A security scanner that builds a searchable model of a codebase and follows how data moves through functions to find possible vulnerabilities. CodeQL supports languages including Python, JavaScript, Go, Java, C/C++, C#, Ruby, and Swift.

Use it to run security or quality scans, assess the scan database, add project-specific data-flow definitions, and investigate both findings and unexpectedly empty results.

It can trace indirect paths through application code, but its results depend on building a complete and accurate database of the source.

codeql — Skill by vigolium

Install

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.

agentmods

npx agentmods add skills/vigolium/piolium/codeql

Any agent

npx skills add vigolium/piolium --skill codeql

Clone the repo

git clone --depth 1 https://github.com/vigolium/piolium

Made for: Claude Code, Codex.

Per session 98 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,553 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin 92% copy Near-identical to another mod in the catalogue.

Token cost

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	$0.00098	$0.03553
Opus 5	$0.00049	$0.01776
Sonnet 5	$0.00020	$0.00711
Haiku 4.5	$0.00010	$0.00355

Measured 2d ago against content hash a4c5b6f128fc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

Origin

This is a copy

92% identical to codeql — 24 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/codeql/SKILL.md · 282 lines

How it starts

The opening of the file, as written. The whole thing — 282 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 are located at {baseDir}/references/ and {baseDir}/workflows/.

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.
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's defaultSuiteFile applies hidden filters that can produce zero results. Always generate a custom .qls suite 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/arm64 mismatch, not a build failure. Try Homebrew arm64 tools or Rosetta before falling back to build-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.

Read the full file on GitHub · 282 lines

Files

What ships with it

15 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.

Changes

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 · 282 lines · 98 tokens per session scan A a4c5b6f128fc

Subscribe to this mod's changes

codeql is a skill published in the GitHub repository vigolium/piolium (131 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 3,553 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to codeql, differing in 24 lines, and is treated as a copy.

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