Download plugins/static-analysis/skills/codeql/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does codeql live?
The publisher keeps it at https://github.com/trailofbits/skills.
Which agents can use codeql?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
>-
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
Skill resources: Reference files and templates are located at and .
{baseDir}/references/
{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 $OUTPUT_DIR and store their artifacts there:
A CodeQL database is identified by the presence of a codeql-database.yml marker file inside its directory. When searching for existing databases, always collect all matches — there may be multiple databases from previous runs or for different languages.
Discovery command.find_databases.sh prints one database path per line, filtering
out the marker files a failed build leaves behind. Build the array in the same block
that selects from it — each Bash call is a fresh shell, so an array built here is empty
by the next call, and the run concludes there is no database:
# Command substitution, not `done < <(...)`: a process substitution discards the script's
# exit status, so "codeql is not on this shell's PATH" (exit 2) would arrive as an empty
# list and route to "build a new database" with three good ones sitting on disk.
if ! DB_LIST=$("{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.
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.
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.
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.
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
How do I install codeql?
Download plugins/static-analysis/skills/codeql/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does codeql live?
The publisher keeps it at https://github.com/trailofbits/skills.
Which agents can use codeql?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.