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For repeatable security checks, keep scanning and pass/fail policy in a reviewable GitHub Actions workflow or CodeQL setup. Use an agent skill separately to guide a coding assistant through bounded tasks such as explaining an alert or checking a workflow against a checklist; the skill does not run the scanner or replace CI controls.
Should you use default or advanced setup for code scanning?
CodeQL is GitHub’s code-analysis engine, but it is not the only way to put static-analysis results in GitHub code scanning. A compatible third-party scanner can also contribute results by producing SARIF, the format GitHub accepts for code-scanning results. SARIF interoperability does not mean tools have equivalent language coverage, rules, alert behavior, licensing, or maintenance costs. See GitHub’s guides to code-scanning setup types and code scanning.
| Choice | Best fit | Control and maintenance | Eligibility |
|---|---|---|---|
| Default CodeQL setup | Teams that want a low-maintenance CodeQL configuration | GitHub automatically selects supported languages, a query suite, and scan events. It offers less workflow-level control than advanced setup. | Depends on repository ownership and plan. GitHub’s CodeQL documentation lists public repositories and qualifying organization-owned repositories with GitHub Code Security enabled; check current access rules for your repository. |
| Advanced CodeQL setup | Teams that need to specify builds, languages, matrices, events, or custom queries | Maintainers configure and update a workflow, gaining control over analysis at the cost of owning more configuration. | Check current GitHub Code Security eligibility before implementation; product access rules can change. |
| Third-party SARIF scanner | Teams whose language, framework, or custom-rule needs are better served by another scanner | Requires maintaining the scanner and its workflow, checking source/build coverage, and verifying SARIF upload compatibility. Confirm licensing and current cost with the vendor. | Depends on the selected product and GitHub code-scanning access. |
Start with default setup if its supported language detection, query suite, and event behavior suit the repository. Choose advanced setup when the team can name a requirement the default cannot meet—for example, a particular build mode, a controlled query selection, or a specific schedule. Before adopting another scanner, verify its current language support, actual source coverage, output compatibility, and ongoing maintenance burden rather than assuming SARIF makes it interchangeable with CodeQL.
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First decide whether GitHub’s managed default setup covers the repository or whether the team needs an editable workflow. In advanced setup, the workflow is ordinary repository configuration: reviewers can inspect its events, permissions, build behavior, and analysis choices just as they review application changes. Use GitHub’s CodeQL code-scanning guide for the currently supported setup process and the workflow configuration reference for available options.
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- Confirm eligibility and language support. Check that code scanning is available for the repository and that the language is supported. For compiled languages, also determine how CodeQL will create its analysis database.
- Choose the setup type. Select default setup for low-maintenance automatic configuration. Select advanced setup if you need to own event filters, build steps, language selection, query configuration, or a matrix.
- Configure the event policy. Run the checks appropriate to your protected branches and pull-request process, and decide whether a scheduled scan is needed. Keep privileged permissions no broader than the workflow requires.
- Verify an actual run. Inspect the workflow result and code-scanning output. Confirm that the intended language and source files were analyzed and that any required build or SARIF upload succeeded.
- Review the configuration as security-sensitive code. Keep action references, permissions, scripts, and changes to query coverage visible to maintainers.
How do you scan pull requests and run CodeQL on a schedule?
Design events around when feedback is useful. A pull_request run can surface findings during review; a push run can check changes that land on relevant branches; a scheduled run can revisit code later. Match branch filters to the repository’s real branch protections instead of copying a generic list. In advanced setup, GitHub’s default CodeQL analysis workflow scans weekly as well as in response to configured events. A later run can identify issues made detectable by changes in queries or vulnerability knowledge, even when the code itself has not changed.
GitHub documents the schedule syntax and event options in its workflow configuration reference. A scheduled workflow only triggers when its workflow file exists on the repository’s default branch, so a schedule added only on a feature branch will not run as intended.
Keep untrusted pull-request content away from privileged execution
Pull-request code is untrusted input. Prefer an unprivileged pull-request event when it meets the need. Do not use pull_request_target merely as a convenient way to obtain elevated context, and never combine a privileged trigger with checking out or executing untrusted pull-request content without a carefully reviewed security design. Treat artifacts from privileged workflows cautiously. GitHub’s secure-use reference explains these risks and other Actions security practices.
- Grant
GITHUB_TOKENonly the permissions the workflow and jobs need. - Review and pin third-party actions to trusted revisions; avoid trusting a mutable reference without understanding who can change it.
- Keep values originating in pull-request titles, branch names, comments, or other untrusted sources out of generated shell scripts. Pass and validate data safely rather than interpolating it into executable code.
- Review which secrets are made available to each job. A third-party action running in a job may be able to access configured secrets and use the repository token available to that job.
Does CodeQL need to build the project?
It depends on the language and analysis mode. CodeQL documents none, autobuild, and manual modes for compiled-language database generation, with support varying by language. In manual mode, maintainers specify build commands. Do not assume that one mode or a single build recipe applies to every compiled language; consult the compiled-language guidance for the project’s language and mode.
Validate database creation and analyzed source in representative CI runs. A green workflow is not by itself proof that the intended application code was included: check the analysis output and investigate missing language or build coverage. Revisit that check when the project adds a language, changes build tooling, or reorganizes source.
How should you tune CodeQL query coverage?
CodeQL provides a default query suite and an expanded security-extended suite. Advanced setup can also select query packs, query files, suites, and filters. Treat a broader query selection as a trade-off among coverage, runtime, and alert volume—not as an automatic security improvement. Decide what the team can review and act on, then assess the resulting findings and workflow cost.
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For custom query packs, use a controlled version strategy. GitHub notes that a pack without a specified version resolves to the latest version; that can change what runs without an explicit workflow edit. See the CodeQL Actions query documentation for built-in Actions checks and suite information. Include workflow files in the security scope: the workflow that runs a scanner can itself introduce risk. CodeQL’s Actions queries support analysis of GitHub Actions workflow configuration.
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How do you reuse a security workflow across repositories?
Choose the reuse mechanism according to the size of the reusable unit. A reusable workflow shares a complete workflow, including multiple jobs and steps. A composite action bundles a sequence of steps to call within a job. GitHub explains the distinction in its guide to reusing workflow configurations.
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| Mechanism | Reuse unit | What to review |
|---|---|---|
| Reusable workflow | A workflow with jobs and steps, called by other workflows | Maintain a trusted central revision; define inputs and secrets deliberately. GitHub recommends commit-SHA references when callers need a fixed revision. A tag or branch reference means trusting whoever can change the referenced version. |
| Composite action | A sequence of steps used inside a job | Review the bundled steps and the permissions, inputs, and secrets available in the calling job. |
Centralize genuinely shared logic, but keep repository-specific policy visible where it matters. A shared scan workflow should make its supported inputs, permissions, and expected outputs understandable to both its maintainers and its callers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is an agent skill, and how does it fit into GitHub Actions?
An agent skill is reusable task guidance for an AI coding assistant, not a scanning engine. GitHub describes a skill as a directory containing a required SKILL.md and optional supporting Markdown, scripts, or other resources. Project skills can be stored in .github/skills, .claude/skills, or .agents/skills; personal skills can use the documented user-level locations. The cited GitHub documentation describes support across Copilot surfaces including cloud agent, code review, CLI, app, and IDE agent modes. See GitHub’s guide to adding agent skills.
A skill can make a bounded task more consistent: for example, ask an agent to summarize a CodeQL alert, map a finding to the relevant code, identify evidence needed for triage, or check a workflow against a security checklist. Keep a skill’s instructions and supporting resources under review like code because they influence agent behavior. Do not let the skill make CI policy decisions on its own or treat its output as proof that a finding is safe.
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| Task | Best owner | Reason |
|---|---|---|
| Run static analysis and upload results | Explicit CI configuration | The scanner, events, permissions, and outputs remain reviewable workflow behavior. |
| Enforce required checks or other merge policy | Repository and CI policy | Policy should not depend on an agent interpreting instructions correctly. |
| Explain a finding or prepare triage notes | A narrowly scoped agent skill, with human review | Reusable guidance can help organize analysis, but the agent’s explanation is not a verified scanner result. |
| Change workflow security settings | Maintainers review changes; CI validates them | Agent suggestions do not replace least-privilege permissions, safe handling of untrusted content, or code review. |
Are GitHub Agentic Workflows the same as agent skills?
No. GitHub documents Agentic Workflows as a separate workflow authoring and execution model: a Markdown file in .github/workflows/ with YAML frontmatter and natural-language instructions, compiled to a .lock.yml file and run through GitHub Actions or the GitHub CLI. Its frontmatter covers triggers, permissions, safe outputs, and engine selection. GitHub’s documentation identified the feature as public preview on October 7, 2026, and notes that it is subject to change; check its current status before relying on it. A SKILL.md is reusable agent task guidance, not another name for that workflow format. See GitHub’s Agentic Workflows guide.
Quick Recap
Implementation checklist
- Choose default CodeQL setup unless the team has a specific need for workflow-level control.
- Configure relevant pull-request and push checks, and schedule a scan if periodic reanalysis fits the repository.
- Verify compiled-language database generation and confirm the intended source was analyzed.
- Make query-suite and custom-pack choices deliberately, considering runtime and alert-review capacity.
- If using an external scanner, verify its coverage, SARIF compatibility, maintenance, and commercial terms independently.
- Use narrow token permissions, trusted action references, careful secret access, and safe handling of pull-request data.
- Reuse whole workflows for cross-repository pipelines and composite actions for reusable job steps; review shared references and inputs.
- Use agent skills for bounded assistance, while leaving scans and policy gates to deterministic, reviewable CI and keeping human review in the loop.
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