planning-agent

Overview

This agent decomposes complex tasks into executable steps with explicit dependencies, owners, and sequencing. Use it at the start of any multi-step effort where order matters and failure at one step must not cascade. It produces a plan that a team — human or agent — can execute without re-deriving the design.

Planning Agent

Quick Reference — see parent for full agent ecosystem.

The Planning Agent decomposes ambiguous feature requests into ordered, executable steps with explicit dependencies, risk assessments, and verification gates. It eliminates the single biggest source of rework — unclear requirements — by forcing specificity before any code is written. Its output is a structured plan that downstream agents (research, code, review, deploy) consume directly.

When Not to Use

• Simple or one-off tasks — if the task is straightforward, direct execution is faster than structured methodology.
• Already established workflows — follow existing team conventions rather than introducing new frameworks.
• When automation overhead exceeds benefit — for very small scopes, the setup cost may not be justified.

Dependencies

• Python 3.8+ or Node.js 18+
• Access to relevant APIs/services for your specific use case
• Basic understanding of the domain concepts

Commands

# Refer to the skill's usage section for specific commands
# Adapt these to your workflow

Key Responsibilities

• Break down features: Convert natural-language requirements into a step graph with clear inputs, outputs, and dependencies
• Identify risks early: Flag ambiguous requirements, missing context, breaking changes, and parallelization opportunities before implementation starts
• Define verification gates: Specify acceptance criteria and test conditions for every step so completion is measurable

Code Example

"""Minimal planning agent pattern — decompose a feature request."""

import json, sys

def plan(feature_request: str) -> dict:
    # In practice, this calls an LLM. Here we show the output shape.
    steps = [
        {
            "name": "auth-setup",
            "type": "implementation",
            "files": ["src/auth/provider.py", "src/auth/config.py"],
            "dependencies": [],
            "risk": "low",
            "effort": "30min",
            "verification": "Auth flow test passes"
        },
        {
            "name": "callback-handler",
            "type": "implementation",
            "files": ["src/auth/callback.py"],
            "dependencies": ["auth-setup"],
            "risk": "medium",
            "effort": "1h",
            "verification": "Callback processes valid/invalid tokens"
        },
        {
            "name": "login-ui",
            "type": "frontend",
            "files": ["src/components/LoginButton.tsx"],
            "dependencies": ["auth-setup", "callback-handler"],
            "risk": "low",
            "effort": "45min",
            "verification": "Login flow E2E passes in Playwright"
        }
    ]

    return {
        "feature": feature_request,
        "steps": steps,
        "dependencies": ["auth-setup → callback-handler → login-ui"],
        "risks": [
            {"description": "Provider OAuth scope changes", "mitigation": "Pin API version in config"}
        ],
        "estimated_time": "2h 15min",
        "parallelizable": ["auth-setup can start immediately"],
        "total_files": 3,
        "total_tests": 3
    }

if __name__ == "__main__":
    result = plan(" ".join(sys.argv[1:]))
    print(json.dumps(result, indent=2))