awesome_cognitive_and_neuroscience_skills

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# Awesome Cognitive and Neuroscience Skills

![Skills](https://img.shields.io/badge/skills-50-blue)
![License](https://img.shields.io/badge/license-MIT-green)
![Platform](https://img.shields.io/badge/platform-Claude%20Code-blueviolet)
![Contributions Welcome](https://img.shields.io/badge/contributions-welcome-brightgreen)

**[Quick Start](#quick-start) · [Skills Catalog](#skills-catalog) · [Evaluation](#evaluation) · [Community](#community) · [Call for Contributions](#call-for-contributions)**

> Skills that encode expert methodological knowledge for cognitive science and neuroscience research. One-command install, one-command paper-to-skill, one-command contribute — vibe researching starts here.
>
> **Vibe research**: Complete specialized research subtasks (experimental design, data preprocessing, visualization, etc.) through conversational CLI interaction — no manual searching through documentation or papers.

> **⚠️ AI-Generated Content Notice**
>
> All skills in this repository were generated by AI from academic papers and other publicly available sources. While automated hallucination checks were applied during generation, the content has **not been individually verified by human domain experts**. Specific numerical parameters, citations, and methodological recommendations may contain errors.
>
> **You MUST independently verify all parameters and citations before using them in research.** This repository is not a substitute for peer-reviewed guidelines or expert consultation. Contributions that verify, correct, or improve existing skills are strongly encouraged — see [CONTRIBUTING.md](CONTRIBUTING.md).

## Quick Start

Install directly in Claude Code:

```
/plugin marketplace add HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills
/plugin install awesome-cognitive-and-neuroscience-skills@awesome-cognitive-and-neuroscience-skills
```

Then `restart` Claude Code to activate the skills.

All 50 skills become immediately available. Skills activate automatically when you ask research methodology questions:

- "Help me design an oddball paradigm for N400 research"
- "What preprocessing pipeline should I use for my fMRI data?"
- "Which drift-diffusion model variant fits my task-switching RT data?"

Manage your installation:

```bash
/plugin list                                                      # View installed plugins and skills
/plugin update awesome-cognitive-and-neuroscience-skills           # Update to the latest version
/plugin update --all                                              # (Optional) Update all installed plugins
/plugin remove awesome-cognitive-and-neuroscience-skills           # Uninstall
```

**Enable auto-update (recommended):** Keep skills up-to-date automatically — `/plugin` → Marketplace → `awesome-cognitive-and-neuroscience-skills` → Enable auto-update.

Skills may cross-reference each other (e.g., `fmri-preprocessing-pipeline-guide` feeds into `fmri-glm-analysis-guide`), but each skill is independently usable.

---

## Classification Criteria

Skills are classified primarily by **research content domain** — the scientific questions and phenomena they address. The classification follows standard academic disciplinary boundaries as recognized by major scientific societies (e.g., Cognitive Science Society, Society for Neuroscience) and established textbooks.

**Rules:**

1. Each skill is assigned to the content domain whose research questions it primarily serves
2. Skills that substantially serve multiple existing categories are listed in all applicable categories
3. Cross-Domain Foundation skills encode methodology applicable across most or all domains
4. **Others** contains skills that do not fit neatly into a single content domain — for example, cross-cutting methodology that spans multiple neuroimaging modalities rather than addressing a specific research question

---

## Skills Catalog

> **50 skills** across 16 categories

### Meta-Skills (7)

| Skill | Description |
|---|---|
| [`paper-to-skill`](skills/paper-to-skill/) | Extract methodology from papers into structured, reusable skills |
| [`repo-to-skill`](skills/repo-to-skill/) | Convert a GitHub repo or local codebase into a structured Claude Code skill |
| [`contribute-skills-via-pr`](skills/contribute-skills-via-pr/) | Step-by-step guidance for contributing a new skill via GitHub Pull Request |
| [`share-case`](skills/share-case/) | One-command community case sharing to GitHub Discussions |
| [`share-usage`](skills/share-usage/) | Share anonymized skill usage statistics to help prioritize development |
| [`contribute-skill`](skills/contribute-skill/) | One-command skill contribution via GitHub Issues |
| [`verify-skill`](skills/verify-skill/) | Interactive skill verification with structured expert review |

### Cross-Domain Foundation (4)

| Skill | Description |
|---|---|
| [`research-literacy`](skills/research-literacy/) | Core scientific methodology: research planning, method justification, assumption checking |
| [`cogsci-statistics`](skills/cogsci-statistics/) | Statistical modeling guidance: mixed models, corrections, Bayesian approaches, effect sizes |
| [`cogsci-visualization`](skills/cogsci-visualization/) | Visualization best practices: plot selection, color standards, publication formatting |
| [`cogsci-power-analysis`](skills/cogsci-power-analysis/) | Power analysis: effect size priors and sample size recommendations by modality |

### Cognitive Psychology (8)

| Skill | Description |
|---|---|
| [`signal-detection-analysis`](skills/signal-detection-analysis/) | SDT decision logic, formulas, and interpretation guidelines |
| [`drift-diffusion-model`](skills/drift-diffusion-model/) | DDM selection, fitting, and evaluation for choice-RT data |
| [`cognitive-paradigm-design`](skills/cognitive-paradigm-design/) | Paradigm selection and parameterization for cognitive experiments |
| [`evidence-accumulation-selector`](skills/evidence-accumulation-selector/) | DDM vs. LBA vs. EZ-diffusion vs. race model selection |
| [`visual-search-array-generator`](skills/visual-search-array-generator/) | Display parameters, set sizes, and array generation rules |
| [`alternative-uses-task-designer`](skills/alternative-uses-task-designer/) | AUT paradigm design for AI-augmented and traditional conditions |
| [`divergent-thinking-scoring`](skills/divergent-thinking-scoring/) | Fluency, flexibility, originality, and semantic distance scoring |
| [`creativity-self-efficacy-mediation`](skills/creativity-self-efficacy-mediation/) | SEM-based mediation/moderation in AI-augmented creativity research |

### Psycholinguistics (3)

| Skill | Description |
|---|---|
| [`self-paced-reading-designer`](skills/self-paced-reading-designer/) | SPR experiment design: regions, timing, probes, spillover |
| [`reading-time-analysis`](skills/reading-time-analysis/) | Eye-tracking measures (FFD, GD, go-past, TRT) and LMM modeling |
| [`sentence-stimulus-norming`](skills/sentence-stimulus-norming/) | Cloze probability, plausibility, acceptability, and counterbalancing |

### EEG / ERP (4)

| Skill | Description |
|---|---|
| [`erp-analysis`](skills/erp-analysis/) | ERP pipeline from preprocessing through statistical testing |
| [`eeg-paradigm-designer`](skills/eeg-paradigm-designer/) | EEG paradigm design to isolate specific ERP components |
| [`eeg-preprocessing-pipeline-guide`](skills/eeg-preprocessing-pipeline-guide/) | Filtering, ICA/ASR, re-referencing, interpolation, QC |
| [`mne-python-guide`](skills/mne-python-guide/) | MNE-Python pipeline: loading, preprocessing, epoching, ERP/ERF, time-frequency, source localization, decoding |

### fMRI / Neuroimaging (8)

| Skill | Description |
|---|---|
| [`fmri-preprocessing-pipeline-guide`](skills/fmri-preprocessing-pipeline-guide/) | Motion correction, slice timing, normalization, smoothing, QC |
| [`fmriprep`](skills/fmriprep/) | Run fMRIPrep (25.2.x LTS) end-to-end: Docker/Apptainer/pip install, CLI flags, workflow internals, output spaces & CIFTI, SDC/fieldmaps, confounds table, BIDS filters, troubleshooting, citations |
| [`fmri-glm-analysis-guide`](skills/fmri-glm-analysis-guide/) | GLM specification: HRF, design matrix, contrasts, inference |
| [`neural-decoding-analysis`](skills/neural-decoding-analysis/) | Decoding, RSA, temporal generalization, encoding models |
| [`fmri-task-design-guide`](skills/fmri-task-design-guide/) | Block vs. event-related vs. mixed, jittering, design efficiency |
| [`brain-connectivity-modeler`](skills/brain-connectivity-modeler/) | PPI, DCM, Granger causality, graph theory with selection tree |
| [`pycortex-guide`](skills/pycortex-guide/) | Cortical surface visualization: flatmaps, WebGL viewers, volume-to-surface mapping, FreeSurfer/fMRIPrep integration |
| [`netneurotools-guide`](skills/netneurotools-guide/) | Network neuroscience toolbox: brain graph construction, connectome analysis, network metrics, surrogate networks, and neuroimaging workflow utilities for fMRI/structural connectivity research |

### Computational Modeling (3)

| Skill | Description |
|---|---|
| [`bayesian-cognitive-model-builder`](skills/bayesian-cognitive-model-builder/) | Hierarchical Bayesian models with Stan/PyMC: priors, MCMC, PPC |
| [`act-r-model-builder`](skills/act-r-model-builder/) | ACT-R: chunk types, productions, subsymbolic parameters |
| [`parameter-recovery-checker`](skills/parameter-recovery-checker/) | Recovery study design, simulation, identifiability validation |

### Computational Neuroscience (4)

| Skill | Description |
|---|---|
| [`neural-population-analysis-guide`](skills/neural-population-analysis-guide/) | PCA, GPFA, dPCA, jPCA for neural population analysis |
| [`spiking-network-model-builder`](skills/spiking-network-model-builder/) | LIF/AdEx/HH neurons, synapse models, STDP, connectivity |
| [`neural-decoding-analysis`](skills/neural-decoding-analysis/) | Decoding, RSA, temporal generalization, encoding models |
| [`brain-connectivity-modeler`](skills/brain-connectivity-modeler/) | PPI, DCM, Granger causality, graph theory with selection tree |

### Cellular / Molecular Neuroscience (2)

| Skill | Description |
|---|---|
| [`calcium-imaging-analysis-guide`](skills/calcium-imaging-analysis-guide/) | Motion correction, ROI extraction, neuropil correction, spike inference |
| [`optogenetics-protocol-designer`](skills/optogenetics-protocol-designer/) | Opsin choice, light delivery, pulse protocols, fiber placement |

### Electrophysiology (1)

| Skill | Description |
|---|---|
| [`spikeinterface-skill`](skills/spikeinterface-skill/) | Extracellular electrophysiology pipeline with SpikeInterface: extractors for ~40 acquisition formats, preprocessing, spike sorters (Kilosort family, Mountainsort, SpykingCircus, Tridesclous, Lupin, HerdingSpikes) via native or Docker/Singularity runs, `SortingAnalyzer` post-processing, quality metrics, curation, comparison, plot_* widgets, export to Phy/IBL/Pynapple |

### Clinical / Neuropsychology (2)

| Skill | Description |
|---|---|
| [`neuropsych-battery-selector`](skills/neuropsych-battery-selector/) | Test battery selection matched to cognitive deficit profiles |
| [`lesion-symptom-mapping-guide`](skills/lesion-symptom-mapping-guide/) | VLSM, SVR-LSM, disconnection and network lesion mapping |

### Developmental Cognition (1)

| Skill | Description |
|---|---|
| [`infant-looking-time-designer`](skills/infant-looking-time-designer/) | Habituation, preferential looking, VOE with age-appropriate parameters |

### Social Cognition (1)

| Skill | Description |
|---|---|
| [`tom-task-selector`](skills/tom-task-selector/) | ToM task selection by population, age, and construct |

### Animal Behavior (1)

| Skill | Description |
|---|---|
| [`deeplabcut`](skills/deeplabcut/) | Markerless animal pose estimation with DeepLabCut: single/multi-animal tracking, SuperAnimal pretrained models, 2D/3D pose, keypoint labeling GUI, model training/evaluation, video analysis |

### Writing (1)

| Skill | Description |
|---|---|
| [`markdown-report-writing`](skills/markdown-report-writing/) | Guide for well-structured, visually polished Markdown reports across GitHub and Obsidian: three-layer writing model, Mermaid diagrams, math, tables, callouts, cross-renderer compatibility |

### Others (2)

> Skills in this category do not fit neatly into the content-domain categories above, because they address **cross-cutting neuroimaging methodology** that spans multiple modalities (fMRI, EEG, MEG) rather than a specific research domain.

| Skill | Description | Reason |
|---|---|---|
| [`neuroimaging-power-guide`](skills/neuroimaging-power-guide/) | Sample-size planning for fMRI/EEG using effect-size benchmarks | Cross-cutting methodology spanning multiple neuroimaging modalities |
| [`neuroimaging-sample-size-calculator`](skills/neuroimaging-sample-size-calculator/) | Simulation-based sample-size calculation from pilot data | Cross-cutting methodology spanning multiple neuroimaging modalities |

---

## Build Your Own Skills

Two meta-skills help you create new skills:

- [`paper-to-skill`](skills/paper-to-skill/) — provide a paper (PDF or text) and it scaffolds a first-draft SKILL.md from the methodology
- [`repo-to-skill`](skills/repo-to-skill/) — provide a GitHub URL or local repo path and it converts the codebase into a structured skill

See [CONTRIBUTING.md](CONTRIBUTING.md) for skill authoring conventions (naming, citation format, required frontmatter).

---

## Evaluation

We tested whether skills improve AI methodology advice on three real neuroscience scenarios (EEG preprocessing, fMRI RSA, hippocampal population analysis).

### Showcase: Before vs. After Skills

**Case 1 — EEG Preprocessing for Emotion Recognition**

| | Without Skills | With Skills |
|---|---|---|
| Detail level | 264 words, generic pipeline | 1350 words, expert-grade pipeline |
| Key gaps / strengths | No filter type, no ICA classifier, no CV strategy, no N=15 warning | 1 Hz high-pass FIR zero-phase (Winkler 2015), ICLabel, LOSO-CV, N=15 warning + 16-item checklist |

**Case 2 — fMRI RSA in Visual Cortex**

| | Without Skills | With Skills |
|---|---|---|
| Detail level | Generic distance metric, split-half only | Unbiased estimator with noise ceiling bounds |
| Key gaps / strengths | "1 - Pearson" distance, no circularity warning, no crossnobis | Crossnobis distance (Walther 2016), Spearman rank, noise ceiling (Nili 2014), circularity warning (Kriegeskorte 2009) |

**Case 3 — Hippocampal Population Analysis**

| | Without Skills | With Skills |
|---|---|---|
| Detail level | NMF with k=5-15, z-score only | Demixing + robust normalization + diagnostics |
| Key gaps / strengths | No soft normalization, no place cell warning, no bin size guidance | dPCA demixing (Kobak 2016), soft normalization, parallel analysis, bin size 100-200 ms, CA1 place cell warning |

### Auto Scores (keyword + method coverage against expert ground truth)

| Scenario | Haiku | Haiku + skill | Sonnet | Sonnet + skill | GPT-5.2 | GPT-5.2 + skill |
|---|:---:|:---:|:---:|:---:|:---:|:---:|
| EEG | 62.8% | **71.8%** | 62.8% | **71.8%** | 78.2% | 74.4% |
| fMRI RSA | 66.7% | **82.5%** | 66.7% | **85.0%** | 69.2% | 69.2% |
| Ephys | 27.6% | **62.2%** | 27.6% | **62.2%** | 39.7% | **64.1%** |
| **Δ (uplift)** | | **+9.0 / +15.8 / +34.6** | | **+9.0 / +18.3 / +34.6** | | **-3.8 / 0.0 / +24.4** |

---

## Community

### Share Your Experience

Used a skill in your research? Share your case directly from Claude Code:

```
"share this case"
```

The [`share-case`](skills/share-case/) meta-skill captures your experience and submits it to [GitHub Discussions](https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/discussions). You control exactly what gets shared.

**Two submission methods:**

1. **Direct submission via `gh` CLI** (Recommended — one command, instant submission)
   ```bash
   sudo apt install gh   # Ubuntu/Debian — other OS see https://cli.github.com/
   gh auth login         # authorize your GitHub account (one-time setup)
   ```

2. **Browser submission** (No installation required — opens GitHub in browser, you copy and paste)

The skill automatically detects which method is available and lets you choose.

### Contribute Domain Knowledge

Have expertise to share? Ways to contribute:

| Say this | What happens |
|---|---|
| "share this case" | Share a usage experience → GitHub Discussion |
| "share my skill usage" | Share anonymized usage statistics → GitHub Discussion |
| "contribute a skill" | Generate and submit a new skill → GitHub Issue |
| "contribute a skill via PR" | Full PR workflow with SKILL.md format guidance → GitHub Pull Request |
| "verify a skill" | Verify an existing skill's accuracy → GitHub Discussion |

All meta-skills support both **`gh` CLI** (one-command submission) and **browser** (manual copy-paste) methods. You review everything before it's posted.

> **Tip:** Multi-phase skills (like `verify-skill`) involve many rounds of conversation. As the context grows longer, the AI may lose track of later steps — especially the final GitHub submission. If you notice the agent has finished reviewing but hasn't submitted, just remind it: *"Please continue with the remaining steps in the skill"* to get it back on track.

Browse community contributions: [Discussions](https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/discussions) · [Community Cases](examples/community-cases/)

---

## Call for Contributions

All skills are currently `ai-generated` and await community verification. We welcome:

- **Domain experts** (PhD-level) to verify methodological accuracy — this is the **highest-impact** way to help
- **Researchers** to spot-check citations and numerical parameters
- **Practitioners** to test skills in real analysis workflows and report issues
- **New skills** in domains not yet covered (e.g., affective science, embodied cognition, neuroethics)

See [CONTRIBUTING.md](CONTRIBUTING.md) for full guidelines, including how to verify citations, submit new skills, and report errors.

---

## License

MIT License. See [LICENSE](LICENSE) for details.

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