Tech Research

Orchestrate multi-source technical research by dispatching parallel subagents to gather intelligence from X/Twitter (via Grok), GitHub repositories (via DeepWiki), and the web (via WebSearch). Synthesize all findings into a single actionable report.

Architecture: The main agent orchestrates research using one of two modes — lightweight (Task Subagents) or heavyweight (Agent Teammates) — chosen based on research complexity.

Language

Match user’s language: Respond in the same language the user uses. If the user writes in Chinese, the entire research report should be in Chinese. If in English, report in English.

Research Mode Selection

Before dispatching any agents, determine the appropriate mode:

Light Mode (default for single-topic research)

Dispatch up to 3 Task Subagents (Task with subagent_type: "general-purpose"). Each handles one data source independently. The main agent synthesizes results after all return.

Heavy Mode (for multi-topic / competitive research)

Use TeamCreate to create a research team → TaskCreate for each research task → spawn Agent Teammates (via Task with team_name and name parameters) → coordinate via SendMessage. Teammates can:

• Communicate to avoid duplication (“I found Project A uses the same approach as B — focus on their differentiators”)
• Share discoveries across tasks (“The blog post I found compares all 3 frameworks, sending you the link”)
• Dynamically adjust scope based on what others have found

When to Use

• Evaluating a technology, library, or framework for adoption
• Comparing alternatives (e.g., “Zustand vs Jotai vs Redux”)
• Investigating a GitHub repo’s architecture and community reception
• Gauging developer sentiment on a new API, tool, or announcement
• Cross-language research (Chinese/Japanese developer communities)

Research Sources

Source Degradation

Not every source will be available every time. Follow this degradation strategy:

Minimum viable research: At least one source must return results. If all sources fail, report the failures and suggest the user check their environment setup.

Grok Browser Backend

Grok requires browser automation with login state. Multiple backends are supported, detected in priority order:

IMPORTANT: Do NOT modify the user’s default playwright MCP to add --user-data-dir. This would force ALL browser operations through a single profile, breaking parallel agent usage. Instead, use a separate playwright-grok instance.

Grok Pre-flight

Before dispatching a Grok subagent, run the pre-flight check to detect the best available backend:

bash ${SKILL_PATH}/scripts/grok_setup.sh check

The script outputs preflight JSON: {"ready": true, "backend": "chrome", "login_status": "logged_in", "hint": "..."}.

Key fields: ready (boolean), backend (chrome/playwright-grok/playwright/none), login_status (logged_in/logged_out/unknown), hint (human-readable summary).

Login Status Cache

Login state is cached at ~/.claude/tech-research/.grok-status.json. Cache semantics: logged_in persists until a subagent observes logout; logged_out auto-expires after 2 hours (or clear with grok_setup.sh reset); unknown/missing = optimistic, try Grok.

Subagent responsibility: After interacting with Grok, update login status:

• Success: bash ${SKILL_PATH}/scripts/grok_setup.sh status logged_in <backend>
• “Sign in” page: bash ${SKILL_PATH}/scripts/grok_setup.sh status logged_out <backend>

Workflow

Progress:

• Step 1: Analyze — Break question into per-source sub-queries
• Step 2: Preflight — Run grok_setup.sh check, determine available sources
• Step 3: Dispatch — Launch subagents in parallel (Light or Heavy mode)
• Step 4: Synthesize — Merge findings into unified report

1. Analyze the Research Question

Break the user’s question into sub-queries for each source:

• Grok query: Developer opinions, community sentiment, expert recommendations
• DeepWiki query: Repository architecture, API design, code quality (requires owner/repo)
• WebSearch query: Official docs, benchmarks, comparisons, recent announcements

Not every research task needs all 3 sources. Select sources based on the question:

2. Grok Pre-flight

Run grok_setup.sh check and determine whether to dispatch a Grok subagent. See Grok Pre-flight above.

3. Dispatch Research Agents

Choose the dispatch method based on the research mode selected above.

Light Mode: Task Subagents

Launch subagents concurrently using Task. See references/subagent_templates.md for complete prompt templates.

Grok subagent:

Task(subagent_type: "general-purpose", description: "Ask Grok about [topic]", prompt: <grok_template with BACKEND>)

DeepWiki subagent:

Task(subagent_type: "general-purpose", description: "DeepWiki research [repo]", prompt: <deepwiki_template>)

WebSearch subagent:

Task(subagent_type: "general-purpose", description: "Web research [topic]", prompt: <websearch_template>)

Heavy Mode: Agent Teammates

1. TeamCreate(team_name: "research-[topic]")
2. TaskCreate(subject: "Research [Project A]", description: "...", activeForm: "Researching [Project A]")
3. TaskCreate(subject: "Research [Project B]", description: "...", activeForm: "Researching [Project B]")
4. Task(subagent_type: "general-purpose", team_name: "research-[topic]", name: "researcher-a", prompt: "...")
5. Task(subagent_type: "general-purpose", team_name: "research-[topic]", name: "researcher-b", prompt: "...")
6. Coordinate via SendMessage — share findings, adjust scope, avoid duplication
7. Synthesize after all teammates report back
8. Shutdown teammates and TeamDelete when done

Each teammate should use all relevant data sources (Grok, DeepWiki, WebSearch) for their assigned topic, rather than splitting by data source.

4. Synthesize and Report

After all subagents return, merge findings into a unified report with these sections:

• TL;DR — 2-3 sentence executive summary with clear recommendation
• Community Sentiment (from X/Twitter) — Key opinions with @username attribution and post URLs
• Repository Analysis (from DeepWiki) — Architecture, code quality, API design, maintenance status
• Web Intelligence — Official docs, benchmarks, blog insights, announcements
• Comparison Matrix (if comparing alternatives) — Criteria-based table
• Recommendation — Clear, actionable recommendation based on all sources
• Limitations — What couldn’t be verified, including skipped sources and why

Grok Query Strategies

See references/grok_query_overview.md for strategy selection table and references/query_strategies.md for full prompt templates.

Troubleshooting & Self-Recovery

See references/troubleshooting.md for the full decision tree and common scenarios.

Tips

• For CJK communities, query Grok in the target language directly
• DeepWiki accepts up to 10 repos in a single query for comparisons
• WebSearch is best for recent information (include current year in queries)
• Always verify Grok post URLs before citing — accuracy is ~80%
• Run subagents in parallel to minimize total research time