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SkillsMP は NeuroAIHub/BrainPilot から 69 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 69 件中 40 件を表示しています。
Audit scientific implementation, exported-model equivalence, dependency completeness, manifests, packaging, and isolated inference. Use when claims depend on code, trained artifacts, evaluators, or deployable entry points.
原文の言語: 英語
Audit scientific data semantics, sample and label alignment, leakage, group splits, preprocessing boundaries, and train-to-inference transforms. Use for any result based on datasets, feature matrices, tensors, repeated observations, or learned preprocessing.
原文の言語: 英語
Audit numeric, artifact, log, citation, and cross-report claims against inspectable evidence. Use for reports, syntheses, benchmark claims, external citations, or conflicting Expert outputs.
原文の言語: 英語
Coordinate iterative evidence and reliability reviews between BrainPilot's Principal Investigator and Auditor. Use when PI needs to audit its own draft, an Expert result, or a multi-agent synthesis; when Auditor receives such a review task; or when a previous…
原文の言語: 英語
Audit whether method discovery, comparison, representative real-data validation, collapse diagnostics, pruning, and selection evidence support claims of suitability or superiority. Use for research-method selection, empirical evaluation, benchmarking,…
原文の言語: 英語
Create or update the canonical Markdown inventory of task-relevant research data. Engineer must invoke this skill before creating or updating any data inventory, data contract, or dataset-coverage summary that downstream agents will use.
原文の言語: 英語
Research a bounded factual, documentation, API, or literature question from authoritative sources and save a self-contained Markdown report with claim-level citations. Use for reading-heavy evidence gathering, not experiment execution or data analysis.
原文の言語: 英語
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
原文の言語: 英語
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work
原文の言語: 英語
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
原文の言語: 英語
Use when executing implementation plans with independent tasks in the current session
原文の言語: 英語
Use when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions
原文の言語: 英語
Use when you have a spec or requirements for a multi-step task, before touching code
原文の言語: 英語
Use when creating new skills, editing existing skills, or verifying skills work before deployment
原文の言語: 英語
Curate BrainPilot Trace Events into human-readable research Episodes, appropriately granular nodes, and direct depends_on relationships. Use when a report contains multiple settings, results, analyses, visualizations, findings, or conclusions; when Episode…
原文の言語: 英語
Domain knowledge for building extracellular electrophysiology pipelines with SpikeInterface: loading data with extractors, preprocessing, running spike sorters, post-processing via SortingAnalyzer, quality metrics, curation, comparison, visualization, and…
原文の言語: 英語
Preprocess task-based or resting-state fMRI data with fMRIPrep — a robust, BIDS-App preprocessing pipeline built on FSL, ANTs, FreeSurfer, AFNI, and Nilearn. Use this skill whenever the user asks to preprocess fMRI/BOLD data, run fMRIPrep on a BIDS dataset,…
原文の言語: 英語
Domain-validated guidance for network neuroscience analysis using netneurotools: datasets, brain network metrics, connectivity consensus, modularity, spatial statistics, null models, and cortical surface visualization. Use this skill whenever the user works…
原文の言語: 英語
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical…
原文の言語: 英語
Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
原文の言語: 英語
Checks whether the uv Python package manager is installed and installs it if missing. Ensures uv is on PATH. Use when another skill requires uv as a prerequisite.
原文の言語: 英語
Toolbox for markerless animal pose estimation with DeepLabCut. Covers single/multi-animal tracking, SuperAnimal pretrained models, 2D/3D pose estimation, keypoint labeling GUI, model training/evaluation, video analysis, and behavioral quantification. Use when…
原文の言語: 英語
One-command skill contribution — generate a SKILL.md from your domain expertise and submit to GitHub Issues for maintainer review
原文の言語: 英語
Step-by-step guidance for contributing a new skill to the NeuroAIHub/awesome_cognitive_and_neuroscience_skills repository via GitHub Pull Request, including SKILL.md format requirements, quality rules, and PR checklist
原文の言語: 英語
Interactive skill that guides extraction of research paradigms and methodological techniques from cognitive science papers into structured, reusable skills
原文の言語: 英語
Convert a GitHub repository or local codebase into a well-structured Claude Code skill with progressive disclosure. Use this skill whenever the user provides a GitHub URL or local repo path and asks to turn it into a skill, create a skill from a repo, or…
原文の言語: 英語
One-command community case sharing — capture research context from your session and submit to GitHub Discussions
原文の言語: 英語
Generate and share anonymized skill usage statistics to help the community understand which skills are most valuable
原文の言語: 英語
Interactive skill verification — assess accuracy of parameters, citations, and methodology through structured expert review
原文の言語: 英語
Domain-specific statistical power analysis guidance for cognitive and neuroscience research, encoding effect size priors and sample size recommendations by modality
原文の言語: 英語
Domain-specific statistical modeling guidance for cognitive science and neuroscience, encoding when and how to apply mixed models, correction methods, Bayesian approaches, and effect size reporting
原文の言語: 英語
Domain-specific visualization best practices for cognitive and neuroscience data, encoding plot type selection, color standards, and publication formatting
原文の言語: 英語
Core scientific methodology principles: research planning, method justification, assumption checking, and human-in-the-loop decision making for cognitive science and neuroscience
原文の言語: 英語
Domain-validated guidance for designing Alternative Uses Task (AUT) experiments measuring divergent thinking, with parameters for AI-augmented and traditional conditions
原文の言語: 英語
Expert guidance for selecting and parameterizing cognitive psychology experimental paradigms based on research questions
原文の言語: 英語
Domain-validated guidance for SEM-based mediation analysis of creative self-efficacy and moderation by baseline creativity in AI-augmented creativity research
原文の言語: 英語
Domain-validated multi-dimensional scoring system for divergent thinking tasks, including fluency, flexibility, originality, and automated semantic distance methods
原文の言語: 英語
Expert guidance on selecting, fitting, and evaluating drift-diffusion models for two-choice response time data in cognitive science
原文の言語: 英語
Advises on when to use DDM vs. LBA vs. race models for choice-RT data based on experimental design and research goals
原文の言語: 英語
Domain-validated decision logic, formulas, and interpretation guidelines for applying Signal Detection Theory to cognitive science data
原文の言語: 英語