codeql▌
github/awesome-copilot · updated Apr 8, 2026
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This skill provides procedural guidance for configuring and running CodeQL code scanning — both through GitHub Actions workflows and the standalone CodeQL CLI.
CodeQL Code Scanning
This skill provides procedural guidance for configuring and running CodeQL code scanning — both through GitHub Actions workflows and the standalone CodeQL CLI.
When to Use This Skill
Use this skill when the request involves:
- Creating or customizing a
codeql.ymlGitHub Actions workflow - Choosing between default setup and advanced setup for code scanning
- Configuring CodeQL language matrix, build modes, or query suites
- Running CodeQL CLI locally (
codeql database create,database analyze,github upload-results) - Understanding or interpreting SARIF output from CodeQL
- Troubleshooting CodeQL analysis failures (build modes, compiled languages, runner requirements)
- Setting up CodeQL for monorepos with per-component scanning
- Configuring dependency caching, custom query packs, or model packs
Supported Languages
CodeQL supports the following language identifiers:
| Language | Identifier | Alternatives |
|---|---|---|
| C/C++ | c-cpp |
c, cpp |
| C# | csharp |
— |
| Go | go |
— |
| Java/Kotlin | java-kotlin |
java, kotlin |
| JavaScript/TypeScript | javascript-typescript |
javascript, typescript |
| Python | python |
— |
| Ruby | ruby |
— |
| Rust | rust |
— |
| Swift | swift |
— |
| GitHub Actions | actions |
— |
Alternative identifiers are equivalent to the standard identifier (e.g.,
javascriptdoes not exclude TypeScript analysis).
Core Workflow — GitHub Actions
Step 1: Choose Setup Type
- Default setup — Enable from repository Settings → Advanced Security → CodeQL analysis. Best for getting started quickly. Uses
nonebuild mode for most languages. - Advanced setup — Create a
.github/workflows/codeql.ymlfile for full control over triggers, build modes, query suites, and matrix strategies.
To switch from default to advanced: disable default setup first, then commit the workflow file.
Step 2: Configure Workflow Triggers
Define when scanning runs:
on:
push:
branches: [main, protected]
pull_request:
branches: [main]
schedule:
- cron: '30 6 * * 1' # Weekly Monday 6:30 UTC
push— scans on every push to specified branches; results appear in Security tabpull_request— scans PR merge commits; results appear as PR check annotationsschedule— periodic scans of the default branch (cron must exist on default branch)merge_group— add if repository uses merge queues
To skip scans for documentation-only PRs:
on:
pull_request:
paths-ignore:
- '**/*.md'
- '**/*.txt'
paths-ignorecontrols whether the workflow runs, not which files are analyzed.
Step 3: Configure Permissions
Set least-privilege permissions:
permissions:
security-events: write # Required to upload SARIF results
contents: read # Required to checkout code
actions: read # Required for private repos using codeql-action
Step 4: Configure Language Matrix
Use a matrix strategy to analyze each language in parallel:
jobs:
analyze:
name: Analyze (${{ matrix.language }})
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
include:
- language: javascript-typescript
build-mode: none
- language: python
build-mode: none
For compiled languages, set the appropriate build-mode:
none— no build required (supported for C/C++, C#, Java, Rust)autobuild— automatic build detectionmanual— custom build commands (advanced setup only)
For detailed per-language autobuild behavior and runner requirements, search
references/compiled-languages.md.
Step 5: Configure CodeQL Init and Analysis
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Initialize CodeQL
uses: github/codeql-action/init@v4
with:
languages: ${{ matrix.language }}
build-mode: ${{ matrix.build-mode }}
queries: security-extended
dependency-caching: true
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@v4
with:
category: "/language:${{ matrix.language }}"
Query suite options:
security-extended— default security queries plus additional coveragesecurity-and-quality— security plus code quality queries- Custom query packs via
packs:input (e.g.,codeql/javascript-queries:AlertSuppression.ql)
Dependency caching: Set dependency-caching: true on the init action to cache restored dependencies across runs.
Analysis category: Use category to distinguish SARIF results in monorepos (e.g., per-language, per-component).
Step 6: Monorepo Configuration
For monorepos with multiple components, use the category parameter to separate SARIF results:
category: "/language:${{ matrix.language }}/component:frontend"
To restrict analysis to specific directories, use a CodeQL configuration file (.github/codeql/codeql-config.yml):
paths:
- apps/
- services/
paths-ignore:
- node_modules/
- '**/test/**'
Reference it in the workflow:
- uses: github/codeql-action/init@v4
with:
config-file: .github/codeql/codeql-config.yml
Step 7: Manual Build Steps (Compiled Languages)
If autobuild fails or custom build commands are needed:
- language: c-cpp
build-mode: manual
Then add explicit build steps between init and analyze:
- if: matrix.build-mode == 'manual'
name: Build
run: |
make bootstrap
make release
Core Workflow — CodeQL CLI
Step 1: Install the CodeQL CLI
Download the CodeQL bundle (includes CLI + precompiled queries):
# Download from https://github.com/github/codeql-action/releases
# Extract and add to PATH
export PATH="$HOME/codeql:$PATH"
# Verify installation
codeql resolve packs
codeql resolve languages
Always use the CodeQL bundle, not a standalone CLI download. The bundle ensures query compatibility and provides precompiled queries for better performance.
Step 2: Create a CodeQL Database
# Single language
codeql database create codeql-db \
--language=javascript-typescript \
--source-root=src
# Multiple languages (cluster mode)
codeql database create codeql-dbs \
--db-cluster \
--language=java,python \
--command=./build.sh \
--source-root=src
For compiled languages, provide the build command via --command.
Step 3: Analyze the Database
codeql database analyze codeql-db \
javascript-code-scanning.qls \
--format=sarif-latest \
--sarif-category=javascript \
--output=results.sarif
Common query suites: <language>-code-scanning.qls, <language>-security-extended.qls, <language>-security-and-quality.qls.
Step 4: Upload Results to GitHub
codeql github upload-results \
--repository=owner/repo \
--ref=refs/heads/main \
--commit=<commit-sha> \
--sarif=results.sarif
Requires GITHUB_TOKEN environment variable with security-events: write permission.
CLI Server Mode
To avoid repeated JVM initialization when running multiple commands:
codeql execute cli-server
For detailed CLI command reference, search
references/cli-commands.md.
Alert Management
Severity Levels
Alerts have two severity dimensions:
- Standard severity:
Error,Warning,Note - Security severity:
Critical,High,Medium,Low(derived from CVSS scores; takes display precedence)
Copilot Autofix
GitHub Copilot Autofix generates fix suggestions for CodeQL alerts in pull requests automatically — no Copilot subscription required. Review suggestions carefully before committing.
Alert Triage in PRs
- Alerts appear as check annotations on changed lines
- Check fails by default for
error/critical/highseverity alerts - Configure merge protection rulesets to customize the threshold
- Dismiss false positives with a documented reason for audit trail
For detailed alert management guidance, search
references/alert-management.md.
Custom Queries and Packs
Using Custom Query Packs
- uses: github/codeql-action/init@v4
with:
packs: |
my-org/[email protected]
codeql/javascript-queries:AlertSuppression.ql
Creating Custom Query Packs
Use the CodeQL CLI to create and publish packs:
# Initialize a new pack
codeql pack init my-org/my-queries
# Install dependencies
codeql pack install
# Publish to GitHub Container Registry
codeql pack publish
CodeQL Configuration File
For advanced query and path configuration, create .github/codeql/codeql-config.yml:
paths:
- apps/
How to use codeql on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add codeql
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches codeql from GitHub repository github/awesome-copilot and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate codeql. Access the skill through slash commands (e.g., /codeql) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★69 reviews- ★★★★★Advait Bansal· Dec 28, 2024
codeql reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yusuf Li· Dec 24, 2024
I recommend codeql for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Shikha Mishra· Dec 20, 2024
Keeps context tight: codeql is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Fatima Jackson· Dec 20, 2024
codeql is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Noah Robinson· Dec 20, 2024
Solid pick for teams standardizing on skills: codeql is focused, and the summary matches what you get after install.
- ★★★★★Yuki Abbas· Dec 12, 2024
codeql is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Evelyn Ndlovu· Dec 8, 2024
codeql fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakura Ramirez· Dec 4, 2024
Keeps context tight: codeql is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Evelyn Nasser· Nov 27, 2024
codeql has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Fatima White· Nov 19, 2024
Keeps context tight: codeql is the kind of skill you can hand to a new teammate without a long onboarding doc.
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