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 agentmods add skills/painhardcore/pstack/interrogatenpx skills add painhardcore/pstack --skill interrogategit clone --depth 1 https://github.com/painhardcore/pstackWhat 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 | $0.00051 | $0.01093 |
| Opus 5 | $0.00026 | $0.00547 |
| Sonnet 5 | $0.00010 | $0.00219 |
| Haiku 4.5 | $0.00005 | $0.00109 |
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 — 108 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 together using the host's native subagent tool. Inherit the parent model by default. When the host supports model selection, use distinct available model families to increase independent signal.
| Subagent | Default model |
|---|---|
| Reviewer A | Parent model |
| Reviewer B | Different available family, or parent model |
| Reviewer C | Different available family, or parent model |
| Reviewer D | Different available family, or parent model |
Give every reviewer read-only access, the same source scope, and the same filled prompt. If the host rejects a requested model or does not support per-subagent models, inherit the parent model. If subagents are unavailable or forbidden, apply the rubric inline once and state that the result is not multi-model.
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 First seen · 108 lines · 51 tokens per session scan A 3cc1be0a4a7a
interrogate is a skill published in the GitHub repository painhardcore/pstack (1 stars, last pushed 5d ago), licensed MIT. It adds 51 tokens to every session and 1,093 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 2026-08-31.
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