best practices: size limits, description templates, testing requirements, and review workflows. Activate whenever the user creates a PR, asks about PR best practices, sets up PR templates, discusses code review turnaround, or mentions PR size, review requirements, or branch protection rules.
review-checklist
Systematic code review checklist with severity classification. Activate whenever the user asks for a code review, wants feedback on their code, submits a PR for review, or asks what to look for when reviewing code. Also activate when writing review comments or discussing code quality standards.
pr-workflow
Guide pull request creation, review process, CI gates, and merge strategies. Use when the user creates a PR, asks about PR templates, review workflows, CODEOWNERS setup, or merge strategies. Apply when running gh pr create.
spawn-reviewers
Spawn and collect the reviewer fleet at stage20spawnreviewers. Consumes spawn.json.spec (the authoritative spawn spec from derive-spawn-spec / derive-static-spec), resolves CODEINTELALLOWED and CODEINTELREQUIREROOTARG, builds per-agent prompts from the per-agent template + role suffixes (Bug Hunter A/B, Unified…
present-local
Render the operator-facing local-mode code review results at stage29present. Covers BLOCKING/HIGH/MEDIUM section templates, Justified Findings (PLN-721), Dismissed Findings (PLN-722), Verifier Stats footer (PLN-773), operator-flag descriptions, override precedence rule for stage22b, Validation Summary, and final…
verify-findings
Dispatch and collect the finding-verifier fleet at stage23verifyfindings (PLN-722). Reads verifymanifest.json (written by stage22bverifyprepare), spawns one falsify-oriented verifier Task per toverify[] entry with mode-specific Task scheduling (GitHub mode dispatches verifiers synchronously; local mode uses parallel…