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 brunob54/superpowers-orchestrator --skill dispatching-parallel-agentsgit clone --depth 1 https://github.com/brunob54/superpowers-orchestratorWrote 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/brunob54/superpowers-orchestrator/dispatching-parallel-agents)<a href="https://agentmods.dev/skills/brunob54/superpowers-orchestrator/dispatching-parallel-agents"><img src="https://agentmods.dev/badge/skills/brunob54/superpowers-orchestrator/dispatching-parallel-agents.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 | $0.00061 | $0.01004 |
| Opus 5 | $0.00030 | $0.00502 |
| Sonnet 5 | $0.00012 | $0.00201 |
| Haiku 4.5 | $0.00006 | $0.00100 |
Grade A, and why
dispatching-parallel-agents scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- The task is content relay — fetching raw content (file contents, web pages, API responses) to bring back to the parent session. Agent results are compressed; raw content will be lost. Fetch it directly instead with Rea How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dispatching Parallel Agents
Use parallel subagents only for truly independent work.
Decision Check
Use parallel dispatch when all are true:
- Problems have separate root causes.
- Tasks do not edit the same files.
- Tasks do not require shared intermediate state.
If any condition fails, run sequentially.
Do not use when:
- Failures are related — fixing one might fix others.
- The task is exploratory — you don't know what's broken yet.
- Agents would edit the same files or shared resources.
- Understanding the problem requires seeing the full system state.
- The task is content relay — fetching raw content (file contents, web pages, API responses) to bring back to the parent session. Agent results are compressed; raw content will be lost. Fetch it directly instead with Read (local files) or
curl(URLs).
Procedure
- Split work into independent domains.
- Write one focused prompt per domain.
- Dispatch all agents in a single message with multiple parallel Agent tool calls. Do not dispatch sequentially across multiple messages — staggered dispatch delays start times, burns cache TTL, and undermines the parallelism.
- Collect summaries and changed files.
- Resolve conflicts between summaries and changed files.
- Run integration verification: execute the full project test suite plus any cross-domain checks. Do not mark the wave complete until integration passes.
Context Isolation
Never forward parent session context or history to subagents. Construct each subagent's prompt from scratch using only the items listed below. Subagents must not receive conversation history, prior reasoning chains, or context from other subagent runs.
Why this is also the cache-optimal approach: All subagents share the same system prompt prefix, which is cached by the API. By keeping each subagent's input as [cached system prompt] + [small unique task prompt], every agent gets a cache hit on the heavy shared prefix and only pays full price for its small task-specific tail. Forwarding parent conversation history would make each subagent's prefix unique and large, breaking cache sharing and multiplying input token costs.
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.
- 6d ago First seen · 98 lines · 61 tokens per session scan A 439b8eb05268
dispatching-parallel-agents is a skill published in the GitHub repository brunob54/superpowers-orchestrator (3 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 1,004 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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