AvivK5498

Bootstrap lean multi-agent orchestration with beads task tracking. Use for projects needing agent delegation without heavy MCP overhead.

Bootstrap lean multi-agent orchestration with beads task tracking. Use for projects needing agent delegation without heavy MCP overhead.

Core engineering principles for implementation tasks

Invoke at the start of any implementation task to enforce verification-first development

React and Next.js performance optimization patterns. Use BEFORE implementing any React code to ensure best practices are followed.

Collaborative CEO co-pilot for SaaS strategy sessions. Researches markets, validates ideas, designs UI inspiration boards, and produces a .strategy/ folder that Beads Orchestration consumes for autonomous building. Use as Session 1 before a Beads build session.

Systematic debugging toolkit for AI agentic workflows in customer support. Use when diagnosing issues with AI agents including wrong responses, tool/function calling problems, conversation loops, stuck states, or performance/latency issues. Works with any framework (LangChain, custom agents, Claude API) and accepts conversation logs, API logs, tool execution logs, and agent configurations.

Create and debug Agentform AI agent configurations (.af files). Use when: (1) Creating new agentform projects or workflows (2) Debugging agentform syntax errors (3) Adding MCP server integrations (4) Configuring agents, models, policies, or capabilities (5) Writing workflow steps with routing and human approval Agentform is "Infrastructure as Code for AI agents" - declarative .af files define agents, workflows, and policies.

Bootstrap lean multi-agent orchestration with beads task tracking. Use for projects needing agent delegation without heavy MCP overhead.

Build production-ready RunPod serverless endpoints with optimized cold start times. Use when creating or modifying RunPod serverless workers for (1) vLLM-based LLM inference, (2) ComfyUI image/video generation, or (3) custom Python inference. Supports both baked models (fastest cold starts) and dynamic loading (shared models). Generates complete projects including Dockerfiles, worker handlers, startup scripts, and configuration optimized for minimal cold start latency.

Set up codebase intelligence for AI agents. Runs the agentifind CLI to extract code structure using LSP (pyright/tsserver) with tree-sitter fallback, then synthesizes a CODEBASE.md navigation guide. Run this skill to get a complete codebase map in .claude/ directory.

Core engineering principles for implementation tasks

Use when user asks to create, build, or add a new skill or capability to the agent

Delegate coding tasks to Claude Code. Use when: building features, creating handlers, refactoring, reviewing PRs, iterative coding. NOT for: simple file edits (use filesystem), or quick config changes (use config_update).

Set up codebase intelligence for AI agents. Runs the agentifind CLI to extract code structure using LSP (pyright/tsserver) with tree-sitter fallback, then synthesizes a CODEBASE.md navigation guide. Run this skill to get a complete codebase map in .claude/ directory.

Create a benchmark to measure CODEBASE.md effectiveness. Sets up hooks to run two parallel agents (one with guide, one without) and compare their efficiency. Requires /agentifind to be run first.