Codeql

Trail of BitsOfficialPublished by Trail of Bits

Skill
Workflow
Security
CC-BY-SA-4.0
Low risk
Has scripts

From the author

Scans a codebase for security vulnerabilities using CodeQL's interprocedural data flow and taint tracking analysis. Triggers on "run codeql", "codeql scan", "build codeql database", "SAST scan", "taint analysis", "dataflow analysis", or "find vulnerabilities in this repo". Covers Python, JavaScript/TypeScript, Go, Java/Kotlin, C/C++, C#, Ruby, and Swift. Supports "run all" (security-and-quality + security-experimental) and "important only" (high-precision) scan modes, and creates data extension models for project-specific sources and sinks. For fast single-file pattern matching, or when no build is available for a compiled language, use the semgrep skill; to parse SARIF that already exists rather than produce it, use the sarif-parsing skill.

— the description by Trail of Bits

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured, multi-phase workflow for performing static application security testing (SAST) using CodeQL. It manages the entire lifecycle of a security scan, including database construction, data extension modeling for custom project APIs, and execution of high-precision or comprehensive query suites. The skill enforces rigorous quality gates, such as database validation and explicit suite verification, to ensure that findings are accurate and that the absence of findings is a result of secure code rather than extraction or configuration errors.

What it does

Builds CodeQL databases for multiple languages including Python, JS/TS, Go, Java, C/C++, C#, Ruby, and Swift.
Automates the creation and application of data extension models to cover project-specific sources and sinks.
Executes security scans using either high-precision or comprehensive query suites.
Validates database quality and query suite resolution to prevent silent analysis failures.
Generates and manages SARIF-formatted security reports within a structured output directory.
Provides a robust fallback mechanism for build failures, including dependency installation and environment-specific workarounds.

When to use it

When you need to perform deep, interprocedural security analysis on a codebase.
When you want to identify vulnerabilities that require taint tracking across complex application logic.
When you need to audit custom frameworks or APIs that standard scanners might miss.
When you require a repeatable, documented security scan process with verified results.

Advantages

Reduces false negatives by enforcing data extension modeling for custom code.
Ensures analysis reliability through mandatory database quality checks.
Prevents configuration drift by using explicit query suite references.
Provides clear audit trails for security findings and scan configurations.

Usage examples

“run codeql scan on this repository”: The agent will discover the codebase language, build a CodeQL database, apply necessary data extensions, run the security analysis, and output a SARIF report.
“find vulnerabilities in this repo”: The agent will initiate the full CodeQL pipeline, including database discovery, quality assessment, and vulnerability analysis.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/trailofbits/codeql/download
Unzip it into the cross-client skills folder of your project:
```
mkdir -p .agents/skills && unzip codeql.zip -d .agents/skills
```

Documentation by Trail of Bits

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 — a cached build extracts nothing while reporting success.
Data extensions catch what CodeQL misses. Django, Spring, and Express projects still wrap database calls, request parsing, and shell execution in project-specific APIs that no shipped model covers.
Explicit suite references prevent silent query dropping. Never pass pack names to codeql database analyze — each pack's defaultSuiteFile applies hidden filters that can produce zero results. Always generate a .qls.
Zero findings needs investigation, not celebration. It can mean poor extraction, missing models, the wrong packs, or suite filtering. Run {baseDir}/scripts/check_db_quality.py after the build, confirm {baseDir}/scripts/verify_query_suite.py exited zero for the suite in use — the generation scripts run it, so invoke it by hand only for a reused or hand-edited suite — and say in the report that both passed.
macOS Apple Silicon requires workarounds for compiled languages. Exit code 137 is an 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. Each phase gates the next; skipping quality assessment or data extensions leaves the gap invisible in the results.

Each Bash call is a fresh shell

Nothing carries across a Bash call: not variables, not arrays, not functions sourced from build_log.sh. Every block below that uses a value must re-establish it in the same block. The workflows point back here rather than repeating it; what they do state is the specific damage at that site, because each one fails differently and silently:

a lost function makes run_logged exit 127, which the build ladder reads as a failed method and walks down to --build-mode=none, never having invoked CodeQL
a lost array expands to nothing, so every --threat-model and --model-packs the user chose is dropped while the final report still lists them as used
a lost scalar under set -u aborts the block with unbound variable

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.

In both cases, always create the directory with mkdir -p before writing any files.

Set USER_SPECIFIED_DIR to the literal path from the user's prompt before running this, or leave it unset to auto-increment. Nothing else assigns it.

# Resolve output directory
USER_SPECIFIED_DIR="${USER_SPECIFIED_DIR:-}"   # substitute the user's path here, if any
if [ -n "$USER_SPECIFIED_DIR" ]; then
  OUTPUT_DIR="$USER_SPECIFIED_DIR"
else
  BASE="static_analysis_codeql"
  N=1
  while [ -e "${BASE}_${N}" ]; do
    N=$((N + 1))
  done
  OUTPUT_DIR="${BASE}_${N}"
fi
mkdir -p "$OUTPUT_DIR"

The output directory is resolved once at the start before any workflow executes. All workflows receive $OUTPUT_DIR and store their artifacts there:

$OUTPUT_DIR/
├── rulesets.txt                 # Selected query packs (logged after Step 3)
├── codeql.db/                   # CodeQL database (dir containing codeql-database.yml)
├── build.log                    # Build log
├── codeql-config.yml            # Exclusion config (interpreted languages)
├── diagnostics/                 # Diagnostic queries and CSVs
├── extensions/                  # Data extension YAMLs
├── raw/                         # Unfiltered analysis output
│   ├── results.sarif
│   └── run-all.qls | important-only.qls
└── results/                     # Final results (filtered for important-only, copied for run-all)
    └── results.sarif

Database Discovery

A CodeQL database is identified by the presence of a codeql-database.yml marker file inside its directory. When searching for existing databases, always collect all matches — there may be multiple databases from previous runs or for different languages.

Discovery command. find_databases.sh prints one database path per line, filtering out the marker files a failed build leaves behind. Build the array in the same block that selects from it — each Bash call is a fresh shell, so an array built here is empty by the next call, and the run concludes there is no database:

# Command substitution, not `done < <(...)`: a process substitution discards the script's
# exit status, so "codeql is not on this shell's PATH" (exit 2) would arrive as an empty
# list and route to "build a new database" with three good ones sitting on disk.
if ! DB_LIST=$("{baseDir}/scripts/find_databases.sh" "${OUTPUT_DIR:-.}" .); then
  echo "ERROR: database discovery failed — see the message above" >&2
  exit 1
fi

FOUND_DBS=()
while IFS= read -r db; do
  [ -n "$db" ] || continue
  FOUND_DBS+=("$db")
done <<<"$DB_LIST"

echo "Found ${#FOUND_DBS[@]} existing database(s)"

# The metadata the selection prompt needs, collected here rather than in a block of its
# own: FOUND_DBS is gone by the next Bash call, and a loop over an array that no longer
# exists prints nothing and reports success.
for db in "${FOUND_DBS[@]}"; do
  CODEQL_LANG=$(codeql resolve database --format=json -- "$db" 2>/dev/null | jq -r '.languages[0]')
  CREATED=$(grep '^creationMetadata:' -A5 "$db/codeql-database.yml" 2>/dev/null | grep 'creationTime' | awk '{print $2}')
  echo "$db — language: $CODEQL_LANG, created: $CREATED"
done

Never assume a database is named codeql.db — discover it by its marker file.

When multiple databases are found: use AskUserQuestion to let the user select which database to use, or to build a new one, from the language and creation time printed above. AskUserQuestion takes at most four options, so with more databases than that, offer the three most recent plus "Build a new database" and list the rest in the prompt text. Skip AskUserQuestion if the user explicitly stated which database to use or to build a new one in their prompt.

Quick Start

For the common case ("scan this codebase for vulnerabilities"):

# Verify CodeQL is installed. Stop here if it is not — every later command fails with
# a less informative error, and the run wastes a build cycle before saying why.
if ! command -v codeql >/dev/null 2>&1; then
  echo "ERROR: codeql not found on PATH. Install it with one of:" >&2
  echo "  gh extension install github/gh-codeql   # then: gh codeql install-stub" >&2
  echo "  brew install --cask codeql" >&2
  echo "  https://github.com/github/codeql-action/releases  (codeql-bundle)" >&2
  exit 1
fi

# jq parses `codeql resolve database --format=json` in the very next step. Without it
# CODEQL_LANG comes back empty and the run continues against the wrong language.
if ! command -v jq >/dev/null 2>&1; then
  echo "ERROR: jq not found on PATH (brew install jq / apt install jq)" >&2
  exit 1
fi

# uv runs both guard scripts and both suite generators. Check it here rather than at
# suite generation, which is after the build — otherwise a machine without uv spends
# the whole build before failing.
if ! command -v uv >/dev/null 2>&1; then
  echo "ERROR: uv not found on PATH (https://docs.astral.sh/uv/getting-started/)" >&2
  exit 1
fi

codeql --version

Then resolve OUTPUT_DIR using the block in Output Directory above — it honours a user-specified directory, which a bare auto-increment does not.

Then execute the full pipeline: build database → create data extensions → run analysis using the workflows below.

Rationalizations to Reject

These shortcuts lead to missed findings. Do not accept them:

"security-extended is enough" - It is the baseline. Always check if Trail of Bits packs and Community Packs are available for the language. They catch categories security-extended misses entirely.
"security-and-quality is the broadest suite" - security-and-quality excludes all experimental/ query paths. For run-all mode, import both security-and-quality and security-experimental. The delta is 1–52 queries depending on the language.
"The database built, so it's good" - A database that builds does not mean it extracted well. Always run quality assessment and check file counts against expected source files.
"Data extensions aren't needed for standard frameworks" - Even Django/Spring apps have custom wrappers that CodeQL does not model. Skipping extensions means missing vulnerabilities.
"build-mode=none is fine for compiled languages" - It produces severely incomplete analysis. Only use as an absolute last resort. On macOS, try the arm64 toolchain workaround or Rosetta first.
"The build fails on macOS, just use build-mode=none" - Exit code 137 is caused by arm64e/arm64 mismatch, not a fundamental build failure. See macos-arm64e-workaround.md.
"No findings means the code is secure" - Run check_db_quality.py and verify_query_suite.py and report that they passed. Without them, zero findings and a database that extracted nothing are the same output.
"I'll just run the default suite" / "I'll just pass the pack names directly" - Each pack's defaultSuiteFile applies hidden filters and can produce zero results. Always use an explicit suite reference.
"I'll put files in the current directory" - All generated files must go in $OUTPUT_DIR. Scattering files in the working directory makes cleanup impossible and risks overwriting previous runs.
"Just use the first database I find" - Multiple databases may exist for different languages or from previous runs. When more than one is found, present all options to the user. Only skip the prompt when the user already specified which database to use.
"The user said 'scan', that means they want me to pick a database" - "Scan" is not database selection. If multiple databases exist and the user didn't name one, ask.

Workflow Selection

This skill has three workflows. Once a workflow is selected, execute it step by step without skipping phases.

These runs are long. A database build has four fallback methods, so use the task tools to track progress. Decide which steps are worth tracking based on the run.

Workflow	Purpose
build-database	Create CodeQL database using build methods in sequence
create-data-extensions	Detect or generate data extension models for project APIs
run-analysis	Select rulesets, execute queries, process results

Building unattended

This plugin ships /static-analysis:codeql-build, which runs the build-database steps end to end: detect the language and toolchain, walk the method ladder applying fixes from build-fixes.md between rungs, and enforce the quality gate.

/static-analysis:codeql-build {"target": "/abs/path", "lang": "cpp"}

It asks nothing. Every method failing, and a database that built but sits below the quality threshold, come back as statuses — no-method-succeeded and built-below-threshold — for you to act on here, because whether the remaining extractor errors are confined to code nobody needs analysed is a judgement call the run cannot make.

Use it when the build is the long, uncertain part and you want it driven to a conclusion. Work build-database.md by hand when you want a say in which method is tried, or when a build failure needs interpreting as it happens.

Auto-Detection Logic

If user explicitly specifies what to do (e.g., "build a database", "run analysis on ./my-db"), execute that workflow directly. Do NOT call AskUserQuestion for database selection if the user's prompt already makes their intent clear — e.g., "build a new database", "analyze the codeql database in static_analysis_codeql_2", "run a full scan from scratch".

Default pipeline for "test", "scan", "analyze", or similar: Discover existing databases using the command in Database Discovery above, then decide.

Condition	Action
No databases found	Resolve new `$OUTPUT_DIR`, execute build → extensions → analysis (full pipeline)
One database found	Use `AskUserQuestion`: reuse it or build new?
Multiple databases found	Use `AskUserQuestion`, capped at four options — see Database Discovery
User explicitly stated intent	Skip `AskUserQuestion`, act on their instructions directly

Database Selection Prompt

When existing databases are found and the user did not explicitly specify which to use, present them via AskUserQuestion under the header "Existing CodeQL Databases". Label each option with the path, language, and creation time collected above — ./static_analysis_codeql_1/codeql.db (language: python, created: 2026-02-24) — and make the last option "Build a new database".

After selection:

If user picks an existing database: Set $OUTPUT_DIR to its parent directory (or the directory containing it), set $DB_NAME to the selected path, then proceed to extensions → analysis.
If user picks "Build new": Resolve a new $OUTPUT_DIR, execute build → extensions → analysis.

General Decision Prompt

If neither the database nor the workflow is clear from the prompt, offer the four workflows via AskUserQuestion — full scan (recommended), build database, create data extensions, run analysis — naming any databases found and the resolved $OUTPUT_DIR.

Reference Index

File	Content
Scripts
scripts/verify_query_suite.py	Fails a suite that resolves to zero queries. The generation scripts run it; invoke by hand only for a reused or hand-edited suite
scripts/check_db_quality.py	Fails a database with no analysable source. Run after every build
scripts/build_log.sh	`log_step`/`run_logged` helpers; source before any build step
scripts/find_databases.sh	Prints every database that `codeql resolve database` accepts, one per line. Build your array from it in the block that reads it
scripts/generate_suite.sh	Writes the run-all or important-only `.qls` and verifies it resolves to a non-zero query count
References — the three workflows are listed under Workflow Selection
references/macos-arm64e-workaround.md	Apple Silicon build tracing workarounds
references/build-fixes.md	Build failure fix catalog
references/quality-assessment.md	Database quality metrics and improvements
references/extension-yaml-format.md	Data extension YAML column definitions and examples
references/sarif-processing.md	jq commands for SARIF output processing
references/diagnostic-query-templates.md	QL queries for source/sink enumeration
references/important-only-suite.md	Important-only suite template and generation
references/run-all-suite.md	Run-all suite template
references/ruleset-catalog.md	Available query packs by language
references/threat-models.md	Threat model configuration
references/language-details.md	Language-specific build and extraction details
references/performance-tuning.md	Memory, threading, and timeout configuration

Success Criteria

A complete CodeQL analysis run should satisfy:

[ ] Output directory resolved (user-specified or auto-incremented default)
[ ] All generated files stored inside $OUTPUT_DIR
[ ] Database built (discovered via codeql-database.yml marker) and {baseDir}/scripts/check_db_quality.py exited zero
[ ] Data extensions evaluated — either created in $OUTPUT_DIR/extensions/ or explicitly skipped with justification
[ ] Analysis run with explicit suite reference (not default pack suite), and {baseDir}/scripts/verify_query_suite.py exited zero for it
[ ] All installed query packs (official + Trail of Bits + Community) used or explicitly excluded
[ ] Selected query packs logged to $OUTPUT_DIR/rulesets.txt
[ ] Unfiltered results preserved in $OUTPUT_DIR/raw/results.sarif
[ ] Final results in $OUTPUT_DIR/results/results.sarif (filtered for important-only, copied for run-all)
[ ] Zero-finding results investigated (database quality, model coverage, suite selection)
[ ] Build log preserved at $OUTPUT_DIR/build.log with all commands, fixes, and quality assessments

Files

ATTRIBUTION.mdAdded by AutoGineer921 B
LICENSELicence file added by AutoGineer19.7 KiB
SKILL.md18.2 KiB
agents/openai.yaml239 B
assets/trail-of-bits-mark.svg3.0 KiB
references/build-fixes.md3.2 KiB
references/diagnostic-query-templates.md9.5 KiB
references/extension-yaml-format.md7.4 KiB
references/important-only-suite.md5.1 KiB
references/language-details.md5.7 KiB
references/macos-arm64e-workaround.md7.3 KiB
references/performance-tuning.md2.8 KiB
references/quality-assessment.md7.8 KiB
references/ruleset-catalog.md2.1 KiB
references/run-all-suite.md5.4 KiB
references/sarif-processing.md3.0 KiB
references/threat-models.md2.7 KiB
scripts/build_log.shScript2.1 KiB
scripts/check_db_quality.pyScript14.7 KiB
scripts/find_databases.shScript2.5 KiB
scripts/generate_suite.shScript3.3 KiB
scripts/pyproject.toml585 B
scripts/test_build_log.pyScript3.2 KiB
scripts/test_build_mode_claims.pyScript3.6 KiB
scripts/test_check_db_quality.pyScript19.8 KiB
scripts/test_find_databases.pyScript6.0 KiB
scripts/test_generation_scripts.pyScript11.6 KiB
scripts/test_script_flags.pyScript6.3 KiB
scripts/test_section_pointers.pyScript7.9 KiB
scripts/test_shell_blocks.pyScript29.1 KiB
scripts/test_suite_templates.pyScript5.0 KiB
scripts/test_verify_query_suite.pyScript1.9 KiB
scripts/verify_query_suite.pyScript2.7 KiB
workflows/build-database.md13.0 KiB
workflows/create-data-extensions.md10.3 KiB
workflows/run-analysis.md13.3 KiB

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Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Names Claude Code tools, Needs Python

Security

What our scanner noticed· Low risk

Instructions that use the network
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This skill includes scripts that your agent may run. Read them before you use it.

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Attribution and licence

Published by

Trail of Bits

Publisher

Trail of Bits (Official)

Source

trailofbits/skills · plugins/static-analysis/skills/codeql · commit e4dd0bb

Licence

Creative Commons Attribution-ShareAlike 4.0 International (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Trail of Bits.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download codeql.zip