team-create-plan

team-create-plan is a skill for Claude Code from mhylle/claude-skills-collection. It costs 82 tokens per session (2,659 once invoked), scanned A, original, MIT.

A planning workflow that uses three separate roles—an architect, a risk analyst, and a researcher—to examine a complex software change before producing an implementation plan.

In plain words
What is it for?
Use it to plan complex features, high-stakes changes, or work where several technical approaches could be valid.
Why use it?
It reduces the chance of choosing a weak design by comparing alternatives and checking the plan for risks and feasibility first.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions subagents; positional $N argument.

Part of the devflow plugin — 38 skills, 13 agents, 5 hooks shipped together

Good fit Use it to plan complex features, high-stakes changes, or work where several technical approaches could be valid.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mhylle/claude-skills-collection/team-create-plan
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add mhylle/claude-skills-collection --skill team-create-plan
Clone the repo
git clone --depth 1 https://github.com/mhylle/claude-skills-collection

Made for: Claude Code.

Or install devflow, the plugin that ships this one along with the rest of its 38 skills, 13 agents, 5 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for team-create-plan

README.md
[![agentmods](https://agentmods.dev/badge/skills/mhylle/claude-skills-collection/team-create-plan/github.svg)](https://agentmods.dev/skills/mhylle/claude-skills-collection/team-create-plan)
Your own site
<a href="https://agentmods.dev/skills/mhylle/claude-skills-collection/team-create-plan"><img src="https://agentmods.dev/badge/skills/mhylle/claude-skills-collection/team-create-plan/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for team-create-plan

Your own site · 80×15
<a href="https://agentmods.dev/skills/mhylle/claude-skills-collection/team-create-plan"><img src="https://agentmods.dev/badge/skills/mhylle/claude-skills-collection/team-create-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,659 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00082 $0.02659
Opus 5 $0.00041 $0.01329
Sonnet 5 $0.00016 $0.00532
Haiku 4.5 $0.00008 $0.00266

Measured 11d ago against content hash 7c03534db0b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

team-create-plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/team-create-plan/SKILL.md · 344 lines

How it starts

The opening of the file, as written. The whole thing — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Team Create Plan

Overview

This skill creates implementation plans through an agent team where independent teammates explore competing designs and challenge each other. Unlike the single-agent create-plan which researches and proposes serially, this skill spawns three specialists who work in parallel and debate trade-offs before the lead synthesizes a plan.

When to use this vs create-plan:

  • Use create-plan for straightforward plans with clear requirements (~15-20K tokens)
  • Use team-create-plan for complex designs with multiple valid approaches, high-stakes decisions, or when adversarial review of the plan itself matters (~40-60K tokens)

Reference: See references/team-lifecycle.md for the standard team lifecycle pattern.

Initial Response

When this skill is invoked, respond:

"I'll set up a planning team to explore design options for your feature. An Architect will propose approaches, a Risk Analyst will stress-test them, and a Researcher will validate feasibility against the codebase. Share what you need built, and I'll ask clarifying questions before launching the team."

Workflow (7 Phases)

Phase 1: Requirements Capture

Parse the user's request to identify:

Element Description
Task description What needs to be implemented
Context files Relevant existing code or documentation
Constraints Timeline, technology, or scope limitations
Brainstorm reference If a brainstorm output exists, read it for context

If a brainstorm path is provided as argument ($0), read it fully for pre-existing analysis.

Phase 2: Socratic Clarification (Lead-Driven)

Before spawning the team, the lead conducts focused clarification with the user. Teammates need a well-defined problem to be effective.

Present initial understanding from any referenced files, then ask:

  • What is the most important quality of this implementation? (performance, correctness, maintainability, speed-to-ship)
  • Are there constraints the codebase won't reveal? (timeline, team skills, deployment environment)
  • Any approaches you've already considered or rejected?

Read the full file on GitHub · 344 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 344 lines · 82 tokens per session scan A 7c03534db0b9

Subscribe to this mod's changes

team-create-plan is a skill published in the GitHub repository mhylle/claude-skills-collection (18 stars, last pushed 8d ago), licensed MIT. It adds 82 tokens to every session and 2,659 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens