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/redhatproductsecurity/prodsec-skills/codeqlnpx skills add RedHatProductSecurity/prodsec-skills --skill codeqlgit clone --depth 1 https://github.com/RedHatProductSecurity/prodsec-skillsWhat 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 | $0.00102 | $0.04029 |
| Opus 5 | $0.00051 | $0.02014 |
| Sonnet 5 | $0.00020 | $0.00806 |
| Haiku 4.5 | $0.00010 | $0.00403 |
Grade A, and why
codeql 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.
How it starts
The opening of the file, as written. The whole thing — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CodeQL Analysis
Supported languages: Python, JavaScript/TypeScript, Go, Java/Kotlin, C/C++, C#, Ruby, Swift.
Skill resources: Reference files and templates live alongside the upstream plugin under references/ and workflows/. (see upstream Trail of Bits prodsec-skills for companion files)
Essential Principles
-
Database quality is non-negotiable. A database that builds is not automatically good. Always run quality assessment (file counts, baseline LoC, extractor errors) and compare against expected source files. A cached build produces zero useful extraction.
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Data extensions catch what CodeQL misses. Even projects using standard frameworks (Django, Spring, Express) have custom wrappers around database calls, request parsing, or shell execution. Skipping the create-data-extensions workflow means missing vulnerabilities in project-specific code paths.
-
Explicit suite references prevent silent query dropping. Never pass pack names directly to
codeql database analyze— each pack'sdefaultSuiteFileapplies hidden filters that can produce zero results. Always generate a custom.qlssuite file. -
Zero findings needs investigation, not celebration. Zero results can indicate poor database quality, missing models, wrong query packs, or silent suite filtering. Investigate before reporting clean.
-
macOS Apple Silicon requires workarounds for compiled languages. Exit code 137 is
arm64e/arm64mismatch, not a build failure. Try Homebrew arm64 tools or Rosetta before falling back tobuild-mode=none. -
Follow workflows step by step. Once a workflow is selected, execute it step by step without skipping phases. Each phase gates the next — skipping quality assessment or data extensions leads to incomplete analysis.
Output Directory
All generated files (database, build logs, diagnostics, extensions, results) are stored in a single output directory.
- If the user specifies an output directory in their prompt, use it as
OUTPUT_DIR. - If not specified, default to
./static_analysis_codeql_1. If that already exists, increment to_2,_3, etc.
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.
- 2d ago First seen · 301 lines · 102 tokens per session scan A 3c347ae3cc50
codeql is a skill published in the GitHub repository RedHatProductSecurity/prodsec-skills (52 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 102 tokens to every session and 4,029 once invoked, about $0.0005 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…