deep▌

Comprehensive GitHub repository analysis through multi-round research combining API data, web search, and structured reporting. \n \n Executes four research rounds: GitHub API extraction, discovery searches, deep investigation with web fetching, and timeline analysis from commits and issues \n Produces structured markdown reports with executive summaries, chronological timelines, metrics tables, and Mermaid diagrams for architecture and comparisons \n Implements source prioritization (official d

Master deep work with a three-part framework: 1 hour daily of Building, balanced with Maintenance and Recovery. \n \n Establish sustainable productivity through a 90-day transformation sequence: diagnose focus state, define vision and anti-vision, build a 10-year-to-daily goal hierarchy, and apply project-based learning to close skill gaps \n Identify lever-moving tasks that actually progress goals using the challenge-equals-skill principle, avoiding both anxiety (challenge too high) and boredom

Answers: "What's happening in this specific market? Who holds it, who's trading it?"

Orchestrate subagents, plan multi-step tasks, and require human approval for sensitive operations. \n \n Delegate work to specialized subagents via the task tool; custom subagents support isolated tool sets and system prompts, while the default \"general-purpose\" subagent inherits main agent configuration \n Plan and track complex workflows with write_todos , organizing tasks across pending, in-progress, and completed states; requires a thread_id for persistence across invocations \n Implement

Comprehensive research assistant that synthesizes information from multiple sources with citations. \n \n Follows a systematic five-step research process: clarifying the question, identifying key aspects, gathering information, synthesizing findings, and documenting sources \n Structures output with executive summary, key findings, detailed analysis by subtopic, areas of consensus and debate, and source evaluation \n Evaluates source credibility across peer-reviewed journals, official reports, r

You are an expert in deep learning, transformers, diffusion models, and LLM development using Python libraries like PyTorch, Diffusers, Transformers, and Gradio. Follow these guidelines when writing deep learning code.

Use when:

Pluggable memory and file backends for Deep Agents with ephemeral, persistent, and hybrid routing options. \n \n Four backend types: StateBackend (thread-scoped, ephemeral), StoreBackend (cross-session persistent), FilesystemBackend (real disk access for local dev), and CompositeBackend (route different paths to different backends) \n FilesystemMiddleware provides six file operation tools: ls , read_file , write_file , edit_file , glob , grep \n CompositeBackend uses longest-prefix matching to r

Comprehensive research and discovery before building something new. Instead of jumping straight into code from training data, this skill goes wide and deep — local exploration, web research, competitor analysis, ecosystem signals, future-casting — and produces a research brief that makes the actual build 10x more productive.

Transforms surface-level reading into deep learning through systematic analysis using 10+ proven thinking frameworks. Guides users from understanding to application through structured workflows.