codeql
CodeQL Analysis performs static security analysis across Python, JavaScript/TypeScript, Go, Java/Kotlin, C/C++, C#, Ruby, and Swift codebases. Use this skill when you need to identify vulnerabilities, code quality issues, or security weaknesses by building a queryable code database and running security queries, with careful attention to database quality validation, custom data extensions for framework-specific patterns, and explicit query suite configuration.
git clone --depth 1 https://github.com/trailofbits/skills /tmp/codeql && cp -r /tmp/codeql/plugins/static-analysis/skills/codeql ~/.claude/skills/codeqlSKILL.md
# 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.
```bash
# 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:
```
$OUTPUT_DIR/
├── rulesets.txt # Selected query packs (logged after Step 3)
├── codeql.db/ # CodeQL database (dir containing codeql-database.yml)
├── build.log # Build log
├── codeql-config.yml # Exclusion config (interpreted languages)
├── diagnostics/ # Diagnostic queries and CSVs
├── extensions/ # Data extension YAMLs
├── raw/ # Unfiltered analysis output
│ ├── results.sarif
│ └── run-all.qls | important-only.qls
└── results/ # Final results (filtered for important-only, copied for run-all)
└── results.sarif
```
### Database Discovery
A CodeQL database is identified by the presence of a `codeql-database.yml` marker file inside its directory. When searching for existing databases, **always collect all matches** — there may be multiple databases from previous runs or for different languages.
**Discovery command.** `find_databases.sh` prints one database path per line, filtering
out the marker files a failed build leaves behind. Build the array **in the same block
that selects from it** — each Bash call is a fresh shell, so an array built here is empty
by the next call, and the run concludes there is no database:
```bash
# Command substitution, not `done < <(...)`: a process substitution discards the script's
# exit status, so "codeql is not on this shell's PATH" (exit 2) would arrive as an empty
# list and route to "build a new database" with three good ones sitting on disk.
if ! DB_LIST=$("{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` — discoAudits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.
Clarify requirements before implementing. Use when serious doubts arise.
Understand a codebase before looking for bugs in it - what each function assumes, what it guarantees, and what it depends on elsewhere. Use when starting an audit, threat model, or architecture review on unfamiliar code, and before any vulnerability-hunting pass.
Scans Algorand smart contracts for 11 common vulnerabilities including rekeying attacks, unchecked transaction fees, missing field validations, and access control issues. Use when auditing Algorand projects (TEAL/PyTeal).
Prepares codebases for security review using Trail of Bits' checklist. Helps set review goals, runs static analysis tools, increases test coverage, removes dead code, ensures accessibility, and generates documentation (flowcharts, user stories, inline comments). Use when preparing your own codebase to be audited by someone else, getting a repository review-ready before an external security review, deciding what to fix before auditors start, or asking what assessors need from a project. For understanding unfamiliar code you are about to audit, use audit-context-building instead.
Scans Cairo/StarkNet smart contracts for 6 critical vulnerabilities including felt252 arithmetic overflow, L1-L2 messaging issues, address conversion problems, and signature replay. Use when auditing StarkNet projects.
Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing, then produces a scorecard with evidence-based ratings and a priority-ordered roadmap. Use when assessing or scoring the maturity of a smart contract or blockchain codebase, producing a maturity scorecard or evaluation, or judging how mature, well-tested, or well-documented such a project is against a rubric.
Scans Cosmos SDK blockchain modules and CosmWasm contracts for consensus-critical vulnerabilities — chain halts, fund loss, state divergence. 25 core + 16 IBC + 10 EVM + 3 CosmWasm patterns. Use when auditing custom x/ modules, reviewing IBC integrations, or assessing pre-launch chain security. Updated for SDK v0.53.x.