Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Generate comprehensive GitHub Feature Area Status reports for the Windows App SDK repository. Use when asked to create triage reports, identify high-priority issues, analyze feature area health, find issues needing attention, or generate status dashboards. Triggers on requests involving issue triage, area status, priority analysis, bug tracking reports, or engineering team focus areas.
Prepare weekly triage meeting summary for WinAppSDK Needs-Triage issues. Use when preparing for triage meetings, reviewing Needs-Triage issues, generating diff reports since last triage, summarizing new or updated issues, or creating action item recommendations. Triggers on requests involving triage preparation, Needs-Triage review, meeting summary, triage diff, or weekly issue analysis.
Generate query-agnostic analytics that model adversary behavior by translating hunt investigative intent into analytic definitions grounded in schema semantics. This skill is used to define how behavior should manifest in data before query execution or validation, and works best when informed by system internals, adversary tradecraft, a structured hunt focus, and suggested data sources.
Assemble a complete hunt blueprint by consolidating outputs from prior hunt planning skills into a single, structured plan for execution. Use this skill after system and tradecraft research, hunt focus definition, data source identification, and analytics generation have been completed. This skill is synthesis and packaging only and must not introduce new research, assumptions, or analytics.
Research system internals and adversary tradecraft to ground a threat hunt in real system behavior and realistic abuse patterns. Use this skill at the start of hunt planning, when you are given a high-level hunt topic but lack a clear understanding of how the system normally operates or how adversaries are known to abuse it. This skill informs early hunt direction by producing candidate abuse patterns, key assumptions, and cited sources, and should be used before defining a concrete hunt hypothesis or selecting data sources.
Audit a documentation site for agent-friendliness: discovery, markdown delivery, crawlability, semantic structure, machine-readable surfaces, and content legibility. Use when asked to assess docs.docker.com or any docs site for AI/agent readiness, produce a scored report, compare with external scanners, or generate a remediation list. Triggers on: "audit docs for agent readiness", "how agent-friendly is docs.docker.com", "score our docs for AI agents", "review llms.txt / markdown / crawlability", "create an agent-readiness remediation plan".
Clone a dockersamples Labspace repo, extract learning objectives and module structure from labspace.yaml, and produce a Hugo guide page under content/guides/ with correct frontmatter, labspace-launch shortcode, and Docker docs style compliance. Use when asked to create a lab guide, write a Labspace page, add a Docker lab tutorial, migrate a lab to docs, or document a hands-on lab.
Handle Hugo docs information-architecture moves: discover old vs new URLs, add front matter aliases (Phase 1), update in-repo links (Phase 2), interactive List 2 resolution and fragment validation (Phase 3; no guessing). Supports PR-scoped mapping plus whole-content sweeps for inbound links to that mapping, or a full-site follow-up. Triggers on: "IA migration", "redirects for moved pages", "fix links after content move", "PR-scoped link/anchor pass", "aliases for old URLs". After branch work, chain the review-changes skill (main...HEAD) before a PR. Agents must run the in-file required procedure and definition of done, not the phases alone in isolation.
Review uncommitted or recently committed documentation changes for correctness, coherence, and style compliance. Use before creating a PR to catch issues. "review my changes", "review the diff", "check the fix before submitting", "does this look right".