Skill UI

Generates a highly polished, self-contained single-page HTML landing page optimized for maximum conversion. It incorporates advanced features like 3D CSS animations, GSAP scroll effects, and mouse parallax depth. To ensure accuracy, the skill uses a structured intake process (product pitch, target audience, brand colors, and tone) to build a conversion-ready page that reflects the actual product, not generic boilerplate. Outputs a single, deployable HTML file.

This skill generates comprehensive Entity Relationship Diagrams (ERDs) and database schemas as D2 source code from an existing Entity Framework Core codebase. It analyzes DbContexts, DbSet declarations, Fluent API mappings, and migrations to accurately visualize tables, columns, primary/foreign keys, relationships (including many-to-many joins), owned types, and constraints. The output is a structured .d2 file, suitable for rendering into SVG or PNG, helping developers document the persistence model.

This skill provides a comprehensive framework for auditing and improving the efficiency of GitHub Codespaces environments. It helps users identify waste sources like oversized devcontainers, inefficient machine types, and poorly scoped prebuilds. By following a structured workflow, users can reduce monthly cloud spending, drastically cut startup times, and maintain optimal development throughput.

This powerful router analyzes complex document types (scanned, medical, table-heavy, etc.) and determines the optimal conversion pipeline (tools, flags, cleanup) to extract data into various formats like Markdown, JSON, or DOCX. It goes beyond simple text extraction by prioritizing the preservation of document structure, relational data, and high fidelity, ensuring the generated output is accurate for downstream machine processing.

This skill serves as a compatibility test to ensure that skills compiled from MDA (Metadata Definition Architecture) sources are correctly recognized and installed by the OpenClaw platform. It validates the integrity of the skill loading process, confirming successful parsing and integration.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

This tool indexes local files or directories into a persistent FTS5 knowledge base. This allows subsequent search operations to efficiently retrieve highly relevant, focused snippets from the project source without needing to re-read the raw files every time. It is crucial for managing and searching large, complex codebases or detailed project documentation.

This tool accesses a persistent FTS5 knowledge base containing previously indexed project documentation, local content, and session memory. Instead of feeding entire raw sources into the conversation context, it allows users to perform targeted, efficient searches using specific queries and source scoping. This is ideal for quickly retrieving technical details, project specifications, or historical context without lengthy re-reading.

This skill enforces an algorithm-first discipline, requiring the user to state the time and space complexity (Big-O), the intended data structure, and the core algorithm family before writing any complex code (loops, queries, recursion). It serves as a critical gatekeeper to prevent common performance pitfalls like O(n²) complexity, N+1 database queries, and brute-force implementation errors, significantly improving code robustness and efficiency.

This skill provides comprehensive financial oversight for internal R&D programs and portfolios. It facilitates multi-period budget planning, applying indirect F&A rates, and tracks critical metrics like burn rate and runway against value-inflection milestones. It also supports the capitalization vs. expense determination process based on established accounting standards (IAS 38/ASC 730). Crucially, the tool only provides decision support and never books entries or determines final accounting treatment.

This skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.

This skill performs an observability-first audit of deployed Vercel applications. It comprehensively analyzes production metrics, project configurations, and codebase scans to identify potential cost overruns and performance bottlenecks. It provides version-aware, actionable recommendations for improving application efficiency and reducing cloud expenditure.