# Tech Research > Comprehensive technical research by combining multiple intelligence sources — Grok (X/Twitter developer discussions via browser automation), DeepWiki (AI-powered GitHub repository analysis), and WebSearch. Dispatches parallel subagents for each source and synthesizes findings into a unified report. This skill should be used when evaluating technologies, comparing libraries/frameworks, researching GitHub repos, gauging developer sentiment, or investigating technical architecture decisions. Trigger phrases include "tech research", "research this technology", "技术调研", "调研一下", "compare libraries", "evaluate framework", "investigate repo". Use when this capability is needed. - Skill: `tomevault-io/tech-research` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/tech-research` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/tech-research/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/tech-research --- # 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: | Signal | → Mode | |--------|--------| | Single topic, multiple data sources (Grok + DeepWiki + WebSearch) | **Light** → Task Subagents | | Multiple independent topics/competitors needing cross-comparison | **Heavy** → Agent Teammates | | Research may produce follow-up questions requiring dynamic re-scoping | **Heavy** → Agent Teammates | | Agent count ≥ 4 | **Heavy** → Agent Teammates | ### 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 | What It Provides | Best For | |--------|-----------------|----------| | **Grok** (X/Twitter) | Real developer opinions, @handles, post URLs | Sentiment, expert discovery, niche recommendations | | **DeepWiki** (GitHub) | AI-powered repo analysis, architecture, API docs | Understanding codebases, comparing repo internals. **Only use `ask_question`** — never `read_wiki_structure` or `read_wiki_contents` (they return massive dumps that easily exceed context limits) | | **WebSearch** | Official docs, blog posts, benchmarks, tutorials | Facts, performance data, official announcements | ## Source Degradation Not every source will be available every time. Follow this degradation strategy: | Source | If unavailable | Fallback | |--------|---------------|----------| | Grok | No browser backend or not logged in | Skip. Note in report: "Grok source skipped — [reason]." | | DeepWiki | No `owner/repo` known, or API error | Skip. Note in report: "DeepWiki skipped — [reason]." | | WebSearch | Tool unavailable (rare) | Skip. Note in report. | **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: | Priority | Backend | MCP Server Name | Pros | Cons | |----------|---------|----------------|------|------| | 1 | **Claude-in-Chrome** | `claude-in-chrome` | Zero setup, uses user's Chrome login state | Occupies user's visible Chrome window | | 2 | **Playwright-Grok** | `playwright-grok` | Dedicated profile, login persists, doesn't block default Playwright | One-time setup required | | 3 | **Playwright** (default) | `playwright` | Already configured for most users | No login persistence, may not be logged in | **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, detect the best available browser backend. There are two paths: **Path A — claude-in-chrome (runtime detection, no script needed):** If `mcp__claude-in-chrome__*` tools are available in your current session, you already have the best backend. The chrome extension is injected at runtime and **never appears in `~/.claude.json`**, so the shell script cannot detect it. Skip the preflight script entirely — set `backend=chrome` and proceed to dispatch. **Path B — Playwright fallback (script-based detection):** If claude-in-chrome is NOT available, run the preflight script to detect playwright backends: ```bash bash ${SKILL_PATH}/scripts/grok_setup.sh check ``` The script outputs preflight JSON: `{"ready": true, "backend": "playwright-grok", "login_status": "logged_in", "hint": "..."}`. Key fields: `ready` (boolean), `backend` (`playwright-grok`/`playwright`/`none`), `login_status` (`logged_in`/`logged_out`/`unknown`), `hint` (human-readable summary). **Live validation note:** Preflight checks MCP server presence in `~/.claude.json` (existence check) but cannot live-test browser MCP connectivity from shell — MCP servers are runtime-managed by Claude Code and only accessible during agent execution. The skill compensates with optimistic dispatch: assume the backend works, then update the login status cache based on actual browser interaction results (see Login Status Cache below). | Exit Code | Meaning | Action | |-----------|---------|--------| | `0` READY | Backend available | Pass `backend` value to Grok subagent. If `login_status` is `logged_out`, skip Grok and note in report. Otherwise dispatch subagent (optimistic). | | `1` NEEDS_SETUP | Has playwright, no playwright-grok | **Setup-first gate:** Run `grok_setup.sh setup` immediately (before any research). Setup backs up `~/.claude.json` before modifying it (backup path is in the output JSON). If setup succeeds, inform user they need to restart Claude Code for playwright-grok to activate, then proceed with research using `backend=playwright` as a degraded fallback for this session only. Do NOT start research and discover the setup need mid-way. | | `2` NOT_AVAILABLE | No browser MCP at all | Skip Grok source entirely. Note in report. | **Config safety:** The setup command always creates a timestamped backup of `~/.claude.json` before writing. If the new config causes issues, restore with the rollback command in the setup output (e.g., `cp ~/.claude.json.backup. ~/.claude.json`). ### 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 ` - "Sign in" page: `bash ${SKILL_PATH}/scripts/grok_setup.sh status logged_out ` **Cache corruption recovery:** If the status file is corrupted (invalid JSON, permission errors), delete it (`rm ~/.claude/tech-research/.grok-status.json`) and proceed in optimistic mode. See [references/troubleshooting.md](references/troubleshooting.md) for the full decision tree. ## Workflow Progress: - [ ] Step 1: Preflight — Run `grok_setup.sh check`, complete any setup BEFORE research - [ ] Step 2: Analyze — Break question into per-source sub-queries - [ ] Step 3: Dispatch — Launch subagents in parallel (Light or Heavy mode) - [ ] Step 4: Synthesize — Merge findings into unified report ### 1. Preflight Gate (MUST run first) Run preflight **before doing anything else**. This determines which sources are available and whether one-time setup is needed. 1. Check if `mcp__claude-in-chrome__*` tools are available in your session. - **Yes** → `backend=chrome`, skip the script, proceed directly to Step 2. - **No** → Run `bash ${SKILL_PATH}/scripts/grok_setup.sh check` and act on exit code: ``` Exit code 0 (READY) → Note the backend and login_status, proceed to Step 2. Exit code 1 (NEEDS_SETUP) → Run `grok_setup.sh setup` NOW. If setup succeeds, tell the user: "playwright-grok has been configured. Restart Claude Code to activate it. For this session, Grok will use the default playwright backend as a fallback." Then proceed to Step 2 with backend=playwright. Exit code 2 (NOT_AVAILABLE) → Grok is unavailable. Proceed to Step 2 without Grok. ``` **Key principle:** Setup completes before research begins. Never discover setup needs mid-research. ### 2. 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: | Research Type | Grok | DeepWiki | WebSearch | |---------------|------|----------|-----------| | "Should we use library X?" | Yes | Yes (if OSS) | Yes | | "What are devs saying about X?" | Yes | No | Maybe | | "How does repo X work internally?" | No | Yes | Maybe | | "Compare X vs Y performance" | Maybe | Yes (both repos) | Yes | | "What's new in framework X?" | Yes | No | Yes | ### 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](references/subagent_templates.md) for complete prompt templates. **Grok subagent:** ``` Task(subagent_type: "general-purpose", description: "Ask Grok about [topic]", prompt: ) ``` **DeepWiki subagent:** ``` Task(subagent_type: "general-purpose", description: "DeepWiki research [repo]", prompt: ) ``` **WebSearch subagent:** ``` Task(subagent_type: "general-purpose", description: "Web research [topic]", prompt: ) ``` #### 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](references/grok_query_overview.md) for strategy selection table and [references/query_strategies.md](references/query_strategies.md) for full prompt templates. ## Troubleshooting & Self-Recovery See [references/troubleshooting.md](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 --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/psylch) — claim your Tome and manage your conversions.