creative-thinking-for-research
Applies cognitive science frameworks for creative thinking to generate genuinely novel research directions in computer science and AI.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
147 skills found
Applies cognitive science frameworks for creative thinking to generate genuinely novel research directions in computer science and AI.
Focus testing effort on highest-risk areas using risk assessment and prioritization. Use when planning test strategy, allocating resources, or making coverage decisions.
Analyze local system hardware (RAM, CPU, GPU/VRAM) to receive expert recommendations for optimized local LLM models, quantization settings, and performance estimates.
Autonomous pattern detection and skill recommendation engine that monitors project memory, logs, and task lists to evolve your AI agent's capabilities automatically.
Analyze AppWorld task failures to extract specific API patterns and generate actionable playbook bullets with concrete code examples.
Architects enterprise AI agents from structured specs, generating production-ready code, data flow diagrams, and platform-specific logic for ServiceNow, Salesforce, and Snowflake.
Autonomous multi-team codebase improvement agent with specialized modes: narrow (goal-directed), broad (hypothesis-divergent), and sweep (quality-focused).
Enforces professional voice, tone, and technical style guidelines for React documentation, ensuring consistency across Learn, Reference, and Blog pages.
Search, discover, and refine AI prompts using the prompts.chat library. Access thousands of community-curated prompts for ChatGPT, Claude, and other AI models.
Generate optimized YouTube metadata, titles, and descriptions for bilingual audiobook videos based on source and target language pairs.
Generate daily and weekly planning reports from backlog and carryover state, applying WIP limits and priority rules from BaseContext.yaml with automatic git commit/push.
Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.