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yonatangross

implement

by yonatangross

The Complete AI Development Toolkit for Claude Code — 159 skills, 34 agents, 20 commands, 144 hooks. Production-ready patterns for FastAPI, React 19, LangGraph, security, and testing.

29🍴 4📅 2026年1月23日
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SKILL.md


name: implement description: Full-power feature implementation with parallel subagents, skills, and MCPs. Use when implementing features, building features, creating features, or developing features. context: fork version: 1.1.0 author: OrchestKit tags: [implementation, feature, full-stack, parallel-agents] user-invocable: true

Implement Feature

Maximum utilization of parallel subagent execution for feature implementation.

Quick Start

/implement user authentication
/implement real-time notifications
/implement dashboard analytics

Phase 1: Discovery & Planning

1a. Create Task List

Break into small, deliverable, testable tasks:

  • Each task completable in one focused session
  • Each task MUST include its tests
  • Group by domain (frontend, backend, AI, shared)

1b. Research Current Best Practices

# PARALLEL - Web searches (launch all in ONE message)
WebSearch("React 19 best practices 2026")
WebSearch("FastAPI async patterns 2026")
WebSearch("TypeScript 5.x strict mode 2026")

1c. Context7 Documentation

# PARALLEL - Library docs (launch all in ONE message)
mcp__context7__query-docs(libraryId="/vercel/next.js", query="app router")
mcp__context7__query-docs(libraryId="/tiangolo/fastapi", query="dependencies")

Phase 2: Skills Auto-Loading (CC 2.1.6)

CC 2.1.6 auto-discovers skills - no manual loading needed!

Relevant skills activated automatically based on task:

  • api-design-framework - REST/GraphQL patterns
  • react-server-components-framework - RSC, Server Actions
  • type-safety-validation - Zod, tRPC, Prisma
  • unit-testing / integration-testing - Test patterns

Phase 3: Parallel Architecture Design (5 Agents)

Launch ALL 5 agents in ONE Task message with run_in_background: true:

AgentFocus
PlanArchitecture planning, dependency graph
backend-system-architectAPI, services, database
frontend-ui-developerComponents, state, hooks
llm-integratorLLM integration (if needed)
ux-researcherUser experience, accessibility
# PARALLEL - All agents in ONE message
Task(subagent_type="Plan", prompt="...", run_in_background=True)
Task(subagent_type="backend-system-architect", prompt="...", run_in_background=True)
Task(subagent_type="frontend-ui-developer", prompt="...", run_in_background=True)
Task(subagent_type="llm-integrator", prompt="...", run_in_background=True)
Task(subagent_type="ux-researcher", prompt="...", run_in_background=True)

Phase 4: Parallel Implementation (8 Agents)

AgentTask
backend-system-architect #1API endpoints
backend-system-architect #2Database layer
frontend-ui-developer #1UI components
frontend-ui-developer #2State & API hooks
llm-integratorAI integration
rapid-ui-designerStyling
test-generator #1Test suite
prioritization-analystProgress tracking

Phase 5: Integration & Validation (4 Agents)

AgentTask
backend-system-architectBackend + database integration
frontend-ui-developerFrontend + API integration
code-quality-reviewer #1Full test suite
security-auditorSecurity audit

Phase 5.5: Progress Notifications (CC 2.1.7)

CC 2.1.7 supports inline notification patterns for real-time progress updates:

Agent Completion Notifications

When subagents complete, track their progress:

# After parallel agent execution
for result in agent_results:
    notification.inline(f"{result.agent}: {result.status}")

# Summary notification
notification.inline(f"Phase 5 complete: {len(agent_results)}/{expected} agents finished")

MCP Deferral Awareness

When MCPs are deferred due to context limits, adapt your workflow:

if mcp.deferred:
    notification.inline("MCP tools deferred - using cached docs")
    # Fall back to cached documentation
    docs = load_cached_docs("react-19-patterns")
else:
    # Normal MCP query
    docs = mcp__context7__query_docs(...)

Progress Tracking Pattern

Use inline notifications for long-running phases:

[Phase 4] Starting: 8 parallel agents
[Phase 4] Complete: backend-system-architect (2.3s)
[Phase 4] Complete: frontend-ui-developer (3.1s)
[Phase 4] Complete: database-engineer (1.8s)
...
[Phase 4] Finished: 8/8 agents (12.5s total)

Phase 6: E2E Verification

If UI changes, verify with agent-browser:

agent-browser open http://localhost:5173
agent-browser wait --load networkidle
agent-browser snapshot -i
agent-browser screenshot /tmp/feature.png
agent-browser close

Phase 7: Documentation

Save implementation decisions to memory MCP for future reference:

mcp__mem0__add-memory(content="Implementation decisions...", userId="project-decisions")

Summary

Total Parallel Agents: 17 across 4 phases

Tools Used:

  • context7 MCP (library documentation)
  • mem0 MCP (decision persistence)
  • agent-browser CLI (E2E verification)

Key Principles:

  • Tests are NOT optional
  • Parallel when independent (use run_in_background: true)
  • CC 2.1.6 auto-loads skills from agent frontmatter
  • Evidence-based completion
  • explore: Explore codebase before implementing
  • verify: Verify implementations work correctly

References

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