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codeql

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".

Install / Use

npx skills add trailofbits/skills --skill codeql

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

96/100

Category

Security

Supported Platforms

Universal

Our assessment of codeql

codeql scores 96/100 on our quality scale, 140th of 774 Security skills we index (top 19%).

Its SKILL.md is 18 KB long, well organised into 28 sections with 5 code examples: a thorough specification that gives an agent plenty to work with.

With 7,225 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
30/30
Structure
20/20
Description
15/15
Adoption
16/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 4 days ago, so codeql is actively maintained.
  • It is released under the CC-BY-SA-4.0 license; check its terms before commercial use.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-09-28. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

codeql compared with similar skills

All 4 of these similar skills score higher than codeql; compare them before choosing.

SkillScoreStarsUpdatedFormat
codeql (this skill)by trailofbits967.2k4d agoSKILL.md
Agent-Reachby Panniantong10085.8k12d agoCLAUDE.md
headroomby headroomlabs-ai10074.0k1d agoCLAUDE.md
rufloby ruvnet10073.4ktodayCLAUDE.md
crawl4aiby unclecode10084.4k3d agoMCP Server

Frequently asked questions

How do I install codeql?
Run npx skills add trailofbits/skills --skill codeql. The install tabs above show the steps for each supported agent.
Which AI agents does codeql work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is codeql safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is CC-BY-SA-4.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is codeql still maintained?
The repository was last updated 4 days ago, so codeql is actively maintained.

name: codeql description: >- 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. allowed-tools: Bash Read Write Edit Glob Grep AskUserQuestion TaskCreate TaskList TaskUpdate TaskGet

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

  1. Database quality is non-negotiable. A database that builds is not automatically good — a cached build extracts nothing while reporting success.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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 rulese

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars7.2k
CategorySecurity
Updated4d ago
Forks615

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions