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 aegntic/compound-engineering --skill orchestrating-swarmsgit clone --depth 1 https://github.com/aegntic/compound-engineeringWrote 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/aegntic/compound-engineering/orchestrating-swarms)<a href="https://agentmods.dev/skills/aegntic/compound-engineering/orchestrating-swarms"><img src="https://agentmods.dev/badge/skills/aegntic/compound-engineering/orchestrating-swarms.svg" alt="Measured on agentmods" 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 | $0.00067 | $0.00733 |
| Opus 5 | $0.00034 | $0.00367 |
| Sonnet 5 | $0.00013 | $0.00147 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
orchestrating-swarms 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 8d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
orchestrating-swarms Skill
Use swarms when the work has real parallelism, specialist boundaries, or dependency staging. If one agent can finish faster than the coordination overhead, stay single-threaded.
When to use
- Parallel research, review, or implementation tracks.
- Pipeline work where one stage unlocks another.
- Large tasks that benefit from specialist prompts and explicit handoffs.
- Situations where a leader must merge findings from multiple workers.
Workflow
- Decide whether you need a swarm. Prefer one agent for linear work, tiny diffs, or tasks with one obvious path.
- Break the job into independent units with owners, dependencies, and a concrete finish condition for each unit.
- Choose the right worker shape: short-lived subagents for one-off work, persistent teammates only when shared coordination is necessary.
- Give every worker full context: goal, scope, constraints, expected output, and what not to touch.
- Track progress, unblock dependencies, merge results, verify the combined outcome, then shut the swarm down cleanly.
Operating guide
Subagent vs teammate
- Use a normal Task/subagent when you want a focused result returned directly.
- Use a teammate only when the worker needs a shared queue, persistent inbox messaging, or multi-step coordination.
- Keep the team small. Extra workers are justified only when they remove wall-clock time or increase specialist quality.
Task design
- Write tasks as outcomes, not vague topics.
- Keep scopes non-overlapping unless the assignment is an explicit cross-check.
- Prefer DAG-style dependencies over ad hoc sequencing.
- State what evidence counts as done: files changed, tests run, findings delivered, screenshots captured, or open questions listed.
Message contract
Have workers report in a terse, machine-checkable shape:
status: ready | blocked | donescope: what they ownedevidence: commands, files, screenshots, or metricshandoff: what the next worker or leader must dorisks: unresolved concerns
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.
- 8d ago First seen · 73 lines · 67 tokens per session scan A fdcab1224310
orchestrating-swarms is a skill published in the GitHub repository aegntic/compound-engineering (2 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 733 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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