Skill UI

This skill teaches users how to optimize search queries for specific, structural information found within academic theses or dissertations. It emphasizes the critical need to include qualifying terms like "acknowledgment," "thesis," or "dissertation" in searches to successfully retrieve details about committee members, dedicating recipients, or other section-specific data, rather than just topic abstracts. This dramatically improves the relevance and accuracy of information retrieval.

Quick-reference card for the caveman skill suite: lists the caveman, ultracave and megacave reply modes, plus sub-skills for terse commits, one-line PR reviews, and markdown compression, with env-var and config-file setup.

This guide outlines security best practices for managing Bright Data zone credentials, API tokens, and webhooks. It covers crucial steps like credential inventory, environment variable security, zone isolation across environments, scheduled credential rotation, and implementing pre-commit hooks to prevent sensitive data leakage, maximizing data security and compliance.

Transforms raw calendar data (ICS/JSON) into actionable development workflows. This skill automatically generates detailed meeting briefings, summarizes standup notes using Git commits and CI status, and creates repeatable Bash scripts. It bridges time management and development tasks, ensuring users are always prepared for their next meeting or sprint.

Implements automated policy enforcement and guardrails for Canva Connect API integrations. This system enforces best practices such as preventing hardcoded credentials, ensuring proper rate limit handling (429 responses), and validating robust OAuth configurations. It is essential for maintaining high code quality and security in CI/CD pipelines and pre-commit hooks.

A comprehensive guide to securing ClickUp API usage. Learn best practices for managing API tokens, implementing least-privilege access, and auditing usage patterns. This includes secure token rotation procedures, using environment variables, setting up pre-commit hooks to prevent leaks, and structuring calls for robust audit logging.

This skill helps users design robust and optimal software interfaces (APIs) for a module by applying the 'Design It Twice' philosophy. It generates multiple, radically different architectural designs using parallel agents, allowing for comprehensive comparison of trade-offs (e.g., simplicity vs. flexibility, specialized vs. general-purpose). Use this when you need to explore various structural options or compare module shapes before committing to a final design.

Act as a constructive critic and rigorous stress-tester for any idea, proposal, or strategy. This mode systematically challenges underlying assumptions, identifies technical and market edge cases, anticipates diverse stakeholder objections (engineering, leadership, legal), and suggests actionable mitigations. Use it when you need a multi-faceted, robust evaluation to strengthen your plan before committing resources.

A comprehensive guide detailing security best practices for integrating with ElevenLabs. It covers secure API key management using environment variables, implementing Git pre-commit hooks, and demonstrating robust webhook signature verification using HMAC-SHA256. This ensures secure data handling, replay protection, and proper voice data governance.

A comprehensive platform for managing the entire fundraising lifecycle. Users can leverage AI to prospect ideal investors, automate outreach campaigns, establish secure deal rooms for document sharing, track due diligence progress, monitor key performance metrics, and finalize funding commitments, all within a single workflow.

This tool enforces four rigorous coding principles, inspired by Andrej Karpathy's insights on LLM pitfalls. It guides developers to surface assumptions, prioritize simplicity, make surgical changes, and define verifiable goals. Use it when reviewing code, checking diffs, or before committing to ensure high code quality and prevent over-engineering.

This skill provides advanced techniques for robust prompt engineering within LangChain applications. It solves common production issues, such as f-string template breakage when user input contains literal curly braces, and guides developers on consolidating scattered prompts. Crucially, it details how to use the LangSmith Prompt Hub to version, pin, and A/B test prompts using immutable commit hashes, ensuring stable, reproducible LLM deployments.