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 Goktug/ai-crew --skill team-leadgit clone --depth 1 https://github.com/Goktug/ai-crewWrote 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/goktug/ai-crew/team-lead)<a href="https://agentmods.dev/skills/goktug/ai-crew/team-lead"><img src="https://agentmods.dev/badge/skills/goktug/ai-crew/team-lead/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/goktug/ai-crew/team-lead"><img src="https://agentmods.dev/badge/skills/goktug/ai-crew/team-lead.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 | $0.00054 | $0.04389 |
| Opus 5 | $0.00027 | $0.02194 |
| Sonnet 5 | $0.00011 | $0.00878 |
| Haiku 4.5 | $0.00005 | $0.00439 |
Grade A, and why
team-lead 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 10d 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Team Lead
Overview
Orchestrate a full feature lifecycle inline, using reference-based dispatch to cheap subagents only where parallelism pays. The team-lead runs in the Fable 5 main session as the coordinator in a plan-big-execute-small split: the frontier model does the planning, judgment, and synthesis; cheap workers do the token-heavy mechanical work in their own context windows. It walks every task through nine phases and does the heavy thinking — intake, spec, plan, verify, review, ship — by reading the relevant agent-skills SKILL.md files itself. It dispatches subagents only for two narrow jobs: web-researcher (Haiku) for one focused web question, and a developer subagent (sonnet-developer, opus-developer, or fable-developer) for one atomized build task — picked per task by the complexity flag in plan.md.
Strict 1-level dispatch: subagents never spawn subagents. The sonnet-developer, opus-developer, fable-developer, and web-researcher agents have no Agent or Task tools by configuration.
Coordinator economics: plan big, execute small
The model split (Fable 5 lead; Opus, Sonnet, and Haiku workers) only pays if the lead behaves like a coordinator. Four operating rules:
- Never pull token-heavy raw material into the main session when a dispatch can read it and report distilled findings. Reference-based dispatch is the cost boundary: the heavy tokens bill at the worker's rate, not Fable's. The exceptions are by design — the lead reads skills, the spec/plan it authors, and the diff at Review.
- Fan out, then wait for everything. Dispatch a wave's independent tasks in a single message and draw no conclusion until every dispatch in the wave has reported.
- Infrastructure failure ≠ task failure. A subagent that dies on a rate limit, timeout, or crash instead of returning PASS/FAIL is re-dispatched fresh with the same prompt. That retry does not consume the max-3 fix-loop budget — the budget is for real verify/review failures only.
- Delegation has a floor cost. Each dispatch pays fixed overhead (agent startup, skill reads, spec/plan slices). Atomize until a task needs no judgment calls, then stop — splitting further raises the bill without raising quality.
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.
- 10d ago First seen · 240 lines · 54 tokens per session scan A 8533aa4c56a1
team-lead is a skill published in the GitHub repository Goktug/ai-crew (6 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 4,389 once invoked, about $0.0003 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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