loom-dispatching-parallel-agents

loom-dispatching-parallel-agents is a skill for Claude Code from xiqin/loom. It costs 42 tokens per session (895 once invoked), scanned A, original, MIT.

A procedure for sending independent coding tasks to separate agents at the same time when they do not edit the same files or depend on one another.

In plain words
What is it for?
Grouping tasks by dependencies, choosing agents for each group, and dispatching independent work in parallel before integration.
Why use it?
It can shorten the work by letting unrelated tasks proceed concurrently while avoiding conflicting changes.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: mentions subagents.

Part of the loom-engineering plugin — 22 skills shipped together

Install

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.

agentmods
npx agentmods add skills/xiqin/loom/loom-dispatching-parallel-agents
Any agent
npx skills add xiqin/loom --skill loom-dispatching-parallel-agents
Clone the repo
git clone --depth 1 https://github.com/xiqin/loom

Made for: Claude Code.

Or install loom-engineering, the plugin that ships this one along with the rest of its 22 skills.

Wrote 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.

agentmods badge for loom-dispatching-parallel-agents

README.md
[![agentmods](https://agentmods.dev/badge/skills/xiqin/loom/loom-dispatching-parallel-agents.svg)](https://agentmods.dev/skills/xiqin/loom/loom-dispatching-parallel-agents)
Your own site
<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>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 895 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 5d ago against content hash 4056a815fa44, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

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.

skills/loom-dispatching-parallel-agents/SKILL.md · 91 lines

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

  1. 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
  2. 分析任务之间的依赖关系
  3. 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
Files

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.

Changes

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.

  1. 5d ago First seen · 91 lines · 42 tokens per session scan A 4056a815fa44

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens