Research

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

Research prediction-market events, venues, underliers, liquidity, and news context for Itô basket workflows. Use for read-only market intelligence, API-gated Itô exploration, and source-grounded prediction-market briefings without investment advice or live trading.

Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.

Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.

Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/...

Multi-agent automated scientific discovery for diseases — given a disease name, Robin generates and ranks experimental assays, proposes therapeutic candidates, and (optionally) analyzes wet-lab data. Open-source, Apache-2.0. Use when the user wants an end-to-end "I have a disease, give me hypotheses to test" workflow rather than a single literature lookup.

Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography.

Deep, high-reasoning literature synthesis via FutureHouse's Falcon agent (LITERATURE_HIGH job). Use when the user wants a thematic review, gap analysis, or systematic synthesis across many papers — not a single fact lookup. Costs more credits and takes minutes longer than Crow but produces SOTA-quality scholarly output.

"Has anyone done this before?" — precedent search across the scientific literature via FutureHouse's Owl agent (formerly HasAnyone). Use when the user wants to know whether a specific experiment, technique, measurement, drug-target combination, or method has ever been published. Returns a yes/no-grounded answer with the closest matching prior work.

Fast scientific literature Q&A with citations via FutureHouse's Crow agent (production PaperQA2). Use when the user wants a single, well-cited answer drawn from the published scientific literature — biology, chemistry, medicine, ML, etc. Handles one focused question per call. For multi-paper thematic synthesis use Falcon; for "has anyone done X" precedent queries use Owl.

Generate Wikipedia-style scientific articles section-by-section by orchestrating PaperQA2 over a topic-specific corpus. Reproduces the WikiCrow recipe used by FutureHouse to write the gene articles at wikicrow.ai. Use when the user wants a structured, fully-cited long-form article on a scientific topic (gene, protein, disease, drug, mechanism) rather than a single Q&A answer.

Write Related Work sections that compare and contrast prior work with your approach. Organize by theme, cite broadly, and explain how your work differs. Use when writing or improving the Related Work section of a paper.

Write point-by-point rebuttals to reviewer comments. Extract concerns from reviews, generate evidence-based responses, and format as a structured rebuttal document. Use after receiving peer review feedback.

Write a specific section of an academic paper (Abstract, Introduction, Background, Related Work, Methods, Experiments, Results, Discussion/Conclusion) with section-specific guidance and two-pass refinement. Use when the user wants to write, draft, or improve a paper section.

12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

Design research plans and paper architectures. Given a research topic or idea, generate structured plans with methodology outlines, paper structure, dependency-ordered task lists, UML diagrams, and experiment designs. Use when starting a new research project or paper.

Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.

Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.

Generate complete academic survey papers using multi-LLM parallel outline generation, RAG-based subsection writing, citation validation, and local coherence enhancement. Based on AutoSurvey pipeline. Use for writing comprehensive literature surveys.

Convert a completed paper into presentation slides (Beamer LaTeX) or poster. Extract key figures, tables, equations, and create a narrative flow for oral presentation. Identified gap in existing tools — designed from best practices.

Design research plans and paper architectures. Given a research topic or idea, generate structured plans with methodology outlines, paper structure, dependency-ordered task lists, UML diagrams, and experiment designs. Use when starting a new research project or paper.