Research a question by splitting it into two to five topics and assigning parallel documentation agents. Get a combined summary, detailed findings for each topic, and source URLs from web searches and documentation lookups.
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Invoke parallel document-specialist agents for external web searches and documentation lookup
安装前可以查看交给 AI 代理的指令内容。
Fetch external documentation, references, and context for a query. Decomposes into 2-5 facets and spawns parallel document-specialist Claude agents.
/oh-my-claudecode:external-context <topic or question>
/oh-my-claudecode:external-context What are the best practices for JWT token rotation in Node.js?
/oh-my-claudecode:external-context Compare Prisma vs Drizzle ORM for PostgreSQL
/oh-my-claudecode:external-context Latest React Server Components patterns and conventions
Given a query, decompose into 2-5 independent search facets:
## Search Decomposition
**Query:** <original query>
### Facet 1: <facet-name>
- **Search focus:** What to search for
- **Sources:** Official docs, GitHub, blogs, etc.
### Facet 2: <facet-name>
...
Fire independent facets in parallel via Task tool:
Task(subagent_type="oh-my-claudecode:document-specialist", model="sonnet", prompt="Search for: <facet 1 description>. Use WebSearch and WebFetch to find official documentation and examples. Cite all sources with URLs.")
Task(subagent_type="oh-my-claudecode:document-specialist", model="sonnet", prompt="Search for: <facet 2 description>. Use WebSearch and WebFetch to find official documentation and examples. Cite all sources with URLs.")
Maximum 5 parallel document-specialist agents.
Present synthesized results in this format:
## External Context: <query>
### Key Findings
1. **<finding>** - Source: [title](url)
2. **<finding>** - Source: [title](url)
### Detailed Results
#### Facet 1: <name>
<aggregated findings with citations>
#### Facet 2: <name>
<aggregated findings with citations>
### Sources
- [Source 1](url)
- [Source 2](url)
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Write or revise instructions, specifications, tickets, and skill files so an agent can act without prior chat history. Make rules checkable, explain their reasons, define completion criteria, and keep authoritative information in one place.
中文简介正在准备中。当前显示英语简介。
Simplify bloated AI-generated code by removing dead code, duplicated logic, and unnecessary layers while checking that existing behavior stays intact. Use a limited cleanup scope, verify changes with relevant checks, or request a review without edits.
中文简介正在准备中。当前显示英语简介。
Review module relationships and change history to identify structural improvements in a codebase. Receive ranked candidates with file references, suggested changes, and risks, plus a recommendation for where to start, without modifying code.
中文简介正在准备中。当前显示英语简介。
Send questions, review requests, or implementation tasks to a supported local AI command-line tool through one shared command. The wrapper handles provider-specific options and saves the result as a Markdown file for later reference.
中文简介正在准备中。当前显示英语简介。
Turn an unclear idea into linked decision tickets on a repository’s issue tracker. Work through one decision per session, record outcomes and dependencies, and prepare a brief defining the objective, scope, and exclusions for delivery.
中文简介正在准备中。当前显示英语简介。
Turn a short product idea into working code through requirements analysis, technical planning, implementation, testing, and review. It coordinates agents across these stages, fixes detected problems, and reports assumptions that still need a human decision.
中文简介正在准备中。当前显示英语简介。