Research

# 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 | 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. ```python 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 ``` **Implementation:** `scripts/classifier.py --question "..."` returns the routing decision + matched signals + per-specialist scores. Use it; don't re-implement. ## Phase 3a: Specialist Delegation (≥2 signals OR single weak match) When delegating: 1. Pass the user's question **verbatim** plus the output preference (Q2) 2. **Let the specialist run its own grill-me intake** — do NOT pre-answer specialist questions 3. Return specialist output as the user-visible result 4. Tag the result with `[Delegated to: research → {specialist}]` in the chat output so the user knows what skill produced it 5. Tag the audit log via `scripts/routing_transparency_logger.py --action record_delegation` ## Phase 3b: Own Fallback Workflow If routing produced no specialist match, run the 8-step fallback. ### Step 1: Decompose Break the research question into 3–5 sub-questions. Use the framework: what / why / how / who / what's next. Show the decomposition to the user before searching. Use `scripts/fallback_decomposer.py --question "..."` for a deterministic starting point. ### Step 2: Source Selection For each sub-question, choose source(s) deterministically: - **Recency-sensitive** → WebSearch + WebFetch + (optionally Reddit/HN if signal) - **Technical specs / docs** → WebSearch + WebFetch - **Academic** → Consensus MCP if connected; otherwise WebSearch with `scholar.google.com` site filter - **Data / numbers** → WebSearch for sources; then WebFetch for primary documents - **Person / company entity-level** → consider routing to `dossier` (offer override) ### Step 3: Search Sequential per sub-question. 1 q/sec etiquette. Per source: 2–4 queries, broad-to-narrow. ### Step 4: Read + Extract For each result that looks high-signal: WebFetch and extract the relevant section. Note the source URL. ### Step 5: Synthesize Per sub-question: 2–4 paragraphs answering it with inline citations. Surface disagreement when sources disagree. ### Step 6: Cross-Cutting Patterns After per-sub-question synthesis: 1–2 paragraphs of patterns across sub-questions — consensus, controversy, gaps. ### Step 7: Output Markdown brief by default (Q2 choice). DOCX if user picked document mode. ### Step 8: Audit Log Three-count summary (sent / received / cited) + per-source list with reliability tier (primary / secondary / tertiary). ## Routing Transparency Protocol (Mandatory) After classification, the skill **always**: 1. **States the decision** in one sentence: "Routing to `litreview` because you mentioned PICO and meta-analysis (2 signals)." 2. **Offers override**: "If you want general research instead OR a different specialist, say so now. Otherwise proceeding in 5 seconds." 3. **Waits 1 turn** for confirmation (or auto-proceeds after 5s in interactive contexts). 4. **If user overrides** → accept, re-route, log the override via `routing_transparency_logger.py --action record_override`. **Never delegates silently.** This is the trust-building property that makes the hybrid pattern work. ## Output Format ### Markdown brief (Q2 = quick chat briefing) ```markdown # [Research Question] — Briefing *Generated: [DATE] | Routed: [delegated specialist | fallback]* ## TL;DR [2-3 sentences] ## Findings ### [Sub-question 1] [2-4 paragraphs with inline citations] ### [Sub-question 2] ... ## Cross-Cutting Patterns [1-2 paragraphs] ## Sources [Numbered list with hyperlinks, reliability tier per source] ## Audit [Three counts + per-source tier + failures] ``` ### DOCX (Q2 = standalone document) Use the standard research-pack DOCX patterns: Arial 12pt, navy headings, blue table headers, hyperlinked sources, mandatory audit log section. Reference the `docx` skill for setup. ## Audit Log Requirement (Fallback Mode) ``` Queries sent: N Sources received: M Sources cited: K Failures: F (3-consecutive-failures triggered: yes/no) Per-source tier: [URL — primary | secondary | tertiary] Routing decision: fallback (no specialist matched) Sub-questions: [list] ``` All routing decisions + overrides also logged to `~/.research_sessions/<session>.json` via `routing_transparency_logger.py`. ## Failure Modes | Failure | Behavior | |---|---| | Classification ambiguous (≤1 signal) | Ask Q3 (domain disambiguation). | | Specialist delegation fails | Note in chat. Offer to retry or fall back to general research. | | User overrides routing | Accept. Re-route to chosen specialist or fallback. Log the override. | | Fallback search returns thin results | Surface explicitly. Suggest the question may be too niche or too new. Do not fabricate. | | 3 consecutive tool failures in fallback | Stop, alert user, share what was collected. | | Question is non-research (e.g., "write me code") | Decline politely. Suggest the user invoke an appropriate skill. | | Sub-question can't be answered | Note in synthesis as "limited public signal on this"; don't omit silently. | | Output format mismatch | Honor Q2 preference; if format unavailable, fall back to markdown with note. | | Specialist skill missing from environment | Skip it in classification scoring; route to fallback or next-best specialist. | ## Anti-Patterns Rejected - LLM-reasoned classification (must be deterministic keyword + intent matching) - Silent delegation (always surface routing decision) - Refusing to route to a specialist when ≥2 signals match - Routing to a specialist when classification is genuinely ambiguous (≤1 signal across all) - Pre-answering the specialist's grill-me intake (let it run its own) - Running fallback when a specialist would clearly do better - Fabricating sources in fallback when search is thin - Skipping audit log in fallback mode - Treating "dossier on [company]" as fallback when `dossier` is the right specialist (the verb-noun-paired phrase, not the generic "research X" form, is what routes) - Treating "what are people saying about X" as fallback when `pulse` is the right specialist - Auto-routing generic "research [topic]" queries to a specialist when the user hasn't paired the verb with a specialist-specific noun (e.g., "research Microsoft" alone is ambiguous — could be dossier or general; ask Q3 instead of guessing) ## Tooling ### Python (stdlib only) - **`scripts/classifier.py`** — Deterministic SIGNALS matching → routing decision + per-specialist score + matched phrases. `--question "..." --output json`. - **`scripts/routing_transparency_logger.py`** — JSON-backed audit log at `~/.research_sessions/<session>.json`. Records every routing decision, override, and delegation handoff. - **`scripts/fallback_decomposer.py`** — Heuristic question → 3–5 sub-questions using what / why / how / who / what's next framework. ### Reference Docs (each cites 7+ authoritative sources) - **`references/hybrid_router_architecture.md`** — router-vs-run trade-offs + routing transparency principle - **`references/deterministic_classification_canon.md`** — why keyword > LLM-reasoned for routing - **`references/fallback_workflow_canon.md`** — plan-decompose-search-synthesize methodology ## Dependencies - **`WebSearch`** + **`WebFetch`** — Required for fallback workflow - **Specialist skills** — Required for delegation: `pulse`, `grants`, `litreview`, `syllabus`, `patent`, `dossier`. If a specialist is missing, the router skips it in classification and routes to fallback instead. - **Node.js `docx` library** — Required if user picks document output (Q2 = standalone) - **Consensus MCP** — Optional; used in fallback if academic sub-questions surface ## Trigger Phrases - "research [topic]" - "look into [topic]" - "what do we know about [topic]" - "investigate [topic]" - "find me information on [topic]" - "do some research on [topic]" - "I need to understand [topic]" - Any research request that doesn't obviously match a more-specific specialist --- **Version:** 1.0.0 **Source spec:** [`megaprompts/13-research-megaprompt.md`](../../../../megaprompts/13-research-megaprompt.md) **Build pattern:** Path B (direct conversion)