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.
npx skills add MotWakorb/ai-agent-dev-team --skill team-plangit clone --depth 1 https://github.com/MotWakorb/ai-agent-dev-teamWrote 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.
[](https://agentmods.dev/skills/motwakorb/ai-agent-dev-team/team-plan)<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/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.
<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="Reviewed on agentmods" width="80" height="20"></a>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 5.5 | $0.00014 | $0.01793 |
| Sonnet 5.5 | $0.00007 | $0.00897 |
| Haiku 4.5 | $0.00003 | $0.00448 |
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 2mo 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.
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.mdexists at the repo root. If missing, refuse to run and tell the PO:This project hasn't been onboarded yet. Run
/onboardfirst — it producesCOMPONENTS.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.mdfor 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
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.
- 2mo ago First seen · 411 lines · 35 tokens per session scan A cc3769468838
team-plan is a skill published in the GitHub repository MotWakorb/ai-agent-dev-team (2 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 4,483 once invoked, about $0.0001 per session on Opus 5.5. 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.
team-plan compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| team-planMotWakorb/ai-agent-dev-team | Skill | 2 | 2mo ago | MIT | A | 35 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |