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. Always run quality assessment (file counts, baseline LoC, extractor errors) and compare against expected source files. A cached build produces zero useful extraction.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

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

# Resolve output directory
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
│   └── <mode>.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 ALL CodeQL databases (top-level and one subdirectory deep)
find . -maxdepth 3 -name "codeql-database.yml" -not -path "*/\.*" 2>/dev/null \
  | while read -r yml; do dirname "$yml"; done

• Inside $OUTPUT_DIR: find "$OUTPUT_DIR" -maxdepth 2 -name "codeql-database.yml"
• Project-wide (for auto-detection): find . -maxdepth 3 -name "codeql-database.yml" — covers databases at the project top level (./db-name/) and one subdirectory deep (./subdir/db-name/). Does not search deeper.

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

When multiple databases are found:

For each discovered database, collect metadata to help the user choose:

# For each database, extract language and creation time
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

Then use AskUserQuestion to let the user select which database to use, or to build a new one. 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"):

# 1. Verify CodeQL is installed
if ! command -v codeql >/dev/null 2>&1; then
  echo "NOT INSTALLED: codeql binary not found on PATH"
else
  codeql --version || echo "ERROR: codeql found but --version failed (check installation)"
fi

# 2. Resolve output directory
BASE="static_analysis_codeql"; N=1
while [ -e "${BASE}_${N}" ]; do N=$((N + 1)); done
OUTPUT_DIR="${BASE}_${N}"; mkdir -p "$OUTPUT_DIR"

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

When to Use

• Scanning a codebase for security vulnerabilities with deep data flow analysis
• Building a CodeQL database from source code (with build capability for compiled languages)
• Finding complex vulnerabilities that require interprocedural taint tracking or AST/CFG analysis
• Performing comprehensive security audits with multiple query packs

When NOT to Use

• Writing custom queries - Use a dedicated query development skill
• CI/CD integration - Use GitHub Actions documentation directly
• Quick pattern searches - Use Semgrep or grep for speed
• No build capability for compiled languages - Consider Semgrep instead
• Single-file or lightweight analysis - Semgrep is faster for simple pattern matching

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" - Zero findings can indicate poor database quality, missing models, or wrong query packs. Investigate before reporting clean results.
• "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.

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 first, then decide.

# Find ALL CodeQL databases by looking for codeql-database.yml marker file
# Search top-level dirs and one subdirectory deep
FOUND_DBS=()
while IFS= read -r yml; do
  db_dir=$(dirname "$yml")
  codeql resolve database -- "$db_dir" >/dev/null 2>&1 && FOUND_DBS+=("$db_dir")
done < <(find . -maxdepth 3 -name "codeql-database.yml" -not -path "*/\.*" 2>/dev/null)

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

Database Selection Prompt

When existing databases are found and the user did not explicitly specify which to use, present via AskUserQuestion:

header: "Existing CodeQL Databases"
question: "I found existing CodeQL database(s). What would you like to do?"
options:
  - label: "<db_path_1> (language: python, created: 2026-02-24)"
    description: "Reuse this database"
  - label: "<db_path_2> (language: cpp, created: 2026-02-23)"
    description: "Reuse this database"
  - label: "Build a new database"
    description: "Create a fresh database in a new output directory"

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 the user's intent is ambiguous (neither database selection nor workflow is clear), ask:

I can help with CodeQL analysis. What would you like to do?

1. **Full scan (Recommended)** - Build database, create extensions, then run analysis
2. **Build database** - Create a new CodeQL database from this codebase
3. **Create data extensions** - Generate custom source/sink models for project APIs
4. **Run analysis** - Run security queries on existing database

[If databases found: "I found N existing database(s): <list paths with language>"]
[Show output directory: "Output will be stored in <OUTPUT_DIR>"]

Reference Index

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) with quality assessment passed (baseline LoC > 0, errors < 5%)
• Data extensions evaluated — either created in $OUTPUT_DIR/extensions/ or explicitly skipped with justification
• Analysis run with explicit suite reference (not default pack suite)
• 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