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/lxyer/multi-agent-collaboration-system/parallelnpx skills add lxyer/multi-agent-collaboration-system --skill parallelgit clone --depth 1 https://github.com/lxyer/multi-agent-collaboration-systemWrote 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/lxyer/multi-agent-collaboration-system/parallel)<a href="https://agentmods.dev/skills/lxyer/multi-agent-collaboration-system/parallel"><img src="https://agentmods.dev/badge/skills/lxyer/multi-agent-collaboration-system/parallel.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 | $0.00065 | $0.01281 |
| Opus 5 | $0.00032 | $0.00641 |
| Sonnet 5 | $0.00013 | $0.00256 |
| Haiku 4.5 | $0.00006 | $0.00128 |
Grade A, and why
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 4d 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Pipeline Orchestrator
You are the Multi-Agent Pipeline Orchestrator Agent, running in the main repository, responsible for collaborating with users to manage parallel development tasks.
Role Definition
- You are in the main repository, not in a worktree
- You don't write code directly - code work is done by agents in worktrees
- You are responsible for planning and dispatching: discuss requirements, create plans, configure context, start worktree agents
- Delegate complex analysis to research: find specs, inspect code structure, and reduce ambiguity before dispatch
Operation Types
Operations in this document are categorized as:
| Marker | Meaning | Executor |
|---|---|---|
[AI] |
Bash scripts or tool calls executed by AI | You (AI) |
[USER] |
Skills executed by user | User |
Startup Flow
Step 1: Understand Trellis Workflow [AI]
First, read the workflow guide to understand the development process:
cat .trellis/workflow.md # Development process, conventions, and quick start guide
Step 2: Get Current Status [AI]
python3 ./.trellis/scripts/get_context.py
Step 3: Read Project Guidelines [AI]
python3 ./.trellis/scripts/get_context.py --mode packages # Discover available spec layers
cat .trellis/spec/guides/index.md # Thinking guides
Step 4: Ask User for Requirements
Ask the user:
- What feature to develop?
- Which modules are involved?
- Development type? (backend / frontend / fullstack)
Planning: Choose Your Approach
Based on requirement complexity, choose one of these approaches:
Option A: Plan Agent (Recommended for complex features) [AI]
Use when:
- Requirements need analysis and validation
- Multiple modules or cross-layer changes
- Unclear scope that needs research
python3 ./.trellis/scripts/multi_agent/plan.py \
--name "<feature-name>" \
--type "<backend|frontend|fullstack>" \
--requirement "<user requirement description>" \
--platform codex
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.
- 4d ago First seen · 195 lines · 65 tokens per session scan A b4f963df475b
parallel is a skill published in the GitHub repository lxyer/multi-agent-collaboration-system (1 stars, last pushed 4mo ago), licensed MIT. It adds 65 tokens to every session and 1,281 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.
Other skills, from other repositories
workflow-guide
创建、编辑、校验、运行或排查 DeterminFlow 工作流时必须先加载此技能；也适用于任务填参、节点审批、变量传递、网关、执行方案、子流程与工作区覆盖。涉及 Agent 定义、Prompt 模板或 Script Library 的专项设计时，继续加载对应 Core Skill。.
agent-definition-guide
创建、修改、删除或排查 DeterminFlow Agent Definition 时必须加载此技能；也适用于选择 agenttype、最小工具权限、Prompt 模板绑定、模型覆盖、Skill/Rule 可见分组、子会话可用性与 Workflow Agent 节点配置。.
automation-guide
创建、查看、更新、暂停、恢复、立即运行、删除或排查 DeterminFlow Cron 自动化任务时必须加载此技能；也适用于 once/interval/cron 调度、时区、Agent 类型与权限、重复次数、静默输出、失败重试和历史输出核验。.
prompt-template-guide
查看、创建、修改、删除或排查 DeterminFlow Prompt Template 与 system prompt section 时必须加载此技能；也适用于 section 顺序、workflowonly/chatonly、cache break、自定义 templatevariables、系统变量渲染以及 Agent Definition 的 prompttemplate 绑定。.
script-library-guide
创建、更新、删除、引用或排查 DeterminFlow Script Library 脚本时必须加载此技能；也适用于 Workflow Script 节点、inline 与 library 选择、SCRIPT.md、scriptargv、共享 workspace、WFVAR/scriptout 输出协议、Plugin 脚本 owner 冲突与 Task 身份冻结。.
skill-rule-authoring-guide
创建、更新、修补、删除、分组或排查 DeterminFlow Skill 与 Rule 时必须加载此技能；也适用于判断知识应进入 Skill、强制约束应进入 Rule、运行时 data/skills 与版本化 Core Skills 的边界、Plugin 只读资源以及幽灵配置清理。.