Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/yeaight7/agent-powerupsnpx agentmods add skills/yeaight7/agent-powerups/subagent-team-orchestrationWrote 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/yeaight7/agent-powerups/subagent-team-orchestration)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/subagent-team-orchestration"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/subagent-team-orchestration/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/yeaight7/agent-powerups/subagent-team-orchestration"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/subagent-team-orchestration.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 | $0.00050 | $0.01111 |
| Opus 5.5 | $0.00020 | $0.00444 |
| Sonnet 5.5 | $0.00010 | $0.00222 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
subagent-team-orchestration 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 1mo 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Turn a multi-part task into a controlled subagent workflow. Plan the task graph before spawning workers, launch only dependency-safe work in parallel, keep one owner responsible for cross-wave coordination, and treat reviewer output as input to a fix loop -- not as a final report.
When to Use
- The task splits cleanly into planning, implementation, review, and verification stages
- Different specialties are needed across the same delivery flow
- Parallel work is possible, but dependencies still matter
- A single agent would create too much latency or too much context bloat
Do NOT use when the task fits one focused agent, when work is purely sequential with no parallelizable branches, or when requirements are still vague enough that clarification should happen first.
Inputs
- The objective, hard constraints, and the verification bar that defines "done"
- The list of subtasks and which ones depend on others
- The set of specialist roles available to delegate to
Core Rules
- Plan the task graph before spawning workers.
- Separate stages: research, planning, implementation, verification, fix loop.
- Launch only dependency-safe work in parallel.
- Keep one owner responsible for cross-wave coordination.
- Treat reviewer output as input to a fix loop, not as a final report.
- No auto-commit, auto-push, auto-merge, or hidden global mutation.
- If external side effects are involved, inspect state first, dry-run when available, summarize impact, then ask for approval.
Workflow
-
Frame the objective. Write the target outcome, constraints, and verification bar in one short block. Identify which tasks must happen locally before delegation.
-
Build the task graph. Break work into concrete subtasks, mark dependencies explicitly, and group tasks by stage and expected owner type. Decision rule: two subtasks belong in the same wave only if neither reads or writes the other's output and they touch disjoint files.
-
Create execution waves. Sequence the dependency graph into ordered batches:
- Wave 0: context gathering, planning, and setup
- Wave 1..N: independent implementation batches
- Final wave: review, testing, and cleanup
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.
- 1mo ago First seen · 89 lines · 50 tokens per session scan A dbe9bd035f88
subagent-team-orchestration is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 19d ago), licensed Apache-2.0. It adds 50 tokens to every session and 1,111 once invoked, about $0.0002 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 2026-09-14.
Other skills, from other repositories
github-issues
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github
Use the GitHub CLI (gh) to inspect and manage repositories, pull requests, issues, workflows, releases, and API calls from the terminal. Use when a user asks to use gh/GitHub CLI, run GitHub repo or PR workflows, automate GitHub Actions from shell, or troubleshoot gh authentication and configuration.
pipeline-conductor
Operating procedure for the kirocrew-pipeline-conductor agent. Use when a pipeline conductor session is seeded, inspected or debugged. You MANAGE one issue/PR pipeline on one repo: two columns per item (session working? item solved?), send intent, never re-derive a red yourself.
babysit
Use when a user asks to babysit, monitor, keep checking, keep an eye on, or report when a pull request, CI run, ticket, deployment, or other changing target reaches an outcome.
goal-loop
Bootstrap a goal-driven self-improving AutoNudge loop from a goal plus an anchor directory, then run the board autonomously until the Definition of Done is met.
goal-ledger-conductor
Deprecated alias of goal-conductor, removed next release. The work-ledger conducting procedure now lives in goal-conductor; read that skill instead.