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:
- Delegation — Classified as specialist-domain. Routes there. User sees the specialist's output.
- Fallback execution — Classified as general research. Runs own plan → search → synthesize workflow.
- 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
| Specialist | Routing signals | Domain | |---|---|---| | pulse | reddit / hn / x / buzz / sentiment / trending / "what's people saying" / "pulse on" / "take the pulse" / "current conversation" | Multi-source recency research | | grants | NIH / grant / R01 / K-award / RePORTER / NOSI / "grants for" / FDA / "study section" / "principal investigator" | NIH grant-funding intelligence | | litreview | literature review / PICO / SPIDER / systematic review / "review papers on" / meta-analysis | Academic literature orientation | | syllabus | syllabus / course outline / curriculum / "reading list" / "for my class" / "for my students" | Course supplementary reading | | patent | prior art / FTO / freedom to operate / patent / "patent landscape" / invention / novelty search / "ip landscape" | Patent prior-art + landscape | | dossier | "dossier on" / "due diligence" / "background check" / "prep me for" / "competitor research" / "investor diligence" / "interview prep" / "background on" | Decision-grade entity research |
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**