Claude Skills by qhjqhj00

This skill should be used when the user asks to "design agent tools", "create tool descriptions", "reduce tool complexity", "implement MCP tools", or mentions tool consolidation, architectural reduction, tool naming conventions, or agent-tool interfaces.

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

This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity.

This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.

Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, adaptive memory, dynamic ...

This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.

This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture, agent-assisted development, cost estimation, or choosing between LLM and traditional approaches.

This skill should be used when the user asks to "design agent tools", "create tool descriptions", "reduce tool complexity", "implement MCP tools", or mentions tool consolidation, architectural reduction, tool naming conventions, or agent-tool interfaces.

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

Open-weights chemistry reasoning model + verifiable reward functions from FutureHouse's ether0 (arXiv 2506.17238). Use to score model-generated chemistry outputs (SMILES validity, molecular completion, synthesis reasoning) against ground truth, or to run the open-weights ether0 model itself for chemistry reasoning. Also useful for visualizing molecules and reactions from SMILES.

Convert an ML research paper into a complete, runnable code repository. 3-stage pipeline from Paper2Code — Planning (UML + dependency graph) → Analysis (per-file logic) → Coding (dependency-ordered generation). Use for reproducing paper methods.

Design experiment plans with progressive stages — initial implementation, baseline tuning, creative research, and ablation studies. Plan baselines, datasets, hyperparameter sweeps, and evaluation metrics. Use when planning experiments for a research paper.

Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.

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.

This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity.

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.

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.

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 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.

Run PaperQA2 locally on a folder of scientific PDFs to get high-accuracy, fully-cited answers. Self-hosted, open-source RAG (Apache-2.0) — needs only an LLM key (OpenAI/Anthropic/local), no FutureHouse credits. Use when the user has a local corpus of papers and wants grounded answers, or wants to avoid the hosted Crow/Falcon for privacy / cost reasons.

Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.

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.

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.

"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.

Cheminformatics-grounded chemistry agent (Phoenix, the successor to ChemCrow) via the FutureHouse Platform. Use for retrosynthesis, reaction planning, molecular property prediction, SMILES manipulation, and proposing new molecules with chemistry tools backing the reasoning. Trigger on chemistry / drug-design / synthesis / molecule questions.

Aviary is FutureHouse's open-source gymnasium for defining and benchmarking LLM agents on scientific tasks (math, multi-hop QA, biological sequences, scientific literature search, Jupyter notebooks). Use when the user wants to evaluate an LLM agent on standardized scientific environments, build custom RL-style environments for agent training, or reproduce results from the Aviary paper.

Generate publication-quality LaTeX tables from experimental results. Convert JSON/CSV data to booktabs-styled tables with bold best results, multi-row layouts, and proper captions. Use when creating result tables, comparison tables, or ablation tables for papers.

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.

Make every number in the final PDF traceable to the exact code line that produced it. Uses \hypertarget/\hyperlink LaTeX commands and \num{formula} evaluated at compile time. Use for reproducibility and data integrity verification.

Hosted biological-data-analysis agent (Finch) on the FutureHouse Platform. Hands a dataset + question to Finch, which builds a Jupyter notebook that explores, analyzes, and interprets the data. Use when the user has a biological dataset (omics, imaging, clinical) and a research question, and wants a multi-step analysis with code + results, not just a literature answer.

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.

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.

Discover scientific equations from data using LLM-guided evolutionary search (LLM-SR). Multi-island algorithm with softmax-based cluster sampling, island reset, and LLM-proposed equation mutations. Use for symbolic regression and equation discovery.

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/...

Yuval Avidani's signature web-design defaults — Anton + Rubik display, Inter + Assistant body, pink/yellow/bone palette, paper grain, warm shadows, asymmetric layouts, GSAP-first motion, RTL/LTR bilingual. Apply automatically to ANY frontend / web request from Yuval — websites, landing pages, dashboards, React components, HTML/CSS, marketing pages, design systems. Trigger words: website, landing page, web UI, React, component, HTML, CSS, frontend, dashboard, marketing page, design system, typ...

Review and create distinctive, production-grade frontend interfaces with high design quality and design system compliance. Evaluates using three pillars: frictionless insight-to-action, quality craft, and trustworthy building. USE FOR: PR reviews, design reviews, accessibility audits, design system compliance checks, creative frontend design, UI code review, component reviews, responsive design checks, theme testing, and creating memorable UI. DO NOT USE FOR: Backend API reviews, database sch...