A professional tool for analyzing AI agent platform requirements to extract structured testing points, interaction scenarios, and critical missing information.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-requirement-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agent-requirement-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Agent Requirement Analyzer?
The Agent Requirement Analyzer is a specialized utility within the Openclaw Skills ecosystem designed to bridge the gap between messy Product Requirement Documents (PRDs) and actionable development or testing plans. It focuses specifically on the full lifecycle of AI agents—covering creation, configuration, publishing, and invocation—ensuring that no technical detail is overlooked during the initial project phase.
By leveraging this skill, teams can automatically identify ambiguities, contradictions, and missing parameters in their AI agent specifications. It transforms raw text or user stories into structured analysis reports, highlighting numerical limits, exception handling logic, and permission rules that are often neglected in standard documentation.
Agent Requirement Analyzer Use Cases
- Analyzing AI agent platform PRDs or user stories to define testing scopes.
- Identifying missing technical specifications like character limits or timeout thresholds.
- Extracting core functional modules for agent lifecycle management.
- Preparing structured requirement reports for Feishu/Lark documentation nodes.
- Resolving contradictions between performance requirements and functional descriptions.
How Agent Requirement Analyzer Works
- The skill parses the provided requirement document to identify core functional modules of the AI agent platform.
- It extracts specific testing requirements based on the agent's lifecycle (Creation, Configuration, Publishing, Invocation).
- It performs a deep scan to identify items requiring clarification, such as missing values, vague adjectives, or conflicting logic.
- It generates a structured requirement analysis report, including a dedicated list of items for stakeholders to confirm.
- The final report is outputted or synced to designated knowledge base nodes for team collaboration.
Agent Requirement Analyzer Setup
To integrate this tool into your workflow, ensure your environment has access to the necessary Feishu/Lark integration tools and the Openclaw Skills framework.
# Example of triggering the analyzer via CLI
openclaw-run agent-requirement-analyzer --input "./prd_v1.md"
Ensure your configuration includes the correct wiki_node and knowledge base IDs for automated document updates.
Agent Requirement Analyzer Data Schema & Taxonomy
The skill organizes extracted data into a multi-dimensional taxonomy for maximum clarity:
| Data Category | Description | Key Metadata |
|---|---|---|
| Functional Modules | Groups features like 'Agent Creation' or 'Monitoring' | Module Name, Priority, Effort Est. |
| Interaction Scenarios | Maps user actions to data flows | Trigger, Data Flow, Test Point |
| Edge Cases | Defines boundaries and exceptions | Field, Boundary, Exception Scenario |
| Confirmation List | Items requiring PM or Tech lead feedback | Issue Description, Suggestion, Priority |
Agent Requirement Analyzer Advanced Features
- Feishu/Lark Integration: Automated reading and writing to specific knowledge base nodes using wiki_node IDs.
- Ambiguity Detection: Heuristic scanning for vague terms like "fast" or "large scale" to enforce quantitative metrics.
- Lifecycle-Aware Analysis: Specifically tuned to recognize AI-specific components like LLM temperature settings, RAG knowledge base bindings, and Token consumption monitoring.
- Multi-Category Classification: Automatically distinguishes between Chat Assistants, Task Executors, and Code Generation agents to adjust testing focus.
SKILL.md
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