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Found 11,818 Skills
Expert knowledge of agentic AI design patterns for autonomous agent development
Product Requirement Prompts (PRP) methodology for AI-assisted development with validation loops and autonomous execution
Agent skill that helps AI coding assistants write smarter, modern SwiftUI code with best practices for API usage, design, performance, and accessibility
A curated collection of research papers and resources on agentic reasoning for Large Language Models, organized by planning, tool use, search, self-evolution, and multi-agent systems.
Integrates a Flows/Dune app with the Fusion built-in PAIA agent panel using @cognite/app-sdk. Use this skill whenever a developer wants to: open the agent panel from their app, send the agent a contextual message, let the agent read app state (resources), or let the agent call actions in the app. Triggers: "fusion agent", "PAIA", "agent panel", "sendAgentMessage", "sendAgentLayoutMode", "agent server", "registerAgentServer", "connectToHostApp", "agent integration", "agent sidebar", "app-sdk agent". Always use this skill instead of manually writing agent integration code — it sets up the correct lifecycle, graceful fallback, and recommended file structure.
Execute codeagent-wrapper for multi-backend AI code tasks. Supports Codex, Claude, Gemini, and OpenCode backends with agent presets, skill injection, file references (@syntax), worktree isolation, parallel execution, and structured output.
Guides product management for human data platforms—annotation and labeling products, workforce workflows, task design, quality systems (gold sets, adjudication, inter-annotator agreement), customer ML-team project delivery, contributor experience, and privacy-safe handling of human-generated training data. Use when prioritizing roadmap for labeling/RLHF/eval data platforms, writing PRDs for annotation or QA features, defining success metrics for throughput and quality, scoping enterprise customer workflows, or balancing cost-quality-speed tradeoffs—not for hands-on model training (data-scientist), warehouse/analytics pipelines (data-warehouse-engineer), generic BRD workshops without product lens (business-analyst), AI solution architecture for copilots (applied-ai-architect-commercial-enterprise), or control implementation for audits (compliance-engineer). UX flows: product-designer. Eval harnesses: prompt-engineer-agent-prompts-evals. Pricing/packaging for platform: product-management-monetization.
Benchmark CodeGraph retrieval quality on a real codebase by comparing agent behavior with vs without CodeGraph. Use when the user runs /agent-eval or asks to test, benchmark, audit, or validate a codegraph version (the local dev build or a published npm version) against a language's repo.
The agentmemory HTTP REST API surface, the primary protocol for talking to the memory server. Use when calling agentmemory over HTTP, when MCP is unavailable and you need a fallback, or when integrating a host that does not speak MCP.
Used when executing implementation plans containing independent tasks in the current session
Use when creating cloud sandboxes (microVMs) to run code, start dev servers, and generate live preview URLs. Also covers deploying AI agents, MCP servers, batch jobs, and Agent Drives (shared filesystems) on Blaxel's serverless infrastructure. Reach for this skill when you need isolated compute environments, real-time app previews, shared file storage across sandboxes, or to deploy agentic workloads.
Manage Blaxel resources from the command line using the bl CLI. Deploy agents, sandboxes, jobs, and MCP servers. Also installs the Blaxel CLI if not present.