여러 프로그래밍 언어에 걸쳐 절차 간 데이터 흐름 및 테인트 트래킹 분석을 사용하여 코드베이스의 보안 취약점을 스캔합니다.
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codeql skill은 무엇을 하나요?
codeql은(는) agent skill입니다. 여러 프로그래밍 언어에 걸쳐 절차 간 데이터 흐름 및 테인트 트래킹 분석을 사용하여 코드베이스의 보안 취약점을 스캔합니다.
skill의 SKILL.md에서 생성된 요약입니다.
codeql skill 정보
보안 연구원과 개발자는 절차 간 데이터 흐름 및 테인트 트래킹 분석을 수행하여 코드베이스의 취약점을 식별할 수 있습니다. 이 SKILL은 Python, JavaScript, TypeScript, Go, Java, Kotlin, C, C++, C#, Ruby, Swift를 포함한 다양한 프로그래밍 언어를 지원합니다. 단순한 패턴 매칭이 아닌, 에이전트가 심층적인 정적 애플리케이션 보안 테스트(SAST)를 수행해야 할 때 로드되도록 설계되었습니다.
프로세스는 CodeQL 데이터베이스 구축, Django 또는 Spring과 같은 프로젝트 전용 API를 위한 데이터 확장 모델 생성, 특정 쿼리 스위트를 사용한 분석 실행을 포함합니다. 사용자는 실험적 쿼리가 포함된 포괄적인 run-all 모드와 높은 정밀도의 결과를 위한 important-only 모드 중 하나를 선택할 수 있습니다. 이 SKILL은 빌드 로그, 진단 및 SARIF 결과를 포함한 모든 아티팩트를 전용 출력 디렉토리 내에서 관리합니다. 데이터베이스 품질과 스위트 검증을 위해 codeql, jq, uv와 같은 도구를 활용합니다.
agent가 수행할 수 있는 작업
- 1심층 보안 스캔 수행저장소에 대해 포괄적인 보안 및 품질 스캔을 실행하여 취약점을 찾습니다.
- 2기존 데이터베이스에서 선택사용 가능한 언어 매치 목록에서 이전에 구축된 특정 데이터베이스를 선택합니다.
skill의 SKILL.md를 기반으로 작성되었습니다. 모든 사용 사례는 해당 문서의 인용구와 대조 확인되었습니다.
codeql skill 설치 방법
각 명령은 skill 폴더를 클라이언트의 skills 디렉터리에 복사합니다. 클라이언트를 재시작하거나 새 세션을 시작하여 로드하세요.플러그인에 codeql skill 설치
/plugin marketplace add trailofbits/skills /plugin install static-analysis@trailofbits
Claude Code에 codeql skill 설치
~/.claude/skills/codeql/SKILL.md
$ npx degit trailofbits/skills/plugins/static-analysis/skills/codeql ~/.claude/skills/codeqlClaude Code (project)에 codeql skill 설치
.claude/skills/codeql/SKILL.md
$ npx degit trailofbits/skills/plugins/static-analysis/skills/codeql .claude/skills/codeqlCodex에 codeql skill 설치
~/.agents/skills/codeql/SKILL.md
$ npx degit trailofbits/skills/plugins/static-analysis/skills/codeql ~/.agents/skills/codeqlGemini CLI에 codeql skill 설치
~/.gemini/skills/codeql/SKILL.md
$ npx degit trailofbits/skills/plugins/static-analysis/skills/codeql ~/.gemini/skills/codeqlClaude.ai에 codeql skill 설치
npx degit trailofbits/skills/plugins/static-analysis/skills/codeql codeql && zip -r codeql.zip codeql # claude.ai → Customize → Skills → + → Create skill → Upload a skill → codeql.zip
자동 검사
2026년 11월 13일에 실행됨전체 보고서 →자동 검사를 통과했습니다. 보안 감사는 아닙니다. 에이전트가 실행하기 전에 스크립트를 읽어 보세요. 검사 방식
- 소스 저장소 연결 및 접근 가능trailofbits/skills/plugins/static-analysis/skills/codeql is public and reachablegithub.com/trailofbits/skills/tree/HEAD/plugins/static-analy ↗통과
- 오픈 소스 라이선스 (OSI)CC-BY-SA-4.0 is not OSI-approvedspdx.org/licenses/CC-BY-SA-4.0.html ↗결함 발견
- 최근 1년 내 커밋 또는 릴리스Last commit 2026-11-11github.com/trailofbits/skills/commits ↗통과
- 스크립트에 다운로드 후 실행이나 자격 증명 탈취가 없음5 of 15 scripts read; no download-and-run, remote eval, base64 execution or credential upload통과
- SKILL.md에 숨겨진 지침 없음No override phrases, safeguard switches, credential requests or hidden charactersgithub.com/trailofbits/skills/blob/HEAD/plugins/static-analy ↗통과
- 사전 허용 도구가 제한됨Bash lets the agent run any shell command without askinggithub.com/trailofbits/skills/blob/HEAD/plugins/static-analy ↗결함 발견
SKILL.md
GitHub에서 보기CodeQL 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'sdefaultSuiteFileapplies 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.pyafter the build, confirm{baseDir}/scripts/verify_query_suite.pyexited 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/arm64mismatch, not a build failure. Try Homebrew arm64 tools or Rosetta before falling back tobuild-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_loggedexit 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-modeland--model-packsthe user chose is dropped while the final report still lists them as used - a lost scalar under
set -uaborts the block withunbound 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-extendedmisses entirely. - "security-and-quality is the broadest suite" -
security-and-qualityexcludes allexperimental/query paths. For run-all mode, import bothsecurity-and-qualityandsecurity-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/arm64mismatch, not a fundamental build failure. See macos-arm64e-workaround.md. - "No findings means the code is secure" - Run
check_db_quality.pyandverify_query_suite.pyand 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
defaultSuiteFileapplies 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
$OUTPUT_DIRto its parent directory (or the directory containing it), set$DB_NAMEto 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.ymlmarker) and{baseDir}/scripts/check_db_quality.pyexited 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.pyexited 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.logwith all commands, fixes, and quality assessments
이 스킬의 파일
34codeql skill 질문
Claude Code에 codeql skill을 어떻게 설치하나요?
npx degit trailofbits/skills/plugins/static-analysis/skills/codeql ~/.claude/skills/codeql를 실행합니다. Claude Code는 ~/.claude/skills(모든 프로젝트) 또는 .claude/skills(한 프로젝트)에서 skills를 로드하며, 설명이 작업과 일치할 때 skill을 사용합니다.
codeql skill을 사용할 수 있는 클라이언트는 무엇인가요?
Agent Skills 형식을 읽는 모든 클라이언트(Claude Code, Codex, Gemini CLI, claude.ai(ZIP으로 업로드))가 사용할 수 있습니다. skill 내 스크립트는 클라이언트가 코드 실행을 허용하는 경우에만 실행됩니다.
CodeQL이 지원하는 프로그래밍 언어는 무엇인가요?
CodeQL은 Python, JavaScript/TypeScript, Go, Java/Kotlin, C/C++, C#, Ruby, Swift를 지원합니다.
CodeQL 데이터베이스의 품질을 어떻게 보장할 수 있나요?
빌드가 성공했다고 해서 품질이 보장되는 것은 아닙니다. 빌드 후 check_db_quality.py 스크립트를 실행하고 verify_query_suite.py가 0으로 종료되었는지 확인해야 합니다.
macOS Apple Silicon에서 exit code 137이 발생하면 어떻게 해야 하나요?
exit code 137은 일반적으로 빌드 실패가 아닌 arm64e/arm64 불일치로 인해 발생합니다. build-mode=none을 시도하기 전에 Homebrew arm64 도구나 Rosetta 사용을 먼저 고려해야 합니다.