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 momentmaker/kaijutsu --skill dispatch-parallelgit clone --depth 1 https://github.com/momentmaker/kaijutsuWrote 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/momentmaker/kaijutsu/dispatch-parallel)<a href="https://agentmods.dev/skills/momentmaker/kaijutsu/dispatch-parallel"><img src="https://agentmods.dev/badge/skills/momentmaker/kaijutsu/dispatch-parallel/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/momentmaker/kaijutsu/dispatch-parallel"><img src="https://agentmods.dev/badge/skills/momentmaker/kaijutsu/dispatch-parallel.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 | $0.00067 | $0.01118 |
| Opus 5.5 | $0.00027 | $0.00447 |
| Sonnet 5.5 | $0.00013 | $0.00224 |
| Haiku 4.5 | $0.00007 | $0.00112 |
Grade A, and why
dispatch-parallel 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dispatch-parallel
Sequential subtask execution wastes wall-clock time when subtasks are independent. This primitive captures the pattern of "decompose, dispatch, collect, synthesize" so other skills don't have to restate it.
When to invoke
- A parent skill needs to run K independent passes / lenses / searches
- The subtasks are read-only or write to disjoint state
- Per-subtask cost dominates over coordination cost
- The outputs can be merged or deduplicated post-hoc
Do not invoke when:
- Subtasks share mutable state
- Order matters (use sequential composition instead)
- The work is small enough that one agent finishes faster than spawning N
The pattern
1. Decompose
Split the parent task into K self-contained subtasks. Each subtask must:
- Have a clear input (what to look at)
- Have a clear lens (what to look for)
- Produce a structured output (list of findings, list of facts, etc.)
- Be runnable without seeing the other subtasks' outputs
If you can't write a one-paragraph brief for a subtask, it's not decomposed enough.
2. Dispatch (per agent platform)
| Agent | Subagent primitive |
|---|---|
| Claude Code | Task tool with subagent_type: general-purpose (or specialized) — multiple Task calls in a single response run in parallel |
| OpenAI Codex CLI | codex agents spawn (where supported) or sequential with explicit context resets |
| Google Antigravity CLI | activate_skill + delegated subtasks where supported; otherwise sequential |
When the platform supports parallel calls in a single turn, use them. Otherwise, fall back to sequential — still apply the decompose/collect pattern, just lose the wall-clock win.
3. Collect
Each subagent returns a structured output. Aggregate into a single list. Tag every item with which subagent produced it (subagent_id, lens, etc.) so dedup and synthesis can preserve provenance.
4. Synthesize
Pick the synthesis pattern based on what the parent skill needs:
- Merge — take the union of all outputs. Use when each subagent searches disjoint territory.
- Dedupe — group equivalent items, keep the highest-confidence variant. Use for findings where the same issue might be spotted by multiple lenses.
- Vote — items present in N+ subagent outputs are high-confidence. Use for adversarial review where agreement = signal.
- Pick best — when each subagent produced a competing answer (e.g., draft prose), score them and pick. Often paired with
multi-model-synth.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 98 lines · 67 tokens per session scan A addc1ca29334
dispatch-parallel is a skill published in the GitHub repository momentmaker/kaijutsu (3 stars, last pushed 8d ago), licensed MIT. It adds 67 tokens to every session and 1,118 once invoked, about $0.0003 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 2026-08-31.
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