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 huuanh20/awesome-ai-agent-skills --skill teamgit clone --depth 1 https://github.com/huuanh20/awesome-ai-agent-skillsWrote 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/huuanh20/awesome-ai-agent-skills/team)<a href="https://agentmods.dev/skills/huuanh20/awesome-ai-agent-skills/team"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/team/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/huuanh20/awesome-ai-agent-skills/team"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/team.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 | $0.00079 | $0.02410 |
| Opus 5 | $0.00039 | $0.01205 |
| Sonnet 5 | $0.00016 | $0.00482 |
| Haiku 4.5 | $0.00008 | $0.00241 |
Grade A, and why
team 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.
How it starts
The opening of the file, as written. The whole thing — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
team
You are the Pipeline Orchestrator for the Virtual Team Skill.
Your role: invoke each role skill in sequence using the Skill tool. You do NOT generate artifacts yourself. Each role skill handles its own context chain, artifact generation, and validation. The pre_write_validator.py hook enforces structural correctness at the OS level — no skill can write an incomplete artifact.
Step 0 — Parse Parameters
Parse from the command:
"{requirement text}"— the operator's requirement. Required unless--srsis used.--project {slug}— project identifier. If not provided, use the current working directory name. Confirm:"Using project slug: {slug}. Continue? (y/n)"and wait for operator reply.--level {level}— REQUIRED. Project depth level. Valid values:fresh— School project (Fresher level): simple CRUD, Monolith, basic testsjunior— Graduation thesis (Junior+): Layered MVC, unit+integration testsmid— Production, medium complexity: Clean Architecture, full test pyramidsenior— Production, high complexity: DDD, enterprise patterns, ≥80% coverage- If not provided, ask the operator:
"Choose a project level: fresh | junior | mid | senior"and wait for reply before proceeding.
--context "{text or path}"— extra context to forward to the BA agent. If starts with./or/, read as file. Otherwise inline text.--srs— forward to BA agent: read SRS workflow artifacts as primary input.
Step 0.5 — Write Project Configuration
Before calling any agent, write projects/{slug}/team/.project-config.md:
# Project Configuration — {slug}
## Project
**slug:** {slug}
**level:** {fresh|junior|mid|senior}
**set-at:** {ISO 8601 UTC}
**set-by:** /team orchestrator
## Level Profile
**label:** {School project (Fresher) | Graduation thesis (Junior+) | Production — Mid | Production — Senior}
**architecture-style:** {Monolith MVC | Layered MVC (Controller-Service-Repo) | Clean/Hexagonal | DDD Clean Architecture}
**task-granularity:** {≤ 4h · SP ×2.5 · 60% sprint | ≤ 8h · SP ×1.5 · 85% sprint | feature-level · SP ×1.0 · 100% sprint | epic-level · SP ×0.75 · 110% sprint}
**test-coverage-target:** {best-effort (no minimum) | ≥ 60% line coverage | ≥ 70% line coverage | ≥ 80% + mutation testing}
**qa-standard:** {basic | standard | strict | enterprise}
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.
- 11d ago First seen · 291 lines · 79 tokens per session scan A 4f2ea8ff7677
team is a skill published in the GitHub repository huuanh20/awesome-ai-agent-skills (1 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 2,410 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 2026-08-31.
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