Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
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.
[](https://agentmods.dev/skills/lxyer/multi-agent-collaboration-system/start)<a href="https://agentmods.dev/skills/lxyer/multi-agent-collaboration-system/start"><img src="https://agentmods.dev/badge/skills/lxyer/multi-agent-collaboration-system/start.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 | $0.00071 | $0.02360 |
| Opus 5 | $0.00036 | $0.01180 |
| Sonnet 5 | $0.00014 | $0.00472 |
| Haiku 4.5 | $0.00007 | $0.00236 |
Grade A, and why
start 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.
How it starts
The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Start Session
Initialize your AI development session and begin working on tasks.
Operation Types
| Marker | Meaning | Executor |
|---|---|---|
[AI] |
Bash scripts or tool calls executed by AI | You (AI) |
[USER] |
Skills executed by user | User |
Initialization [AI]
Step 1: Understand Development Workflow
First, read the workflow guide to understand the development process:
cat .trellis/workflow.md
Follow the instructions in workflow.md - it contains:
- Core principles (Read Before Write, Follow Standards, etc.)
- File system structure
- Development process
- Best practices
Step 2: Get Current Context
python3 ./.trellis/scripts/get_context.py
This shows: developer identity, git status, current task (if any), active tasks.
Step 3: Read Guidelines Index
python3 ./.trellis/scripts/get_context.py --mode packages
This shows available packages and their spec layers. Read the relevant spec indexes:
cat .trellis/spec/<package>/<layer>/index.md # Package-specific guidelines
cat .trellis/spec/guides/index.md # Thinking guides (always read)
Important: The index files are navigation — they list the actual guideline files (e.g.,
error-handling.md,conventions.md,mock-strategies.md). At this step, just read the indexes to understand what's available. When you start actual development, you MUST go back and read the specific guideline files relevant to your task, as listed in the index's Pre-Development Checklist.
Step 4: Report and Ask
Report what you learned and ask: "What would you like to work on?"
Task Classification
When user describes a task, classify it:
| Type | Criteria | Workflow |
|---|---|---|
| Question | User asks about code, architecture, or how something works | Answer directly |
| Trivial Fix | Typo fix, comment update, single-line change, < 5 minutes | Direct Edit |
| Simple Task | Clear goal, 1-2 files, well-defined scope | Quick confirm → Task Workflow |
| Complex Task | Vague goal, multiple files, architectural decisions | Brainstorm → Task Workflow |
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 · 352 lines · 71 tokens per session scan A 8853e4ddc168
start is a skill published in the GitHub repository lxyer/multi-agent-collaboration-system (1 stars, last pushed 4mo ago), licensed MIT. It adds 71 tokens to every session and 2,360 once invoked, about $0.0004 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。.