agentic-workflow
A multi-agent workflow that orchestrates research, planning, validation, and coding tasks to ensure quality implementation.
Install
mkdir -p .claude/skills/agentic-workflow && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1149" && unzip -o skill.zip -d .claude/skills/agentic-workflow && rm skill.zipInstalls to .claude/skills/agentic-workflow
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Agentic Workflow PatternKey capabilities
- →Orchestrate a multi-agent pipeline for implementation tasks
- →Execute research using a dedicated research agent
- →Generate implementation plans based on research findings
- →Validate implementation plans against research and best practices
- →Implement code using a Test-Driven Development (TDD) approach
- →Review implementations by cross-referencing artifacts and confirming tests
How it works
The skill orchestrates a multi-agent pipeline where specialized agents perform research, planning, validation, implementation, and review, communicating via file-based handoff and running in the background.
Inputs & outputs
When to use agentic-workflow
- →Implementing complex features requiring research
- →Drafting implementation plans for large codebase changes
- →Validating technical approaches against documented best practices
About this skill
Agentic Workflow Pattern
Standard multi-agent pipeline for implementation tasks.
Architecture Principles
- Use
run_in_background: truefor all agents to keep main context minimal - Use
Tasktool (neverTaskOutput) to avoid receiving full agent transcripts - Agents write outputs to
.claude/cache/agents/<stage>/for injection into subsequent agents - Main conversation is pure orchestration — no heavy lifting, only coordination
Workflow Stages
1. Research Agent
Task(subagent_type="oracle", run_in_background=true, prompt="""
Query NIA Oracle (via /nia-docs skill) to verify approach and gather best practices.
Output to: .claude/cache/agents/oracle/<task>-research.md
""")
- Enforce NIA as the research layer
- Output: Research findings
2. Planning Agent
Task(subagent_type="plan-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/oracle/<task>-research.md
Use RP-CLI to analyze the target codebase section.
Generate implementation plan informed by research.
Output to: .claude/cache/agents/plan-agent/<task>-plan.md
""")
- Receives: Research agent output as context
- Output: Implementation plan
3. Validation Agent
Task(subagent_type="validate-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/plan-agent/<task>-plan.md
Read: .claude/cache/agents/oracle/<task>-research.md
Review plan against research findings and best practices.
Output to: .claude/cache/agents/validate-agent/<task>-validated.md
""")
- Reviews plan against research
- Output: Validated plan with amendments
4. Implementation Agent
Task(subagent_type="agentica-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/validate-agent/<task>-validated.md
Read: .claude/cache/agents/oracle/<task>-research.md
TDD approach: Write failing tests FIRST, then implement.
Run tests to verify.
Output summary to: .claude/cache/agents/implement-agent/<task>-implementation.md
""")
- Receives: Validated plan + research context
- TDD: Failing tests first
- Output: Implementation + tests
5. Review Agent
Task(subagent_type="review-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/implement-agent/<task>-implementation.md
Read: .claude/cache/agents/validate-agent/<task>-validated.md
Read: .claude/cache/agents/oracle/<task>-research.md
Cross-reference implementation against plan and research.
Run tests to confirm passing.
Output to: .claude/cache/agents/review-agent/<task>-review.md
""")
- Cross-references all artifacts
- Confirms tests pass
- Output: Review summary
Agent Progress Monitoring
# Watch for system reminders:
# "Agent a42a16e progress: 6 new tools used, 88914 new tokens"
# Poll for output files:
find .claude/cache/agents -name "*.md" -mmin -5
# Check task file size growth:
wc -c /tmp/claude/.../tasks/<id>.output
Stuck detection:
- Progress reminders stop arriving
- Task output file size stops growing
- Expected output file not created after reasonable time
Directory Structure
.claude/cache/agents/
├── oracle/
│ └── <task>-research.md
├── plan-agent/
│ └── <task>-plan.md
├── validate-agent/
│ └── <task>-validated.md
├── implement-agent/
│ └── <task>-implementation.md
└── review-agent/
└── <task>-review.md
Key Rules
- Never use TaskOutput - floods context with 70k+ token transcripts
- Always run_in_background=true - isolates agent context
- File-based handoff - each agent reads previous agent's output file
- Poll, don't block - check file system for outputs, don't wait
- TDD in implementation - failing tests first, then make them pass
Source
- Session 2026-01-01: SDK Phase 3 implementation using this pattern
How it compares
This skill employs a structured, multi-agent pipeline with file-based communication and background execution, which differs from a single-agent approach or direct sequential execution.
Compared to similar skills
agentic-workflow side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| agentic-workflow (this skill) | 6 | 7mo | Review | Advanced |
| opencode-cli | 14 | 7mo | Review | Advanced |
| claude-automation-recommender | 47 | 2mo | Review | Beginner |
| mcp-integration | 21 | 8mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by parcadei
View all by parcadei →You might also like
opencode-cli
SpillwaveSolutions
This skill should be used when configuring or using the OpenCode CLI for headless LLM automation. Use when the user asks to "configure opencode", "use opencode cli", "set up opencode", "opencode run command", "opencode model selection", "opencode providers", "opencode vertex ai", "opencode mcp servers", "opencode ollama", "opencode local models", "opencode deepseek", "opencode kimi", "opencode mistral", "fallback cli tool", or "headless llm cli". Covers command syntax, provider configuration, Vertex AI setup, MCP servers, local models, cloud providers, and subprocess integration patterns.
claude-automation-recommender
anthropics
Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.
mcp-integration
anthropics
This skill should be used when the user asks to "add MCP server", "integrate MCP", "configure MCP in plugin", "use .mcp.json", "set up Model Context Protocol", "connect external service", mentions "${CLAUDE_PLUGIN_ROOT} with MCP", or discusses MCP server types (SSE, stdio, HTTP, WebSocket). Provides comprehensive guidance for integrating Model Context Protocol servers into Claude Code plugins for external tool and service integration.
hook-development
anthropics
This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
agent-factory
alirezarezvani
Claude Code agent generation system that creates custom agents and sub-agents with enhanced YAML frontmatter, tool access patterns, and MCP integration support following proven production patterns
swarm-advanced
ruvnet
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows