Borrowing it
Nothing to install: this file belongs to AlexanderMattTurner/agent-glovebox. 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/AlexanderMattTurner/agent-glovebox/main/.claude/skills/vuln-research/SKILL.mdgit clone --depth 1 https://github.com/AlexanderMattTurner/agent-gloveboxWrote 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/alexandermattturner/agent-glovebox/vuln-research)<a href="https://agentmods.dev/skills/alexandermattturner/agent-glovebox/vuln-research"><img src="https://agentmods.dev/badge/skills/alexandermattturner/agent-glovebox/vuln-research/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/alexandermattturner/agent-glovebox/vuln-research"><img src="https://agentmods.dev/badge/skills/alexandermattturner/agent-glovebox/vuln-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 52 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00204 | $0.03530 |
| Opus 5.5 | $0.00082 | $0.01412 |
| Sonnet 5.5 | $0.00041 | $0.00706 |
| Haiku 4.5 | $0.00020 | $0.00353 |
Grade A, and why
vuln-research scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
This is the rule the whole activity hangs off, because the economics run against you. Generating a plausible vulnerability narrative is cheap and verifying one is expensive, so an unverified report moves your cost onto w How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vulnerability research
The load-bearing rule
A finding you cannot reproduce is not a finding. It is a hypothesis, and it stays one until a command you can name turns it into evidence. Report the hypothesis as a hypothesis or drop it — never as a vulnerability.
This is the rule the whole activity hangs off, because the economics run against you. Generating a plausible vulnerability narrative is cheap and verifying one is expensive, so an unverified report moves your cost onto whoever reads it. The curl project watched its confirmed-vulnerability rate fall from over 15% to under 5% as unverified AI reports arrived, and closed a seven-year bug bounty on 31 January 2026 rather than keep paying that tax. The recovery is the instructive half: by April 2026 the slop had stopped and the AI-assisted reports that replaced it were mostly valid, because reporters started verifying before sending.
Scope this skill holds
This skill fires when the deliverable is a claim about real software being exploitable. That is a different job from the reviews this tree already has, so pick the right one and do not run two:
- A pending diff before a PR →
peer-review. - A single-pass look at the current branch → the built-in
security-review. - A whole-codebase multi-axis audit ending in a fix plan →
parallel-audit. - Proving an isolation layer still engages, with tests →
stress-test. - A vulnerability claim you intend to act on or send somewhere → this skill.
Deliver the finding set you were asked for. Do not widen a bounded question ("is this one path exploitable?") into a full audit.
Hunting with /goal
Use /goal for an open-ended hunt on one named surface. A bounded question gets no goal. In Codex, set the goal only when the user asks for one. After each turn a small model judges the condition from the transcript alone. So write the condition as the evidence the judge must SEE, never as "a vulnerability was found".
- State the outcome, not the method. Ask for exactly one finding that breaks a quoted claim of the target's threat model (this tree's is SECURITY.md), at evidence level 1 of §2. Name a method only when you want that method ("use Hypothesis").
- List what does not count: SECURITY.md's out-of-scope list and § Known limitations, a finding a §3 tool already reports, evidence below level 1, and any edit to an existing test, hook, lint, deny rule or allowlist entry.
- Make "nothing yet" not met, except at the cap. The judge reads only the condition, so the cap is a second met-clause: the transcript shows the turn count and §7's clean-run report.
- Run an unattended goal inside a glovebox, never on the host.
- Aim at what is ASSUMED, not at what is proved. TLC already checks the invariants under
docs/tla/. Hunt in the constants a config hand-sets to stand for shipped code, in a clause a model assumes (Consent.tla'sMintedLabels = FALSE), and in the uncheckedA-rows ofdocs/merge-queue-fsm.md§7.
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.
- 10d ago Changed 242612423448
- 14d ago Changed 51abfe53f8ad
- 21d ago Changed 5ba98c966838
- 26d ago Changed · +27 lines · +7 tokens per session 1d25e2437f04
- 1mo ago Changed 34b9a1b3b858
- 1mo ago Changed 22291d3df684
- 1mo ago Changed e6bbfe9ad294
- 1mo ago First seen · 120 lines · 197 tokens per session scan A 2bf9f45b2168
vuln-research is a skill published in the GitHub repository AlexanderMattTurner/agent-glovebox (68 stars, last pushed today), licensed Apache-2.0. It adds 204 tokens to every session and 3,530 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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