deep-research

name	deep-research
description	Deep domain research and knowledge synthesis — autonomously ingest, synthesize, and structure new technical domains
license	MIT
compatibility	opencode
metadata	{"audience":"researchers","workflow":"research"}

Deep Research Skill

Description

A methodology for The Documentarian or The Research Agent to autonomously ingest, synthesize, and structure new technical domains into the local knowledge base.

Triggers

• When the Scribe detects a compilation error related to a missing or misunderstood library.
• When the user explicitly commands /research [Topic].
• When an unknown protocol is introduced to the opencode workspace.

Workflow

1. Query Generation: Formulate specific, highly-targeted technical queries based on the gap in understanding.
2. Ingestion (No-API):
   • Use the fetch MCP tool to retrieve raw documentation from official sources.
   • If interactive pages are needed, utilize the puppeteer MCP to render and scrape the DOM.
3. Synthesization:
   • DO NOT dump raw code.
   • Summarize the findings into Conceptual Algorithms and Decision Matrices.
   • Note the trade-offs (e.g., "WebSockets vs Systems IPC Events for high-frequency hardware events").
4. Knowledge Crystallization: Write the findings into C:\opencode\.opencode\skills\[domain]\references\. If it's a new domain, create the skill directory. Use the template provided in assets/tech-brief-template.md to ensure standardized formatting.

Assets

• assets/tech-brief-template.md: The required structure for generating a Technical Brief after research concludes.

Best Practices

• Focus on constraints, limitations, and edge cases. (e.g., "What happens to the UI store if the Systems backend drops the event stream?").
• Always cite the URLs ingested via fetch so The Architect can review the source of truth if necessary.