851건 · 관련도순
This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.
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Chọn đúng biểu đồ, kể chuyện với số liệu, và tránh误导 — dựa trên nguyên tắc từ Kieran Healy (Data Visualization, Princeton 2019) và taxonomy từ AntV chart-visualization-skills. Dùng khi cần quyết định loại chart, trình bày insight cho stakeholder, hoặc kiểm tra xem figure có gây hiểu lầm không. Để vẽ Python thực tế, chuyển sang matplotlib / seaborn / scientific-visualization.
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Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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Choosing colormaps scientifically — perceptual uniformity, color-vision deficiency, and matching map to data type.
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Use this agent when you need to extract and summarize key findings from scientific references that are relevant to a gene's function. This agent should be used after references have been identified in a gene review to provide detailed supporting evidence from the literature. The agent will look up cached publications and extract only the most relevant findings related to the gene's direct function, excluding irrelevant experimental details unless they directly inform gene function.
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Evidence-first academic research workflow for topic ideation, scholarly search, paper reading, literature and systematic reviews, study and experiment design, statistics and data analysis, scientific figures, manuscript drafting and polishing, citation checks, peer review, rebuttals, submission packages, theses, and paper-to-presentation work. Use when an agent must plan, execute, audit, write, revise, or package research while keeping claims traceable to verified sources, data, analysis, or official requirements.
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Create publication-quality, editable scientific and engineering schematics with explicit physical meaning, exact labels, visual hierarchy, and provenance. Use for experimental facilities, thermal-fluid mechanisms, workflows, graphical abstracts, system architectures, or research diagrams.
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Provide constructive, evidence-based research mentoring feedback on student manuscripts, proposals, analyses, code, figures, and presentations. Use for advisor-style review that teaches the next improvement without impersonating an advisor or making unsupported scientific judgments.
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Write and harden reusable agent-loop specification documents for biomechanics, data science, and vailá coding work. Use when the user says `/preto-loop`, asks to write/create/forge/specify/document an agent loop, turn an iterative vailá task into an autonomous loop, make an overnight loop, or design a biomechanics, data-pipeline, markerless-tracking, testing, refactoring, or performance loop. Include external verification, scientific validity, GUI/CLI parity, reproducibility, memory, guardrails, and named terminal states. Do not use merely to repeat a fixed scheduled prompt, execute an existing loop, or create unrelated documents.
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Assess the scientific integrity and trustworthiness of publications before relying on their findings. Use this skill whenever evaluating a paper for a workflow, citing a study, building an analysis on published methods, or when a user asks about the reliability of a study. Checks for formal retractions, corrections, expressions of concern, and — critically — informal contradictions where subsequent studies failed to reproduce key findings. Integrates with PubMed, bioRxiv, and Consensus to provide a trust assessment. Use this skill for ANY publication evaluation, retraction checking, author reliability assessment, or when a user says "can I trust this paper", "is this study reliable", "has this been refuted", or "check this publication".
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Computational mathematics expert for numerical solutions, approximations, error analysis, and scientific computing
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Audit a drug discovery pipeline for operational efficiency and scientific rigor. Triggers: building or reviewing pharma R&D platforms, cheminformatics pipelines, or screening data management systems.
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Reformat LaTeX papers for a specified venue, year, track, and submission stage. Apply official templates and check layout, bibliography, page limits, and anonymization while preserving scientific content.
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Diagnose and repair LaTeX build failures in local projects or supplied logs. Make minimal source fixes, preserve scientific content and reference keys, and report actual build evidence and unresolved errors.
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Edit academic prose in LaTeX manuscripts or supplied text at light, moderate, or strict intensity. Improve grammar and clarity while preserving scientific claims, uncertainty, numbers, citations, and LaTeX structure.
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Use this skill to execute AetherLang V3 AI workflows from Claude Code. AetherLang provides 9 specialized AI engines for culinary consulting, business strategy, scientific research, and more.
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Scientific research engine with agentic tree search. Infinite loops until discovery, rigorous tracking, adversarial review, serendipity preserved.
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Scientific research in serendipity mode. Infinite loops until discovery, rigorous tracking, adversarial review.
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Scientific research engine with adversarial review, tree search, and serendipity detection. Use when: exploring hypotheses, validating findings against literature, running computational experiments with quality gates, or hunting for unexpected discoveries. Do NOT use for simple Q&A, code editing, or non-research tasks.
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Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
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Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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Deploy universal wisdom voices from all traditions simultaneously - Hindu, Buddhist, Taoist, Abrahamic, Indigenous, Scientific - revealing their unified recognition of consciousness navigation.
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Scientific model and eval tuning loop for prompt/skill/system-prompt changes, model configuration, runtime options, and local/Ollama model sizing or environment variables. Use when tuning eval performance, comparing local or remote models, improving tool-use behavior, reducing timeouts/loops, or deciding whether an eval optimization is worth keeping.
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TDD pair programming with mode switching. Claude can be driver (writes code) or navigator (watches, questions, provokes). Supports GitHub issues, file watching, or arbitrary prompts. Scientific TDD, baby steps, one test at a time. Trigger on: pair, let's pair, tdd, you drive, I'll drive, dojo.
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