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 agentmods add skills/xiqin/loom/loom-dispatching-parallel-agentsnpx skills add xiqin/loom --skill loom-dispatching-parallel-agentsgit clone --depth 1 https://github.com/xiqin/loomWrote 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/xiqin/loom/loom-dispatching-parallel-agents)<a href="https://agentmods.dev/skills/xiqin/loom/loom-dispatching-parallel-agents"><img src="https://agentmods.dev/badge/skills/xiqin/loom/loom-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 | $0.00042 | $0.00895 |
| Opus 5 | $0.00021 | $0.00447 |
| Sonnet 5 | $0.00008 | $0.00179 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
loom-dispatching-parallel-agents 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 5d 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.
What it actually says
并行派发
适用场景
当多个任务之间没有依赖关系时,可以并行执行以提高效率。
限制条件: 仅当多个 task 之间无依赖、无共享文件修改时才可并行。若 task 间有文件冲突,必须退回到 loom-subagent-driven-development 的串行模式。
模型选择: 并行任务通常使用便宜模型(cheap model),因为它们大多是机械实现任务。
会话边界: 每个并行任务必须使用独立 fresh subagent。派发前必须有确认过的 task 边界和必要 handoff;不要把主会话原始上下文复制给所有 subagent。
执行流程
Step 1:分析任务依赖
- 读取
specs/<date+feature>/plan.md中的 Task 概览,读取specs/<date+feature>/tasks/目录下的各 task 文件 - 分析任务之间的依赖关系
- 找出可并行的任务组
## 依赖关系分析
| Task | 依赖 | 可并行组 | 复杂度 |
| -------------------- | --------- | -------- | ------ |
| Task 1 (Module A) | 无 | 组 1 | 简单 |
| Task 2 (Module B) | 无 | 组 1 | 简单 |
| Task 3 (Module C) | Task 1 | 组 2 | 中等 |
| Task 4 (Module D) | Task 2 | 组 2 | 中等 |
| Task 5 (Integration) | Task 3, 4 | 组 3 | 复杂 |
Step 2:创建并行组
将可并行的任务分组:
组 1: [Task 1, Task 2] → 并行执行(使用 cheap model)
组 2: [Task 3, Task 4] → 等组 1 完成后并行执行(使用 standard model)
组 3: [Task 5] → 等组 2 完成后执行(使用 capable model)
Step 3:并行派发
对同一组的任务,同时派发 subagent:
派发 Task 1 subagent (cheap) ──→ ┐
├→ 全部完成后进入组 2
派发 Task 2 subagent (cheap) ──→ ┘
模型选择:并行任务通常使用便宜模型(机械实现),集成/复杂任务使用标准/强模型。
并行派发模板
并行派发以下独立任务(使用 cheap model):
## Task N: <任务名>
<完整 task 内容>
## Task M: <任务名>
<完整 task 内容>
## 约束
- 每个 subagent 独立工作,互不干扰
- 如发现与其他任务有冲突,立即报告
- 完成后输出创建/修改的文件列表
- 使用 cheap model(除非任务复杂)
约束
- 只有真正独立的任务才能并行
- 并行任务不能修改同一文件
- 必须等待所有并行 subagent 完成后才能继续
- 并行任务的结果需要合并验证
- 根据任务复杂度选择模型(cheap/standard/capable)
- 每个并行 subagent 只接收自己的 task、必要 spec 片段、subagent-context 和相关 handoff
What ships with it
1 file 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.
- 5d ago First seen · 91 lines · 42 tokens per session scan A 4056a815fa44
loom-dispatching-parallel-agents is a skill published in the GitHub repository xiqin/loom (5 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 895 once invoked, about $0.0002 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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