productivity▌

Multi-stage GitLab CI/CD pipelines with Docker builds, Kubernetes deployments, and security scanning. \n \n Covers core pipeline patterns including build, test, and deploy stages with artifact caching and environment management \n Includes Docker image building and pushing to registries, multi-environment deployments (staging/production), and Terraform infrastructure automation \n Provides security scanning templates (SAST, dependency scanning, container scanning) and Trivy vulnerability checks

Captures learnings, errors, and corrections to enable continuous improvement across agent sessions. \n \n Logs failures, user corrections, knowledge gaps, and API errors to structured markdown files ( .learnings/LEARNINGS.md , ERRORS.md , FEATURE_REQUESTS.md ) with consistent ID, priority, and status tracking \n Supports promotion of broadly applicable learnings to project memory files ( CLAUDE.md , AGENTS.md , .github/copilot-instructions.md ) and workspace files in OpenClaw ( SOUL.md , TOOLS.m

Systematic PR preparation that validates tests and generates high-quality PR bodies.

Update specification files for AI-ready consumption based on new requirements or code changes. \n \n Modifies existing specification documents in /spec/ directory using structured markdown with YAML front matter \n Ensures specifications are precise, unambiguous, and machine-readable through established formatting standards (headings, lists, tables, code blocks) \n Covers 11 core sections: purpose, definitions, requirements, interfaces, acceptance criteria, testing strategy, rationale, dependenc

Microsoft Fabric Lakehouse storage for unified tabular and non-tabular data with Delta Lake, SQL analytics, and fine-grained security. \n \n Combines data lake flexibility with data warehouse management through Delta Lake format, ACID transactions, versioning, and SQL endpoints for T-SQL querying \n Organizes data via schemas (folders under Tables), shortcuts (virtual links to internal/external sources), and materialized views for optimized query performance \n Supports multiple data formats: De

CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md，其中包含认证、权限处理。然后阅读 ../lark-vc/SKILL.md，了解会议纪要相关操作。

Scaffold new Agent Skills with proper frontmatter, directory structure, and bundled resource organization. \n \n Generates SKILL.md files with required YAML frontmatter (name, description) and optional metadata fields for skill discovery \n Supports optional bundled directories: scripts/ for automation, references/ for documentation, assets/ for static files, and templates/ for starter code \n Provides step-by-step workflow for creating skills from scratch or duplicating this template as a start

Security engineering tools for threat modeling, vulnerability analysis, secure architecture design, and penetration testing.

Convert X tweets and articles to markdown with YAML front matter using reverse-engineered API. \n \n Extracts tweets, threads, and X articles to markdown with metadata (author, URL, cover image, thread count) \n Requires explicit user consent before first use; displays disclaimer about reverse-engineered API and potential account risks \n Supports media download to local imgs/ and videos/ directories with automatic link rewriting, configurable per-conversion or via preferences \n Authenticates v

Best practices guide for C# asynchronous programming patterns and pitfalls. \n \n Covers naming conventions (Async suffix), return types (Task, ValueTask, avoid void), and exception handling strategies including ConfigureAwait and Task.FromException \n Highlights performance optimization techniques: Task.WhenAll for parallel execution, Task.WhenAny for timeouts, and cancellation token usage \n Documents critical pitfalls to avoid: blocking calls like .Wait() and .Result, async void methods outsi