一键导入
contribute-skill
One-command skill contribution — generate a SKILL.md from your domain expertise and submit to GitHub Issues for maintainer review
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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One-command skill contribution — generate a SKILL.md from your domain expertise and submit to GitHub Issues for maintainer review
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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 export.
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, set up Docker/Singularity/Apptainer containers for fMRIPrep, choose output spaces (MNI152NLin2009cAsym, fsaverage, fsLR/CIFTI), configure susceptibility distortion correction (SDC), extract or interpret the confounds table, resample to surface/grayordinates, cite fMRIPrep and its dependencies, or debug fMRIPrep crashes and hangs.
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 with brain connectivity matrices, connectomes, graph theory on brain networks, parcellated brain data (Schaefer, Cammoun, Desikan-Killiany), cortical surface templates (fsaverage, fsLR, CIVET, Conte69), network communication metrics, null model generation, spatial autocorrelation on brain maps, community detection in connectomes, or surface-based visualization with PyVista/PySurfer. Also trigger when the user mentions netneurotools, netneurolab, structure-function coupling, network neuroscience, brain graph analysis, or needs to fetch neuroimaging atlases and templates.
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 significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.
Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
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 the user needs animal pose estimation, behavior tracking, keypoint detection in videos, or mentions DeepLabCut/DLC/SuperAnimal.
| name | contribute-skill |
| description | One-command skill contribution — generate a SKILL.md from your domain expertise and submit to GitHub Issues for maintainer review |
| domain | meta-skill |
| version | 1.0.0 |
| review_status | ai-generated |
| dependencies | {"recommended":["paper-to-skill","research-literacy"]} |
This meta-skill lets researchers contribute new domain knowledge skills to the repository without needing Git expertise. It generates a properly formatted SKILL.md from the user's domain knowledge and submits it as a GitHub Issue for maintainer review.
Activate when the user:
Before starting the contribution process, you MUST:
For detailed methodology guidance, see skills/research-literacy/SKILL.md.
This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.
This skill supports two submission methods:
Direct submission via gh CLI (Recommended)
gh CLI installed and authenticatedgh auth status to checkgh auth loginManual submission via web browser
gh CLI requiredThe skill will check for gh availability and let the user choose their preferred method.
Q1: Where does this knowledge come from?
Q2: What is the skill about? (free text — brief description of the methodology)
Q3: Suggested skill name?
If the source is a paper with DOI/title, check whether the paper-to-skill meta-skill would be more appropriate. If the user has the full paper available, suggest: "Since you have the full paper, the paper-to-skill skill can do a more thorough extraction. Would you like to use that instead?"
Generate a SKILL.md following the project conventions. The generated skill MUST include:
YAML Frontmatter:
---
name: "[Human-Readable Skill Name]"
description: "[One-sentence summary]"
domain: "[subdomain]"
version: "1.0.0"
review_status: "ai-generated"
papers:
- "[Author, Year]"
dependencies:
required:
- research-literacy
---
Required Sections (in order):
Purpose — What domain knowledge this skill encodes
When to Use This Skill — Trigger conditions
Research Planning Protocol — Standard preamble (adapt from template):
## Research Planning Protocol
Before executing the domain-specific steps below, you MUST:
1. **State the research question** — What specific question is this analysis/paradigm addressing?
2. **Justify the method choice** — Why is this approach appropriate? What alternatives were considered?
3. **Declare expected outcomes** — What results would support vs. refute the hypothesis?
4. **Note assumptions and limitations** — What does this method assume? Where could it mislead?
5. **Present the plan to the user and WAIT for confirmation** before proceeding.
For detailed methodology guidance, see `skills/research-literacy/SKILL.md`.
Verification Notice — Standard disclaimer:
## ⚠️ Verification Notice
This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please [open an issue](https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/issues).
Domain-specific content — The actual skill logic with:
Content Quality Rules:
(Author, Year)references/ subdirectoryDisplay the complete generated SKILL.md to the user.
First, check gh CLI availability:
gh auth status 2>&1
Then present submission options using AskUserQuestion:
If gh is available:
If gh is NOT available:
You MUST wait for explicit user confirmation before proceeding to Step 4.
Based on the user's choice in Step 3:
gh CLICreate a GitHub Issue with the community-skill label:
gh issue create \
--repo "HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills" \
--title "[Community Skill] skill-name-here" \
--label "community-skill" \
--body "ISSUE_BODY_HERE"
Issue body format:
## Contributed Skill: `skill-name-here`
### Source
- **Knowledge source**: [paper / experience / session / other]
- **Key references**: [papers cited in the skill]
### Contributor Notes
> [Any additional context from the user about this skill]
### Generated SKILL.md
<details>
<summary>Click to expand full SKILL.md</summary>
[Full SKILL.md content here]
</details>
---
*Submitted via the `contribute-skill` meta-skill.*
On success, display the Issue URL to the user:
✅ Skill submitted successfully!
🔗 Issue URL: [URL]
The maintainers will review your contribution. Thank you!
On failure, fall back to Option B.
Save the SKILL.md locally to the current directory as skill-name-here.md
Prepare the issue body and save it as skill-name-here-issue.md
Open the GitHub Issues page:
xdg-open "https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/issues/new?labels=community-skill&title=%5BCommunity%20Skill%5D%20skill-name-here" 2>/dev/null || \
open "https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/issues/new?labels=community-skill&title=%5BCommunity%20Skill%5D%20skill-name-here" 2>/dev/null || \
echo "Please open: https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/issues/new?labels=community-skill"
✅ Files saved:
- skill-name-here.md (the skill)
- skill-name-here-issue.md (issue body to copy)
🌐 Opening GitHub Issues in your browser...
📋 Next steps:
1. Copy the content from skill-name-here-issue.md
2. Paste it into the issue body
3. Submit the issue
The maintainers will review your contribution. Thank you!
Save the SKILL.md to the current directory as skill-name-here.md:
✅ Skill saved to: skill-name-here.md
You can submit it later by:
1. Opening https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/issues/new
2. Using the label "community-skill"
3. Copying the skill content into the issue
Before generating the skill, verify each piece of content against the litmus test:
"Would a competent programmer who has never taken a cognitive science course get this wrong?"
Flag any borderline items to the user for their decision.