Borrowing it
Nothing to install: this file belongs to snyk-labs/snyk-vulnbench. 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/snyk-labs/snyk-vulnbench/main/.agents/skills/benchmark-add-new-category/SKILL.mdgit clone --depth 1 https://github.com/snyk-labs/snyk-vulnbenchWrote 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/snyk-labs/snyk-vulnbench/benchmark-add-new-category)<a href="https://agentmods.dev/skills/snyk-labs/snyk-vulnbench/benchmark-add-new-category"><img src="https://agentmods.dev/badge/skills/snyk-labs/snyk-vulnbench/benchmark-add-new-category/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/snyk-labs/snyk-vulnbench/benchmark-add-new-category"><img src="https://agentmods.dev/badge/skills/snyk-labs/snyk-vulnbench/benchmark-add-new-category.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 | $0.00147 | $0.02393 |
| Opus 5.5 | $0.00059 | $0.00957 |
| Sonnet 5.5 | $0.00029 | $0.00479 |
| Haiku 4.5 | $0.00015 | $0.00239 |
Grade A, and why
benchmark-add-new-category 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark Add New Category
Instructions
Add a new eval category so a set of tasks can be filtered with --category <id>
and scored with the appropriate pipeline. The outcome is that the new category
appears in dry-run output, tasks using it load and score correctly, and docs
reflect the change.
Categories determine three things: (1) how tasks are grouped for CLI filtering, (2) what system/user prompts the agent receives, and (3) which scoring pipeline runs (F1 find-vulns or LLM-judge fix-vulns). Adding a find-* category reuses the existing F1 scorer; adding a fix-* category reuses the LLM judge scorer.
Step 1: Gather requirements
Confirm with the user:
- Category ID — kebab-case string used in
--categoryflag (e.g.llm-find-vulns). - Category constant name — UPPER_SNAKE for the
EVAL_CATEGORIESkey (e.g.LLM_FIND_VULNS). - Scoring type — does this category use find-vulns scoring (F1 precision/recall) or fix-vulns scoring (LLM judge)? Most new categories are find-vulns variants.
- Which existing tasks should move to this category (task JSON files to update).
- Prompt customization — any special emphasis for the system prompt (e.g. "focus on LLM-specific risks") or whether the default find-vulns/fix-vulns prompt is fine.
Done when: you have the category ID, scoring type, affected tasks, and prompt direction.
Step 2: Add the category to EVAL_CATEGORIES
Edit src/types.ts. Add a new entry to the EVAL_CATEGORIES object before the closing } as const satisfies Record<string, EvalCategory>. Each entry needs:
id— the CLI-facing category ID stringname— human-readable namedescription— one-sentence descriptiondefaultSystemPrompt— the system prompt agents receive (must include theFINDINGS_JSONoutput format block for find-* categories)defaultPrompt— the user message sent to the agent
For find-* categories, copy the FINDINGS_JSON format block from FIND_VULNS verbatim — only customize the introductory text and defaultPrompt. The JSON schema must stay identical so the scorer can parse it.
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.
- 1mo ago First seen · 213 lines · 147 tokens per session scan A 207da88f9734
benchmark-add-new-category is a skill published in the GitHub repository snyk-labs/snyk-vulnbench (4 stars, last pushed 14d ago), licensed Apache-2.0. It adds 147 tokens to every session and 2,393 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.