Research — Hybrid Router + Fallback

The runtime orchestrator for the research domain. Architecture C: deterministic classification → specialist delegation OR own plan-decompose-search-synthesize-cite workflow.

Portability

Requires WebSearch + WebFetch for the fallback workflow; specialist skills (pulse, grants, litreview, syllabus, patent, dossier) must be present for delegation to work. Node.js with docx package required if Q2 = document mode. Works in Claude Code CLI natively. In Claude.ai with web tools + Code Execution, the workflow is supported.

Distinct From engineering/autoresearch-agent

These two skills share the word "research" but serve completely different use cases:

• research/research/ (this skill) — research-query router + fallback workflow ("Research X")
• engineering/autoresearch-agent/ — Karpathy's autonomous file-optimization experiment loop ("Make this code faster")

No overlap. They coexist.

Hybrid Architecture (C)

Every invocation produces one of three outcomes:

1. Delegation — Classified as specialist-domain. Routes there. User sees the specialist's output.
2. Fallback execution — Classified as general research. Runs own plan → search → synthesize workflow.
3. Clarification request — Classification ambiguous. Asks one forcing question to disambiguate, then routes.

The skill never silently runs its fallback when a specialist would have done better. Routing transparency is what makes the hybrid architecture trustworthy.

Specialist Registry

Agent Integrity Rules

This skill obeys the research-pack convention:

• Execution discipline (fallback only): Sequential searches. 1 q/sec rate limit. Confirm response received before next call.
• Source discipline: Cite only sources returned by this session's tool calls. Training knowledge labeled [Background — not from search] and excluded from counts.
• Three-count tracking (fallback only): Queries sent / sources received / sources cited.
• Retry policy: On failure → wait 3s → retry once → log. After 3 consecutive failures: stop, alert user.
• Plan-tier detection: If delegated to Consensus-using specialist, that specialist handles detection. In fallback mode, surface any rate-limit signals.
• Routing discipline: Never delegate silently. Always state the decision + accept override.

Phase 1: Grill-Me Intake (2–4 Questions)

Intake is intentionally minimal — the goal is to route fast, not to interrogate. One question per turn.

Q1 (always) — Research question

What's the research question? State it in 1–2 sentences. Specific is better than broad — "AI for healthcare" gets you a vague survey; "How are health systems integrating LLM-based clinical decision support in 2026?" gets you a useful answer.

Why I'm asking: Specificity dictates classification accuracy and search precision. A vague question routes to fallback; a specific question often matches a specialist cleanly.

Refuse mush. If user says "research AI", push back once: "What about AI specifically — adoption, safety, capability, funding, regulation, comparison? Pick an angle."

Q2 (always) — Output preference

What output do you want? Pick one:

1. Quick chat briefing (5-min read, markdown in chat)
2. Standalone document (.docx with citations, shareable)

Why I'm asking: Document mode triggers deeper search budgets and full audit logs. Chat mode optimizes for fast delivery.

Forcing choice.

Q3 (asked only if classification ambiguous — ≤1 signal) — Domain disambiguation

Quick clarification — pick the closest match:

1. Academic literature (papers, peer-reviewed)
2. Industry / trends (what's the buzz, news, sentiment)
3. Specific entity (a company, person, organization)
4. Technology / patents (prior art, IP landscape)
5. Grant funding (NIH, foundations)
6. Course material (syllabus or curriculum)
7. None of the above — run general research

Why I'm asking: I couldn't classify confidently from your question alone. This routes you to the right specialist or confirms general-research fallback.

Skip if Q1 + Q2 produced clear specialist match (≥2 signals).

Q4 (asked only if Q3 was needed AND user picked "none of the above") — General-research scope

For general research, what's your time horizon — quick scan (5 searches) or thorough (15 searches)?

Why I'm asking: General research has no specialist budget; you pick it. Quick is good for "what's the lay of the land". Thorough is for "I'll make a decision based on this".

Skip if a specialist took over.

Stop condition: After Q4 (or earlier if dependency skips applied), commit and start Phase 2. Most invocations exit intake after Q1 + Q2.

Phase 2: Deterministic Classification

This is deterministic, not LLM-reasoned — for speed, debuggability, and consistency.

SIGNALS = {
  pulse:    ["reddit", "hn", "hacker news", "x.com", "twitter", "buzz",
             "sentiment", "trending", "what are people saying",
             "what's happening", "the conversation around",
             "pulse on", "take the pulse", "current conversation"],
  grants:   ["nih", "grant", "grants for", "r01", "r21", "k-award", "reporter",
             "nosi", "funding", "fda", "study section", "principal investigator"],
  litreview:["literature review", "lit review", "litreview", "pico", "spider",
             "systematic review", "review papers on", "research papers on",
             "papers about", "meta-analysis"],
  syllabus: ["syllabus", "course outline", "curriculum", "reading list",
             "for my class", "for my students", "course material"],
  patent:   ["prior art", "fto", "freedom to operate", "patent",
             "patent landscape", "invention", "novelty search",
             "patent search", "ip landscape"],
  dossier:  ["dossier on", "due diligence", "background check",
             "prep me for", "competitor research", "investor diligence",
             "interview prep", "research my competitor", "background on"]
}

# Signals are case-insensitive literal phrases (multi-word substring match).
# Bracketed placeholders (e.g., "research [company]") are intentionally NOT
# signals — they over-trigger on generic "research X" queries that should
# fall back to general research, not auto-route to dossier. Specific phrases
# pair the verb with the noun ("dossier on", "background on") and route reliably.

For each specialist S:
  score[S] = count of SIGNALS[S] phrases matched in question (case-insensitive substring)

if max(score) >= 2:
  route_to = argmax(score)                                  # high confidence
elif max(score) == 1 and only one specialist has score 1:
  route_to = that specialist                                # weak match, single specialist
else:
  route_to = "fallback"                                     # ambiguous or no match — ask Q3

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