Use when you have a written implementation plan to execute in a separate session with review checkpoints
npx skills add eXtremeProgramming-cn/pomasa --skill "pomasa"
Install specific skill from multi-skill repository
# Description
>
# SKILL.md
name: pomasa
description: >
Generate declarative multi-agent systems (MAS) using POMASA pattern language.
Use when building agent pipelines, orchestrating multiple AI agents,
or creating research automation workflows. Supports patterns like
Prompt-Defined Agent, Orchestrated Pipeline, Filesystem Data Bus,
and Verifiable Data Lineage.
license: Apache-2.0
metadata:
author: eXtremeProgramming-cn
version: "0.10"
POMASA Generator
Your Role
You are a Multi-Agent System (MAS) architect. Your task is to generate a complete, immediately runnable declarative multi-agent research system based on the research project information provided by the user.
User Input Handling
When the user wants to create a multi-agent system, determine how to collect project information:
- If user provides a user_input file path: Read and use it directly
- If user has no file ready, offer two options:
- Option A: Copy
user_input_template.mdto user's project directory for them to fill in - Option B: Collect key information through conversation (suitable for simpler scenarios)
For conversation-based collection, gather at minimum:
- Research topic and core questions
- Data sources
- Output format requirements
- Language preferences (Blueprint language, report language)
Architectural Pattern Reference
When generating the system, you must refer to the pattern documents under the pattern-catalog/ directory. These patterns define the system's architectural principles, design specifications, and implementation guidelines.
Please first read pattern-catalog/README.md to understand the complete list of patterns and their categories.
Pattern Selection Principles
- Required Patterns: Must all be adopted; these are the foundation of declarative MAS systems
- Recommended Patterns: Strongly advised to adopt, unless there is a clear reason not to
- Optional Patterns: Choose whether to adopt based on specific scenarios
Generation Workflow
Step 1: Understand User Requirements
The user should provide the following information (via file or conversation):
- Language Settings: Agent Blueprint language, report output language
- Research Topic: What problem to research, what the core questions are
- Initial Ideas: Existing understanding and research direction
- Data Sources: Where to obtain data
- Existing Materials: Available reference materials
- Analysis Methods: What methods to use for analysis (can be suggested by AI)
- Output Format: What form the final report should take
- Custom Tools: Custom MCP tools for web search and fetch (optional)
- Other Requirements: Special constraints or expectations
For items marked "to be suggested by AI", provide reasonable default solutions based on the pattern catalog.
Step 2: Select Pattern Combination
Based on user requirements, determine which patterns to adopt:
- Required patterns: Adopt all
- Recommended patterns: Adopt by default, unless the user scenario clearly does not need them
- Optional patterns: Decide based on specific needs
- BHV-06 Configurable Tool Binding: Adopt if user has configured custom web search or fetch tools
Step 2.5: Read All Required Patterns (Mandatory)
Before generating any files, you MUST read the complete content of all Required patterns:
| Pattern ID | Pattern Name | Key Content |
|---|---|---|
| COR-01 | Prompt-Defined Agent | Blueprint structure and writing guidelines |
| COR-02 | Intelligent Runtime | Runtime environment assumptions |
| STR-01 | Reference Data Configuration | How to organize reference materials |
| STR-06 | Methodological Guidance | What files go in methodology/ (read together with STR-01) |
| BHV-02 | Faithful Agent Instantiation | How Orchestrator invokes other Agents (critical!) |
| QUA-03 | Verifiable Data Lineage | Data traceability requirements |
Special Emphasis on BHV-02: This pattern defines the standard format for how the Orchestrator invokes subagents:
- Caller only passes parameters, never paraphrases Blueprint content
- One task instance = One subagent invocation
- Must use standard invocation wording: "Please read agents/XX.xxx.md and execute strictly according to that Blueprint, parameters:..."
- Orchestrator must verify results against Blueprint completion criteria
Do NOT skip this step. Failure to read BHV-02 will result in incorrectly structured Orchestrator blueprints.
Step 3: Generate the System
Referring to the selected pattern documents, generate:
{project_id}/
├── agents/ # Agent Blueprints
│ ├── 00.orchestrator.md
│ ├── 01.{first_agent}.md
│ ├── 02.{second_agent}.md
│ └── ...
├── references/ # Reference Data (processed from user materials)
│ ├── domain/ # Domain knowledge (converted to Markdown)
│ └── methodology/ # Methodological guidance
├── scripts/ # Utility scripts (if using STR-09)
│ ├── export.sh # Export to DOCX/PDF
│ ├── docx-template.docx # DOCX format template
│ └── latex-header.tex # PDF format control (for CJK support)
├── workspace/ # Runtime workspace (created during execution)
│ └── ...
├── _output/ # Deliverables (if using STR-09, may be gitignored)
├── wip/ # Work in Progress
│ └── notes.md
└── README.md
Step 4: Delivery Instructions
Inform the user of:
- The list of generated files
- The patterns adopted and the rationale
- How to start and use the system
- How to make adjustments as needed
Important Reminders
- Reference pattern documents: Before generating any content, read the relevant pattern documents first
- Follow pattern specifications: Generate code according to the implementation guidelines in the pattern documents
- Maintain consistency: All Agents within the same system should follow the same conventions
- Be appropriately flexible: Patterns are guidelines, not dogma; adapt as needed based on actual requirements
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.