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.
npx skills add chhoumann/t3-pstack --skill interrogategit clone --depth 1 https://github.com/chhoumann/t3-pstackWrote 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/chhoumann/t3-pstack/interrogate)<a href="https://agentmods.dev/skills/chhoumann/t3-pstack/interrogate"><img src="https://agentmods.dev/badge/skills/chhoumann/t3-pstack/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/chhoumann/t3-pstack/interrogate"><img src="https://agentmods.dev/badge/skills/chhoumann/t3-pstack/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 | $0.01363 |
| Opus 5.5 | $0.00020 | $0.00545 |
| Sonnet 5.5 | $0.00010 | $0.00273 |
| Haiku 4.5 | $0.00005 | $0.00136 |
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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 82 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. 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 response with delegate_task, one per entry on the interrogate reviewers line in the model sheet, labeled Reviewer A, B, and so on to match the entry count. If the sheet or that line is missing, use the table defaults.
| Subagent | Default model |
|---|---|
| Reviewer A | claudeAgent/claude-opus-5-5 effort=xhigh |
| Reviewer B | codex/gpt-6.1-sol reasoningEffort=xhigh |
| Reviewer C | grok/grok-4.7 reasoningEffort=xhigh |
Each reviewer gets role: "review", a brief that says to inspect only and change no file, and target set from its configured entry, or from its table default when there is no configured line. Omit target for an auto or inherit-parent entry, so that reviewer runs on the parent model.
If delegate_task rejects a configured entry, run that reviewer on another target of the same family from orchestrator_capabilities and say so. A model's family is the first segment of its ID's last / part, such as claude, gpt, or grok, whatever provider instance serves it. With no family match, use Reviewer A's default. If it rejects a table default, spawn on the closest target of the same family from orchestrator_capabilities (prefer the same reasoning tier), and open a separate PR to update the default table. Do not block the review on the slug issue. Never treat an alias entry as a rejected slug or apply either fallback to it.
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.
- yesterday Changed · -2 lines 0ab56444bcd2
- 3d ago First seen · 84 lines · 51 tokens per session scan A 6cd82d960418
interrogate is a skill published in the GitHub repository chhoumann/t3-pstack (190 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 1,363 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-13.
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