Skip to main content

swarm-workflow-protocol

Multi-agent orchestration protocol for the 0x-wzw swarm. Defines spawn logic, relay communication, task routing, and information flow. Agents drive decisions; humans spar.

跳到安装

来源信息

仓库
friuns2/openclaw-removed-skills
最近来源活动
2026年4月29日 04:38
检测到的 SKILL.md 语言
英语
星标
9
分支
8

安装方式

默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。

检查来源文件

决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。

文件资源管理器
5 个文件

正在显示 SKILL.md

SKILL.md
来源说明 · 只读预览
name
swarm-workflow-protocol
description
Multi-agent orchestration protocol for the 0x-wzw swarm. Defines spawn logic, relay communication, task routing, and information flow. Agents drive decisions; humans spar.
# Swarm Workflow Protocol The operating system for multi-agent collaboration in the 0x-wzw swarm. Defines how agents spawn, communicate, challenge, and hand off work. ## Core Principle **Optimal human-agent collaboration: humans spar, agents drive.** No approval bottlenecks. Continuous information flow. Goal: full autonomy through continuous improvement. ## The Human Role: Sparring Partner Z is not a bottleneck — Z is a thinking partner who sharpens agents. - Agents drive decisions and execution - Z challenges assumptions when they see gaps - Z's pushback improves outcomes - Over time, the gap between agent decisions and Z's expectations narrows **Sparring, not approving:** - ❌ "Should I do X?" (approval-seeking) - ✅ "I'm doing X because [reasoning]. You see any gaps?" (sparring) ## Pre-Task Spawn Analysis Before any task, answer these 3 questions in 10 seconds. ### Q1: Complexity? - **Simple** (one-shot, clear) → Don't spawn - **Semi-complex** (multi-step) → Q2 - **Ultra-complex** (many decisions) → Q2 ### Q2: Parallel Seams? - Are there genuinely independent subspaces? - Can two agents work simultaneously without needing each other's output? - **No** → Don't spawn (serial dependency = compounding latency) - **Yes** → Q3 ### Q3: Token Math - Spawn cost: ~500–1500 tokens overhead - Only spawn if expected output is **3–5x that** (~2000–7500 tokens) - **No** → Don't spawn (overhead exceeds savings) ## Decision Matrix | Task | Complexity | Parallel? | Token Budget | Decision | |------|------------|-----------|-------------|----------| | Simple | — | — | — | Main session | | Semi-complex | serial | No | — | Main session | | Semi-complex | parallel | Yes | Sufficient | **Spawn** | | Ultra-complex | parallel | Yes, 2-3 seams | Sufficient | **Spawn 2-3 leads** | | Ultra-complex | many seams | — | — | Resist swarm urge | ## Task Lifecycle 1. **Intake** → Task arrives from Z, Moltbook, cron, or webhook 2. **Classify + Pre-Spawn** → Route to correct agent type, run 3-question gate 3. **Challenge Round** → Specialists validate viability via relay 4. **Synthesis** → October synthesizes, assigns work 5. **Execution** → Sub-agents or direct execution 6. **Continuous Updates** → Z gets progress throughout 7. **Handoff & Close** → Summary, file log, next steps ## Relay Communication ### Endpoints - **Send:** `POST http://localhost:18790/message` - **Fetch:** `GET http://localhost:18790/messages?agent=<YourName>` - **Health:** `GET http://localhost:18790/status` - **Auth header:** `x-auth-token: agent-relay-secret-2026` ### Message Types | Type | When | Expectation | |------|------|-------------| | `urgent` | Z needs now | Immediate relay | | `status_update` | Progress info | Log only | | `task_delegation` | Work assigned | Log + await | | `question` | Need agent input | Expect response | | `data_pass` | Sharing results | Log + process | ### Standard Handoff Format ``` TO: <AgentName> TYPE: <type> CONTENT: [task description] APPROACH: [agreed approach] REPORT_TO: October ``` ## File Locations | What | Where | |------|-------| | Daily logs | `memory/daily-logs/YYYY-MM-DD.md` | | Agent comm audit | `memory/agent-comm-logs/YYYY-MM-DD.jsonl` | | This protocol | `skills/swarm-workflow-protocol/SKILL.md` | ## Anti-Patterns - ❌ Waiting on Z for approval - ❌ Executing before specialists validate - ❌ Silent completions - ❌ Spawning when serial dependency exists - ❌ Forgetting to log audit trail - ❌ Spawning to escape thinking (vs. leveraging parallel seams)
在 GitHub 查看