AG

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.zip

Installs 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 Pattern
24 charsno explicit “when” trigger
Advanced

Key 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

You give it
Implementation tasks requiring a structured pipeline
You get back
Research findings, implementation plans, validated plans, code implementation, and review summaries

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: true for all agents to keep main context minimal
  • Use Task tool (never TaskOutput) 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:

  1. Progress reminders stop arriving
  2. Task output file size stops growing
  3. 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

  1. Never use TaskOutput - floods context with 70k+ token transcripts
  2. Always run_in_background=true - isolates agent context
  3. File-based handoff - each agent reads previous agent's output file
  4. Poll, don't block - check file system for outputs, don't wait
  5. 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.

SkillInstallsUpdatedSafetyDifficulty
agentic-workflow (this skill)67moReviewAdvanced
opencode-cli147moReviewAdvanced
claude-automation-recommender472moReviewBeginner
mcp-integration218moReviewIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

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.

14174

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.

47140

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.

21123

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.

11122

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

8109

swarm-advanced

ruvnet

Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows

7110

Search skills

Search the agent skills registry