interrogate

A multi-reviewer process that challenges code changes from several independent model perspectives.

In plain words
What is it for?
It is for adversarial reviews of a diff or feature, with reviewers examining the same changes and a final synthesized verdict.
Why use it?
It helps reveal blind spots and disagreements before changes are accepted, without automatically editing the code.

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/ericlitman/open-pstack/interrogate
Any agent
npx skills add ericlitman/open-pstack --skill interrogate
Clone the repo
git clone --depth 1 https://github.com/ericlitman/open-pstack

Made for: Claude Code, Codex.

Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,263 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.01263
Opus 5 $0.00026 $0.00632
Sonnet 5 $0.00010 $0.00253
Haiku 4.5 $0.00005 $0.00126

Measured 2d ago against content hash 07dcab809c4d, 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 2d 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.

plugins/pstack/skills/interrogate/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 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.

Dispatch contract. Read provider-dispatch.md before launching reviewers. Configured entries are provider-qualified descriptors; the parent starts native and external read-only lanes directly. On Codex, resolve remaining Claude tool names via codex-tools.md.

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

Start all reviewers in one fan-out phase. Use interrogate reviewers from the current harness's pstack model sheet when present, 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. Native reviewers use the parent subagent primitive. External reviewers use the launcher directly and must return a complete, model-verified receipt.

Read the full file on GitHub · 110 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. 2d ago First seen · 110 lines · 51 tokens per session scan A 07dcab809c4d

Subscribe to this mod's changes

interrogate is a skill published in the GitHub repository ericlitman/open-pstack (155 stars, last pushed 6d ago), licensed MIT. It adds 51 tokens to every session and 1,263 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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