Borrowing it
Nothing to install: this file belongs to jacob-dietle/context-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jacob-dietle/context-os/main/.claude/skills/coordinated-agent-teams/SKILL.mdgit clone --depth 1 https://github.com/jacob-dietle/context-osWrote 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/jacob-dietle/context-os/coordinated-agent-teams)<a href="https://agentmods.dev/skills/jacob-dietle/context-os/coordinated-agent-teams"><img src="https://agentmods.dev/badge/skills/jacob-dietle/context-os/coordinated-agent-teams/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/jacob-dietle/context-os/coordinated-agent-teams"><img src="https://agentmods.dev/badge/skills/jacob-dietle/context-os/coordinated-agent-teams.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00114 | $0.03826 |
| Opus 5.5 | $0.00046 | $0.01530 |
| Sonnet 5.5 | $0.00023 | $0.00765 |
| Haiku 4.5 | $0.00011 | $0.00383 |
Grade A, and why
coordinated-agent-teams 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 — 400 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Coordinated Agent Teams
Methodology for decomposing implementation specs into agent DAGs with verified coordination patterns. Prevents integration surprises through contract-first boundaries, evidence-based parallelism decisions, and independent verification.
Meta-Principle: "The coordination overhead must cost less than the parallelism saves. If the DAG plan takes longer to write than sequential execution would take, just build it sequentially."
When to Use This Skill
Apply this skill when:
- Implementation spec exists and needs decomposition into agent tasks
- 3+ agents will work on interdependent modules
- Parallel execution would save meaningful wall-clock time
- Work spans multiple context windows or worktrees
- Integration risk is high (multiple agents writing to connected systems)
Do NOT use for:
- Solo agent implementations (< 3 agents)
- Embarrassingly parallel work (no dependencies, e.g., batch processing)
- Exploratory work without a spec (use
feature-planningfirst) - Work that fits in a single context window
The tell: If you're drawing arrows between agents on a napkin, use this skill. If you can describe the build as "just do steps 1-5 in order," don't.
The Coordination Overhead Test (Run First)
Before planning ANY multi-agent build, answer honestly:
Total estimated agent-hours: ___ hours
Estimated spec-writing time: ___ hours
Estimated merge/integration time: ___ hours
Coordination overhead: ___ hours (spec + merge)
Sequential build time: ___ hours (agent-hours, no parallelism)
Parallel build time: ___ hours (critical path + coordination)
Is parallel faster? ___ yes/no
By how much? ___ hours saved
If coordination overhead > 30% of sequential build time → just build sequentially.
The LinkedIn Pipeline succeeded with sequential agents (~15 hours, zero context loss). A transcript processing build succeeded with ~15 parallel agents (minutes end-to-end). The difference: transcript processing was embarrassingly parallel (disjoint batches). Pipeline phases were interdependent (sequential was correct).
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 · 400 lines · 114 tokens per session scan A 268bab1e4006
coordinated-agent-teams is a skill published in the GitHub repository jacob-dietle/context-os (115 stars, last pushed 2mo ago), licensed MIT. It adds 114 tokens to every session and 3,826 once invoked, about $0.0005 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 2026-08-30.
coordinated-agent-teams compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| coordinated-agent-teamsjacob-dietle/context-os | Skill | 115 | 2mo ago | MIT | A | 114 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |