A planning session in which ten different project roles review the same brief, then debate disagreements and produce a shared plan.

Use it to analyze a project brief from multiple perspectives, compare concerns, resolve conflicts, and prepare decisions for the project owner.

It exposes conflicting assumptions and important decisions before implementation begins.

team-plan — Skill for Claude Code by MotWakorb

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 MotWakorb/ai-agent-dev-team --skill team-plan

Clone the repo

git clone --depth 1 https://github.com/MotWakorb/ai-agent-dev-team

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/motwakorb/ai-agent-dev-team/team-plan.svg)](https://agentmods.dev/skills/motwakorb/ai-agent-dev-team/team-plan)

Your own site

<a href="https://agentmods.dev/skills/motwakorb/ai-agent-dev-team/team-plan"><img src="https://agentmods.dev/badge/skills/motwakorb/ai-agent-dev-team/team-plan.svg" alt="Measured on agentmods" height="20"></a>

Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,483 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.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00035	$0.04483
Opus 5	$0.00017	$0.02242
Sonnet 5	$0.00007	$0.00897
Haiku 4.5	$0.00003	$0.00448

Measured 8d ago against content hash cc3769468838, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

team-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 8d 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.

team-plan/SKILL.md · 411 lines

How it starts

The opening of the file, as written. The whole thing — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Team Planning Session

This skill orchestrates a parallel planning session across all ten personas. Each persona analyzes the project brief independently, then the team comes together to debate, disagree, and produce a unified plan with decision points for the PO.

Preflight: Verify Onboarding & Effective Tier

Before any other step, verify deployment-tier setup. Defaulting to enterprise rigor across the board is the failure mode this preflight prevents.

Check COMPONENTS.md exists at the repo root. If missing, refuse to run and tell the PO:

This project hasn't been onboarded yet. Run /onboard first — it produces COMPONENTS.md, which records each component's deployment tier (home-lab / small-team / startup / enterprise). Without it, personas calibrate to enterprise rigor across the board. See _shared/deployment-tier.md for the tier model.

Do not proceed.
Identify in-scope components for this run (from the brief).
Look up tiers in COMPONENTS.md. If an in-scope component is missing, ask the PO to add it (with reasoning) before proceeding.
Resolve cross-tier conflicts using strictest-wins by default. If applying that across the board produces clearly wasteful work, surface it as a decision per _shared/deployment-tier.md.

Inject tier context into every agent prompt. Every prompt below must additionally include:

Read ~/.claude/skills/_shared/deployment-tier.md.
In-scope components and tiers: [component] ([tier]), ...
Effective tier for this work: [tier]
Calibrate your recommendations to the effective tier. Do not invent enterprise practices for home-lab components. If you would recommend something at a higher tier, frame it as "at a higher tier I would also recommend X" rather than presenting it as a baseline expectation.

Model Selection

When spawning agents, pass model: explicitly per _shared/orchestration.md (Agent Model Selection). For this skill:

Quick mode (all personas): sonnet
Full mode — security-engineer, it-architect, database-engineer: opus — decisions made here are sticky and expensive to undo
Full mode — other personas: sonnet

Read the full file on GitHub · 411 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.

8d ago First seen · 411 lines · 35 tokens per session scan A cc3769468838

Subscribe to this mod's changes

team-plan is a skill published in the GitHub repository MotWakorb/ai-agent-dev-team (2 stars, last pushed 25d ago), licensed MIT. It adds 35 tokens to every session and 4,483 once invoked, about $0.0002 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 2026-08-31.

Planning Project management

recipe-create-meet-space

Create a Google Meet meeting space and share the join link.

googleworkspace/cli · 18 tokens

workthreads

SpecStory Workthreads - a weekly work-thread rollup across a team's repos from SpecStory coding histories (any agent - Claude Code, Codex, Cursor, Gemini, and more). It groups the window's sessions into threads of work per project and labels each new / open / recently closed, so a lead sees what shipped, what is still…

specstoryai/getspecstory · 126 tokens

atmos-config

Atmos root configuration: atmos.yaml discovery, precedence, deep merging, basepath, imports, minimal bootstrap, and routing to narrower Atmos skills.

cloudposse/atmos · 31 tokens

story-readiness

Validate that a story file is implementation-ready. Checks for embedded GDD requirements, ADR references, engine notes, clear acceptance criteria, and no open design questions. Produces READY / NEEDS WORK / BLOCKED verdict with specific gaps. Use when user says 'is this story ready', 'can I start on this story', 'is…

Donchitos/Claude-Code-Game-Studios · 77 tokens

autotask-creator

Rules for automation CRUD from the group-chat commander. The commander does not call mutation tools and does not edit cloud/autotasks files directly. It emits one or more top-level ... containers in its final text; the bus parses and applies them after the turn.

Orkas-AI/Orkas · 5 tokens

monorepo-management

Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.

wshobson/agents · 54 tokens