Lead Researcher

Lead Researcher

MANDATORY orchestrator for ALL research. Based on Anthropic's orchestrator-worker pattern. You plan, decompose, assign (1 language + 1 sub-topic) per agent, then synthesize.

Arguments

Format: [mode] [topic] — mode is optional (default: medium).

/lead-researcher high compare auth solutions for Next.js
/lead-researcher max full ecosystem survey of MCP servers
/lead-researcher what's the best ORM for PostgreSQL     ← mode inferred as medium

Modes

Chinese (ZH) is ALWAYS included — even in low mode. The difference between modes is iteration depth and additional languages, not whether ZH is included.

Mode Selection Heuristic

Execution Engine

How to trigger a dynamic workflow

After Phase 1 confirms high/max mode, tell the user:

"This is a high/max research — I'll run it as a dynamic workflow for better parallelism and cross-checking."

Then describe the research task with the word "workflow" in your message to trigger the workflow engine. Include ALL the planning details (sub-topics, languages, iterations, output path) so the workflow script encodes them.

Example trigger prompt:

Run a workflow to research [topic]:

Phase 1 — Parallel search (per language × sub-topic):
[list each agent assignment: language + sub-topic + iterations]

Phase 2 — Cross-check:
Have independent agents adversarially review each other's findings.
Flag contradictions, unverified claims, and missing sources.

Phase 3 — Synthesize:
Merge verified findings into one report at [output path].
Lead with recommendation, present cross-language tensions, cite sources.

Language matrix reference: [paste relevant T1/T2 langs from matrix]
Each agent should use the `research` skill for search-fetch loop methodology.

For low/medium mode, skip the workflow engine and use regular subagent spawning (Phase 2-5 below).

Workflow (Subagent Mode — low/medium)

Phase 0: Clarity Triage (MANDATORY)

Before planning research, assess whether the request is researchable as stated. This prevents wasting compute on poorly-scoped queries.

Triage decision:

When routing to heavy-think:

1. Invoke Skill("heavy-think") — it will classify the thinking mode (brainstorm/decompose/unstick/analyze)
2. After heavy-think produces a clear, bounded research question with success criteria → return here at Phase 1
3. State: "The research question wasn't clear enough to scope. After thinking through it, the researchable question is: [X]"

Phase 0 is distinct from Phase 1's AskUserQuestion. Phase 0 catches "the problem isn't researchable yet." Phase 1 catches "confirm my research plan parameters." Don't blur them.

Pass-through examples (skip heavy-think, go directly to Phase 1):

• "Compare Next.js vs Remix for SSR performance" — criteria implied (performance), scope bounded
• "What's the latest stable version of React" — single fact, clear target
• "Evaluate Drizzle vs Prisma for type-safe PostgreSQL ORM" — evaluation axes implicit in "type-safe PostgreSQL ORM"

Fire examples (route to heavy-think first):

• "Research auth" — auth for what? Security posture? Developer experience? Cost? Different criteria produce different research
• "What's the best approach for our backend" — needs decomposition before any search is meaningful
• "How should I think about caching" — asking for perspective, not information

Phase 1: Confirm Research Plan with User (MANDATORY)

Before executing, you MUST present the research plan and let the user confirm or adjust. Use AskUserQuestion with these questions:

Question 1 — Mode selection: Show the recommended mode (based on heuristic) and let user pick. Include estimated agent count and cost.

"Which research depth? I recommend [X] based on your query."
Options:
- low: 2 langs (EN+ZH), 2-3 iterations/agent, ~2-4 agents
- medium: 2 langs (EN+ZH), 3-5 iterations/agent, ~4-6 agents
- high: 3 langs (EN+ZH+RU), 5-8 iterations/agent, ~6-12 agents
- max: 3+ langs, 8-10 iterations/agent, ~9-15+ agents

Question 2 — Sub-topics: Show the decomposed sub-topics and let user add/remove/adjust.

"I've broken this into [N] sub-topics. Adjust?"
Options:
- Looks good (Recommended)
- [list each sub-topic so user can see what will be researched]
- Let me customize (user types their own breakdown)

Question 3 — Languages: Show which languages will be searched, based on the language matrix lookup.

"Which languages to search?"
Options:
- [Recommended set based on mode + matrix, e.g. "EN + ZH + RU (Recommended)"]
- EN + ZH only (faster, fewer agents)
- Add [specific T2 language from matrix, e.g. "DE" or "JA"]
- Let me specify

After user confirms, proceed to Phase 2 with the confirmed settings.

Skip Phase 1 ONLY when: user explicitly specified all parameters (mode + topics) in their original message, e.g. /lead-researcher high compare A vs B.

Phase 2: Plan (YOU do this — don't delegate)

1. Apply confirmed settings — use mode, sub-topics, and languages from Phase 1
2. Read references/language-matrix.md — look up T1/T2 languages for the topic's field (if not already done in Phase 1). This determines which languages to assign beyond the EN+ZH default.
3. Decompose — break topic into sub-questions (use confirmed sub-topics from Phase 1)
4. Assign agents — each agent gets 1 language + 1 sub-topic (atomic, single responsibility). Use T1 languages from the matrix as primary assignments; add T2 languages in higher modes.
5. Set iterations — per confirmed mode
6. Set output path — relative to current working directory: ./research/YYMMDD-<topic>/

Agent assignment: 1 language + 1 sub-topic per agent (atomic)

Each agent has a single, clear job. If you have 2 sub-topics and 3 languages, that's 6 agents — batch into waves of 3.

Example for "compare auth solutions" in high mode:

Wave 1 (parallel):
  Agent 1: EN + "OAuth/OIDC providers comparison"
  Agent 2: ZH + "OAuth/OIDC providers comparison"  
  Agent 3: RU + "OAuth/OIDC providers comparison"

Wave 2 (parallel):
  Agent 4: EN + "self-hosted auth libraries"
  Agent 5: ZH + "self-hosted auth libraries"
  Agent 6: RU + "self-hosted auth libraries"

For simpler topics (low/medium), fewer sub-topics = fewer agents:

Low mode (1 sub-topic, 2 langs):
  Agent 1: EN + the question
  Agent 2: ZH + the question

Medium mode (1-2 sub-topics, 2 langs):
  Agent 1: EN + sub-topic A
  Agent 2: ZH + sub-topic A
  (wave 2 if needed for sub-topic B)

Phase 3: Delegate (Parallel Subagents — low/medium only)

For high/max mode, use the workflow engine above instead of this phase.

Spawn researcher agents. Each prompt:

Research: [1 specific sub-question]
Language: [1 assigned language — search ONLY in this language]
Iterations: [N] (min [X], max [Y] — each = search + fetch + evaluate)
Output: Return findings as structured data, NOT a formatted report

Scope: ONLY [sub-topic]. Do NOT investigate [other sub-topics].
Report path: [./research/YYMMDD-topic/lang-subtopic.md]

RECOMMENDED SKILLS: research - use for search-fetch loop methodology

Max 3 agents per wave. Fire all in a single message.

Phase 4: Deepen (YOU do this after agents return)

After agents return, YOU verify and deepen:

This phase separates surface search from real research. NEVER skip.

Phase 5: Synthesize

Merge into one report at ./research/YYMMDD-<topic>.md (relative to cwd):

1. Lead with recommendation — what and why
2. Cross-language tensions — where communities disagree
3. Cite primary sources — every claim links to a fetched URL
4. List contradictions — present honestly
5. Unresolved questions — what couldn't be answered

Anti-Patterns

Related

• research skill — methodology loaded by each agent
• language-matrix.md — conditional languages