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Agency Agents Wrapper

作者 Jabir Iliyas Suraj-Deen · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install agency-agents-wrapper
功能描述
Activate specialized AI agent personas across 18+ domains with tailored expertise, workflows, communication styles, and technical deliverables.
使用说明 (SKILL.md)

Agency Agents

The Agency is a collection of meticulously crafted AI agent personalities, each with:

  • Deep expertise in their domain (not generic templates)
  • Unique voice and communication style
  • Proven workflows and success metrics
  • Technical deliverables with code examples

Quick Start

Browse Available Agents

Agents are organized by domain. List available agents:

ls -la references/agents/

Each .md file contains a complete agent personality with identity, mission, rules, and deliverables.

Major domains:

  • engineering/ — Backend, Frontend, DevOps, Security, ML, etc.
  • product/ — Product Managers, UX Designers, Growth strategists
  • sales/ — Sales specialists, Account Executives, Deal closers
  • marketing/ — Content, Paid Media, Community, Growth
  • design/ — UI/UX, Brand, Product Design
  • strategy/ — Consultants, Analysts, Business strategists
  • support/ — Customer Success, Support specialists
  • testing/ — QA Engineers, Test strategists
  • And more: academic/, game-development/, integrations/, specialized/

Activate an Agent

  1. Find the agent in references/agents/\x3Cdomain>/ (e.g., engineering-frontend-developer.md)
  2. Read the agent file to absorb personality, mission, and workflows
  3. Adopt the persona — use the agent's voice, approach, and technical style for your current task
  4. Follow their rules — each agent has critical guidelines and success metrics

Example: Frontend Developer

Read references/agents/engineering/engineering-frontend-developer.md, then work with:

  • Their performance-first mindset
  • Accessibility and inclusive design focus
  • Technical deliverables (React components, PWAs, design systems)
  • Communication style (detail-oriented, user-centric, precise)

Agent Structure

Each agent file contains:

---
name: Agent Name
description: Brief description
color: Visual indicator
emoji: 🎭
vibe: One-liner describing their energy
---

# Agent Personality

## 🧠 Identity & Memory
- Role, personality, experience

## 🎯 Core Mission
- Primary responsibilities
- Technical domains
- Key deliverables

## 🚨 Critical Rules
- Non-negotiable guidelines
- Quality standards
- Red lines

## 📋 Technical Deliverables
- Code examples
- Templates
- Proven patterns

## 💬 Communication Style
- Tone and voice
- How they interact
- Success metrics

Workflow: Task-Agent Matching

When facing a complex task:

  1. Identify the domain — What expertise does this need? (engineering, marketing, sales, etc.)
  2. Find the best agent — Browse that domain folder for the closest match
  3. Read their profile — Understand their perspective, priorities, and rules
  4. Adopt their approach — Use their workflows, communication style, and technical standards
  5. Execute — Apply their expertise and deliverables to your task

Example flow:

  • Task: "Build a responsive dashboard"
  • Domain: Engineering (Frontend)
  • Agent: engineering-frontend-developer.md
  • Adopt: Performance-first, accessibility-focused, Core Web Vitals optimization
  • Execute: Build with their rules, code examples, and UX patterns

Tips for Effective Agent Use

🎯 Specificity Matters

  • Don't just activate "Engineer" — pick a specific role like Frontend Developer, Backend Architect, or DevOps Automator
  • The more specific the agent, the more tailored their expertise

📚 Read Actively

  • Each agent file is self-contained and comprehensive
  • Reading takes 2-3 minutes; the detail is worth it
  • You'll pick up their mental models and priorities

🔄 Mix and Match

  • You can combine insights from multiple agents (e.g., Frontend Developer + UX Designer for a UI project)
  • Cross-domain personas often have complementary perspectives

✅ Follow Their Rules

  • Each agent has non-negotiable guidelines ("Critical Rules")
  • These aren't suggestions — they're their professional standards
  • Following them ensures quality output

Available Agent Categories

Domain Focus Example Agents
Engineering 23 roles Frontend Dev, Backend Architect, DevOps, Security, ML, etc.
Product 8 roles Product Manager, UX Designer, Growth, Analytics
Sales 6 roles Sales Rep, Account Exec, Deal Closer, Pipeline builder
Marketing 8 roles Content, Paid Media, Community, Growth, Brand
Design 7 roles UI/UX, Product Design, Brand, Design System
Strategy 5 roles Consultant, Analyst, Business Strategist
Support 4 roles Customer Success, Support, Community
Testing 4 roles QA Engineer, Test Strategist, Automation
Other 12+ roles Academic, Game Dev, Integrations, Specialized

Reference Structure

All agent files are stored in references/agents/\x3Cdomain>/ following the source repository structure. Each file is a standalone personality — load only the ones you need.

To see available agents, explore:

  • references/agents/engineering/ — All engineering specialties
  • references/agents/product/ — Product and UX roles
  • references/agents/sales/ — Sales professionals
  • (and so on for all domains)
安全使用建议
This package appears coherent for the stated purpose (a local collection of agent/persona files). Before enabling: (1) inspect the one included shell script (references/agents/integrations/mcp-memory/setup.sh) — do not run it unless you trust its contents, (2) confirm the skill's origin since registry metadata lists 'unknown' and there's no homepage, and (3) remember that although the skill itself requests no secrets, personas may ask you for context during use; don't paste sensitive credentials into chats. If you want extra caution, open the repository files yourself and verify there are no embedded external endpoints or secret-collection prompts in the persona MDs before use.
功能分析
Type: OpenClaw Skill Name: agency-agents-wrapper Version: 1.0.0 The skill bundle provides a comprehensive and well-documented framework for adopting specialized AI personas and orchestrating multi-agent workflows. The content primarily consists of markdown-based persona definitions and operational playbooks designed for professional expertise. While a technical vulnerability exists in 'engineering-ai-data-remediation-engineer.md' due to the use of eval() on AI-generated strings, the script includes explicit safety gates and forbidden-term checks (e.g., blocking 'import', 'os', and 'subprocess') to mitigate RCE risks. No evidence of malicious intent, data exfiltration, or unauthorized persistence was found.
能力标签
cryptorequires-walletcan-make-purchasescan-sign-transactionsrequires-oauth-token
能力评估
Purpose & Capability
Name/description promise (activate pre-built agent personas) matches the bundle: the repository contains many .md persona files organized by domain and the SKILL.md tells the agent to list/read those files. There are no unrelated environment variables, binaries, or config paths required.
Instruction Scope
SKILL.md only instructs the agent to browse and read local markdown persona files (e.g., ls and open references/agents/...). It does not direct the agent to read system files, access external endpoints, collect or transmit user secrets, or run arbitrary commands beyond listing/reading these repository files.
Install Mechanism
No install spec is present (instruction-only), which is lowest-risk. There is one shell script file present (references/agents/integrations/mcp-memory/setup.sh) but SKILL.md does not instruct running it. Presence of that script is plausible for an integrations subfolder and is not by itself evidence of an installer or remote download.
Credentials
The skill declares no required environment variables, no primary credential, and no config paths. Nothing in SKILL.md tries to access undeclared env vars or credentials.
Persistence & Privilege
Flags show always:false and normal model invocation; the skill does not request permanent presence or system-wide configuration changes in its instructions. Nothing in the files indicates modification of other skills or system settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agency-agents-wrapper
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agency-agents-wrapper 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial public release of agency-agents-wrapper - Introduces a comprehensive library of pre-built AI agent personas across 18+ professional domains (engineering, marketing, sales, product, design, support, and more). - Each agent profile includes unique voice, deep expertise, proven workflows, rules, communication style, and technical deliverables. - Guides users through browsing, activating, and adopting specialized agent personas for focused, domain-specific tasks. - Provides clear structure for agent files and best practices for effective use, including task-agent matching and combining multiple agents. - Designed for quick onboarding and high-quality, professional outputs by following each agent's critical rules and workflows.
元数据
Slug agency-agents-wrapper
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Agency Agents Wrapper 是什么?

Activate specialized AI agent personas across 18+ domains with tailored expertise, workflows, communication styles, and technical deliverables. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 110 次。

如何安装 Agency Agents Wrapper?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install agency-agents-wrapper」即可一键安装,无需额外配置。

Agency Agents Wrapper 是免费的吗?

是的,Agency Agents Wrapper 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Agency Agents Wrapper 支持哪些平台?

Agency Agents Wrapper 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Agency Agents Wrapper?

由 Jabir Iliyas Suraj-Deen(@jabir-srj)开发并维护,当前版本 v1.0.0。

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