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oc-team-builderOC 团队建设者

Agent Skill

oc-team-builder 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

7,296

周安装

301

GitHub Stars

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下载量

2,384
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:oc-team-builder(OC 团队建设者)
来源仓库:https://github.com/joeszeles/oc-team-builder
安装命令:
openclaw skills install oc-team-builder
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install oc-team-builder

简介

从 OpenClaw 核心、代理部门和研究实验室中组建专家团队。

  • 适合根据关键词或任务需求快速匹配并激活对应领域专家。
  • 安装后输入场景描述即可获取候选名单及专长摘要。
  • 结果依赖内部名册数据,实际可用性需人工确认。oc-team-builder 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 不涉及敏感信息处理,但输出内容应作为参考而非最终决策。

SKILL.md

name
team-builder
description
Discover, compose, and activate specialist teams from 3 rosters — OpenClaw Core (CEO/Artist), Agency Division (55+ specialists), and Research Lab (autonomous experiment loops via Karpathy's autoresearch). Planner proposes optimal teams; Reviewer validates deliverables.
metadata

Team Builder

Compose the right team for any job by drawing from three rosters of specialists. The Research Lab uses Karpathy's autoresearch methodology for autonomous experiment loops.

Quick Start — Scripts

1. Browse available agents

bash {baseDir}/scripts/roster.sh                     # all 3 rosters
bash {baseDir}/scripts/roster.sh -r agency            # agency only
bash {baseDir}/scripts/roster.sh -d engineering        # one division
bash {baseDir}/scripts/roster.sh -s "frontend"         # search
bash {baseDir}/scripts/roster.sh -v                    # verbose descriptions
bash {baseDir}/scripts/roster.sh -j                    # JSON output

2. Generate a team proposal

bash {baseDir}/scripts/plan.sh "Build a portfolio dashboard with pie charts"
bash {baseDir}/scripts/plan.sh --mode sprint "Optimize image generation prompts using autoresearch"
bash {baseDir}/scripts/plan.sh -o proposal.md "Analyze astronomy photos for star classification"

The planner auto-detects task domains (engineering, creative, research, marketing, operations, spatial) and proposes the right-sized team (micro/sprint/full).

3. Activate a specialist

bash {baseDir}/scripts/activate.sh --division engineering --agent frontend-developer
bash {baseDir}/scripts/activate.sh --division testing --agent evidence-collector
bash {baseDir}/scripts/activate.sh --division testing --list
bash {baseDir}/scripts/activate.sh --file reference/agency-agents-main/design/design-ui-designer.md
bash {baseDir}/scripts/activate.sh --division engineering --agent ai-engineer --personality-only

Outputs the agent's full personality definition for use in delegation prompts.

4. Run QA review

bash {baseDir}/scripts/review.sh --task "Portfolio dashboard"
bash {baseDir}/scripts/review.sh --task "Image pipeline" --criteria criteria.txt --pass evidence
bash {baseDir}/scripts/review.sh --task "LLM training optimization" --pass reality
bash {baseDir}/scripts/review.sh --task "Full product" --pass both -o review.md

Generates review checklists (Evidence Collector Pass 1 + Reality Checker Pass 2) and logs to ~/.openclaw/team-reviews/.

5. Run a Research Lab experiment

bash {baseDir}/scripts/experiment.sh --setup /path/to/project     # initialize experiment
bash {baseDir}/scripts/experiment.sh --run /path/to/project       # run one experiment cycle
bash {baseDir}/scripts/experiment.sh --status /path/to/project    # show ledger

See references/TEAM-RESEARCH.md for the full autoresearch methodology and working examples.

The Three Rosters

1. Core Team (references/TEAM-CORE.md)

The permanent OpenClaw agents. Always available, always running.

AgentRoleWorkspace
CEOLeader, orchestrator, final authority.openclaw/workspace/
ArtistImage generation, visual analysis.openclaw/workspace-artist/

2. Agency Division (references/TEAM-AGENCY.md)

55+ specialist agents across 9 divisions. Activated on demand from reference/agency-agents-main/.

DivisionAgentsKey Specialists
Engineering7Frontend Developer, Backend Architect, AI Engineer, DevOps
Design7UI Designer, UX Architect, Image Prompt Engineer
Marketing8Growth Hacker, Content Creator, Social Media
Product3Sprint Prioritizer, Trend Researcher, Feedback Synthesizer
Project Management5Senior PM, Studio Producer, Experiment Tracker
Testing7Evidence Collector, Reality Checker, API Tester
Support6Analytics Reporter, Finance Tracker, Legal Compliance
Spatial Computing6XR Architect, visionOS Engineer
Specialized7Agents Orchestrator, Data Analytics, LSP Engineer

3. Research Lab (references/TEAM-RESEARCH.md)

Autonomous experiment loops adapted from Karpathy's autoresearch. Set up a measurable experiment, run it in a fixed time budget, keep improvements, discard failures, loop forever.

Source code reference: reference/autoresearch-master/ (program.md, train.py, prepare.py)

Cross-Team Workflow Examples

Image Analysis + Research Loop

Artist (image acquisition) + Research Lab (analysis loop) + AI Engineer (classification)

Visual Content Pipeline

Artist (generation) + Image Prompt Engineer (prompts) + Visual Storyteller (narrative)

Dashboard / UI Feature Build

Senior PM (scope) + Frontend Developer (build) + Evidence Collector (QA)

Autonomous LLM Training (autoresearch)

Research Lab (experiment loop on train.py) + AI Engineer (architecture suggestions)
→ 12 experiments/hour, ~100 overnight, fully autonomous

Full Product Launch

CEO (orchestrate) + Engineering (build) + Design (UX) + Marketing (launch) + Testing (validate)

Handoff Protocol

When passing work between specialists:

## Handoff
| Field | Value |
|-------|-------|
| From | [Agent Name] |
| To | [Agent Name] |
| Task | [What needs to be done] |
| Priority | [Critical / High / Medium / Low] |

## Context
- Current state: [What's been done]
- Relevant files: [File paths]

## Deliverable
- What is needed: [Specific output]
- Acceptance criteria:
  - [ ] [Criterion 1]
  - [ ] [Criterion 2]

## Quality
- Evidence required: [What proof looks like]
- Reviewer: [Who validates]

For complete handoff templates: reference/agency-agents-main/strategy/coordination/handoff-templates.md

NEXUS Pipeline Modes

ModeScaleAgentsTimeline
MicroSingle task/fix1-3Hours-days
SprintFeature or MVP5-101-2 weeks
FullComplete product10+Weeks-months

Reference Files

FileContents
SKILL.mdThis file — overview, scripts, quick start
scripts/roster.shBrowse and search all agent rosters
scripts/plan.shGenerate team proposals from task descriptions
scripts/activate.shLoad agent personality definitions
scripts/review.shGenerate QA review checklists
scripts/experiment.shRun autoresearch experiment loops
references/TEAM-CORE.mdCEO/Artist — roles and interactions
references/TEAM-AGENCY.mdAll 55+ Agency specialists indexed by division
references/TEAM-RESEARCH.mdAutonomous experiment methodology (autoresearch)
references/PLANNER.mdJob analysis → team proposal workflow (detailed)
references/REVIEWER.mdQA validation workflow with quality gates
references/PROOF-OF-WORK.mdExample proposals showing cross-roster teams

适合场景

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用户想查找某类 Agent Skill 时

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能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

75.57%
按下载量换算1,802

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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