codeql
by trailofbitsThe codeql skill helps you run CodeQL with fewer blind spots during a security audit. It focuses on database quality, suite selection, data extensions, and SARIF review so you can use codeql usage more reliably across supported languages. Use it for repeatable codeql guide steps when analyzing real repositories.
This skill scores 84/100, which means it is a solid listing candidate for Agent Skills Finder. It gives directory users a credible install decision: the workflow is real, the supported languages and scan modes are explicit, and the references provide practical analysis/build guidance that reduces guesswork compared with a generic prompt.
- Strong triggerability: SKILL.md names exact triggers like "run codeql", "codeql scan", and "build codeql database" plus concrete supported actions.
- Good operational depth: the repo includes workflow docs for building databases, running analysis, and creating data extensions, along with references for SARIF processing and query suite handling.
- High agent leverage: it documents important execution rules such as database quality checks, suite selection, and language-specific extraction guidance across multiple languages.
- No install command in SKILL.md, so users must infer setup/integration steps from repository structure rather than a turnkey install flow.
- The description field is very short and the skill is documentation-heavy, so first-time users may still need to read several reference files to choose the right workflow.
Overview of codeql skill
What codeql does
The codeql skill helps you run CodeQL with fewer blind spots when you are doing a security audit. It is designed for users who need to build a reliable database, run the right analysis suite, and interpret SARIF output without missing project-specific flows.
Who this is for
Use this codeql skill if you are auditing a real repository, not just experimenting with a single prompt. It fits security engineers, appsec reviewers, and agents that need repeatable codeql usage on Python, JavaScript/TypeScript, Go, Java/Kotlin, C/C++, C#, Ruby, or Swift projects.
What makes it different
The main value is operational: database quality checks, suite selection, and data extensions are treated as required steps, not optional extras. That matters because a working build is not automatically a useful CodeQL database, and generic prompts often miss custom wrappers, framework edges, or query-suite pitfalls.
How to Use codeql skill
Install and trigger it
Install the codeql skill in the trailofbits/skills bundle, then invoke it with a task statement that names the target repo, language, and desired outcome. Example: “Run the codeql skill on this service to find auth and injection issues, then report only high-confidence findings.”
Give the skill the right input
Strong inputs describe the codebase and analysis goal, not just “scan this repo.” Include:
- language or stack
- build system and package manager
- target path if the repo is large or monorepo-based
- whether you want
run allorimportant only - any custom framework wrappers, RPC layers, or job runners that may need data extensions
Follow the repository reading path
Start with SKILL.md, then read references/quality-assessment.md, references/important-only-suite.md, references/diagnostic-query-templates.md, and workflows/build-database.md. If the project has custom data flow, also inspect workflows/create-data-extensions.md and references/extension-yaml-format.md before analyzing.
Use the workflow in practice
For codeql install and codeql usage, the critical sequence is: build a good database, assess extraction quality, choose the suite, then analyze and review SARIF. If results look sparse, do not immediately rerun queries; first verify source coverage, extractor errors, and whether the repo needs data extensions for application-specific sources or sinks.
codeql skill FAQ
Is codeql only useful for security audit work?
No. The codeql skill is strongest for codeql for Security Audit, but it also helps with regression-style code review, framework-specific flow tracing, and triage of suspected taint paths. If you need quick syntax linting, this is the wrong tool.
Do I need to know CodeQL before using it?
No, but you do need enough context to describe the target application and build process. The skill guides codeql guide steps, yet a vague prompt will still produce weaker analysis because database quality and suite choice depend on the repo.
When should I avoid using codeql?
Avoid it when the project cannot be built or extracted at all, when you only need a shallow static scan, or when the bug class is outside CodeQL’s flow-analysis strengths. It is also a poor fit if you cannot provide a stable source root or build command.
How is this different from a normal prompt to “scan the repo”?
A normal prompt often jumps straight to findings. This codeql skill is more installation-oriented: it emphasizes the database, the suite, the data model, and the SARIF review path so you get fewer false negatives and less guesswork.
How to Improve codeql skill
Improve the prompt with build and scope detail
Better inputs produce better codeql usage. Say what to analyze, what to exclude, and how to build it. For example: “Analyze services/api only, use npm ci, skip generated files, and focus on command injection and unsafe deserialization.” That is much stronger than “find vulnerabilities.”
Watch for the common failure modes
The most common misses are bad extraction, missing dependencies, and overly narrow modeling. If findings look thin, check whether the database actually covers the source files you care about, whether build logs show extractor errors, and whether custom wrapper functions need data extensions.
Iterate after the first run
Use the first result set to decide whether to expand coverage or tighten precision. If you need deeper coverage, add data extensions and rerun. If you need fewer noisy results, prefer the important only path and inspect the suite logic before changing queries.
Tune output quality for codeql for Security Audit
For audit work, ask for the likely entry points, the sink classes, and the path explanation rather than just a vulnerability list. That encourages the skill to surface traceable evidence, not just query hits, and makes the final review easier to validate.