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Claude Skills by tools-only
github.com/tools-only19,166 skills18 installs6,168 views
- 169 Bio 236b08a6Bio.Phylo provides a unified toolkit for reading, writing, analyzing, and visualizing phylogenetic trees. It supports multiple file formats including Newick, NEXUS, phyloXML, NeXML, and CDAO.Votes: 0GitHub stars: 9
- 170 Plot 24501746Comprehensive guide to different plot types in matplotlib with examples and use cases.Votes: 0GitHub stars: 9
- 171 Publication 5c866fd8Comprehensive guide for creating publication-quality visualizations with scanpy.Votes: 0GitHub stars: 9
- 171 Publication C509423fComprehensive guide for creating publication-quality visualizations from Neuropixels data.Votes: 0GitHub stars: 9
- 172 Sql C37b970d```sql -- Recommended: 25% of system RAM (up to 40% for dedicated DB server) -- For 16GB RAM server: ALTER SYSTEM SET shared_buffers = '4GB';Votes: 0GitHub stars: 9
- 1724 Cross References 202cb6feQuarto provides a unified cross-reference system for figures, tables, equations, sections, theorems, and more.Votes: 0GitHub stars: 9
- 173 Preprocessing 99ecb6c5Comprehensive preprocessing techniques for Neuropixels neural recordings.Votes: 0GitHub stars: 9
- 1732 Memory Integration Analysis Ebd781ce> **HISTORICAL DOCUMENT**: This analysis from 2026-01-30 proposed a hybrid push/pull memory architecture with briefing.md, episodes, and skill profiles. The actual implementation (Memory System v3, 2026-01-31) took a different approach: append-only event store with auto-capture from checkpoints, 4-signal scoring, and concept entity matching. See [docs/index.md](index.md#memory-system-v3) for the current implementation. > `/heavy` analysis — 7 Opus agents across 3 rounds (2026-01-30)Votes: 0GitHub stars: 9
- 1733 Grading Rubric 592f746cDetailed scoring criteria for evaluating web UI design quality from a world-class designer perspective.Votes: 0GitHub stars: 9
- 174 Deploying 14495e71Comprehensive guide to deploying computer vision models in production environments.Votes: 0GitHub stars: 9
- 175 Project 451337ed**Project:** Claude Skills Intelligent Textbook **Date Started:** 2025-11-08 **Initial Token Count:** 29,660 tokens usedVotes: 0GitHub stars: 9
- 176 Torchdrug 09988e58TorchDrug provides extensive support for protein-related tasks including sequence analysis, structure prediction, property prediction, and protein-protein interactions. Proteins are represented as graphs where nodes are amino acid residues and edges represent spatial or sequential relationships.Votes: 0GitHub stars: 9
- 177 Reference D1c26264This reference covers file formats specific to proteomics, metabolomics, lipidomics, and related omics workflows.Votes: 0GitHub stars: 9
- 1773 Api Reference 496ca6d2Base class for defining structured I/O schemas. Inherits from Pydantic BaseModel.Votes: 0GitHub stars: 9
- 1773 Api Reference Fc785ae0This document provides detailed API reference for the MCP Metrics Collection Service.Votes: 0GitHub stars: 9
- 1774 Readme Cn 185ef924client = ov.SyncOpenViking(path="./data") try: client.initialize() add_result = client.add_resource( path="https://raw.githubusercontent.com/volcengine/OpenViking/refs/heads/main/README.md" ) root_uri = add_result['root_uri'] ls_result = client.ls(root_uri) print(f"Directory structure:\n{ls_result}\n") glob_result = client.glob(pattern="**/*.md", uri=root_uri) if glob_result['matches']: content = client.read(glob_result['matches'][0]) print(f"Content preview: {content[:200]}...\n") print("Wai...Votes: 0GitHub stars: 9
- 1774 Readme Cn 29975317client = ov.SyncOpenViking(path="./data") try: client.initialize() add_result = client.add_resource( path="https://raw.githubusercontent.com/volcengine/OpenViking/refs/heads/main/README.md" ) root_uri = add_result['root_uri'] ls_result = client.ls(root_uri) print(f"Directory structure:\n{ls_result}\n") glob_result = client.glob(pattern="**/*.md", uri=root_uri) if glob_result['matches']: content = client.read(glob_result['matches'][0]) print(f"Content preview: {content[:200]}...\n") print("Wai...Votes: 0GitHub stars: 9
- 1774 Readme Cn 2a39c48dclient = ov.SyncOpenViking(path="./data") try: client.initialize() add_result = client.add_resource( path="https://raw.githubusercontent.com/volcengine/OpenViking/refs/heads/main/README.md" ) root_uri = add_result['root_uri'] ls_result = client.ls(root_uri) print(f"Directory structure:\n{ls_result}\n") glob_result = client.glob(pattern="**/*.md", uri=root_uri) if glob_result['matches']: content = client.read(glob_result['matches'][0]) print(f"Content preview: {content[:200]}...\n") print("Wai...Votes: 0GitHub stars: 9
- 1774 Readme Cn 3041db78ollama serve ``` ```json // Ollama { "vlm": { "provider": "litellm", "model": "ollama/llama3.1", "api_base": "http://localhost:11434" } } ``` 完整的模型支持，请参见 [LiteLLM 提供商文档](https://docs.litellm.ai/docs/providers)。 </details> 创建配置文件 `~/.openviking/ov.conf`，复制前请删除注释： ```json { "storage": { "workspace": "/home/your-name/openviking_workspace" }, "log": { "level": "INFO", "output": "stdout" // 日志输出："stdout" 或 "file" }, "embedding": { "dense": { "api_base" : "<api-endpoint>", // API ...Votes: 0GitHub stars: 9
- 1774 Readme Cn 37d93107client = ov.SyncOpenViking(path="./data") try: client.initialize() add_result = client.add_resource( path="https://raw.githubusercontent.com/volcengine/OpenViking/refs/heads/main/README.md" ) root_uri = add_result['root_uri'] ls_result = client.ls(root_uri) print(f"Directory structure:\n{ls_result}\n") glob_result = client.glob(pattern="**/*.md", uri=root_uri) if glob_result['matches']: content = client.read(glob_result['matches'][0]) print(f"Content preview: {content[:200]}...\n") print("Wai...Votes: 0GitHub stars: 9
- 1774 Readme Cn 61502b0dclient = ov.SyncOpenViking(path="./data") try: client.initialize() add_result = client.add_resource( path="https://raw.githubusercontent.com/volcengine/OpenViking/refs/heads/main/README.md" ) root_uri = add_result['root_uri'] ls_result = client.ls(root_uri) print(f"Directory structure:\n{ls_result}\n") glob_result = client.glob(pattern="**/*.md", uri=root_uri) if glob_result['matches']: content = client.read(glob_result['matches'][0]) print(f"Content preview: {content[:200]}...\n") print("Wai...Votes: 0GitHub stars: 9
- 1774 Readme Cn 817096d4client = ov.SyncOpenViking(path="./data") try: client.initialize() add_result = client.add_resource( path="https://raw.githubusercontent.com/volcengine/OpenViking/refs/heads/main/README.md" ) root_uri = add_result['root_uri'] ls_result = client.ls(root_uri) print(f"Directory structure:\n{ls_result}\n") glob_result = client.glob(pattern="**/*.md", uri=root_uri) if glob_result['matches']: content = client.read(glob_result['matches'][0]) print(f"Content preview: {content[:200]}...\n") print("Wai...Votes: 0GitHub stars: 9
- 1774 Readme Cn 921bf3cfclient = ov.SyncOpenViking(path="./data") try: client.initialize() add_result = client.add_resource( path="https://raw.githubusercontent.com/volcengine/OpenViking/refs/heads/main/README.md" ) root_uri = add_result['root_uri'] ls_result = client.ls(root_uri) print(f"Directory structure:\n{ls_result}\n") glob_result = client.glob(pattern="**/*.md", uri=root_uri) if glob_result['matches']: content = client.read(glob_result['matches'][0]) print(f"Content preview: {content[:200]}...\n") print("Wai...Votes: 0GitHub stars: 9
- 1774 Readme Cn Aa76074bclient = ov.SyncOpenViking(path="./data") try: client.initialize() add_result = client.add_resource( path="https://raw.githubusercontent.com/volcengine/OpenViking/refs/heads/main/README.md" ) root_uri = add_result['root_uri'] ls_result = client.ls(root_uri) print(f"Directory structure:\n{ls_result}\n") glob_result = client.glob(pattern="**/*.md", uri=root_uri) if glob_result['matches']: content = client.read(glob_result['matches'][0]) print(f"Content preview: {content[:200]}...\n") print("Wai...Votes: 0GitHub stars: 9
- 178 Scientific C8430dfeScientific figures must be clear, accurate, and accessible. Publication-ready figures follow these fundamental principles:Votes: 0GitHub stars: 9
- 179 Calling 1915d5311. [Calling Python from MATLAB](#calling-python-from-matlab) 2. [Data Type Conversion](#data-type-conversion) 3. [Working with Python Objects](#working-with-python-objects) 4. [Calling MATLAB from Python](#calling-matlab-from-python) 5. [Common Workflows](#common-workflows)Votes: 0GitHub stars: 9
- 180 Total 825b8b4b- **Total Concepts**: 200 - **Foundational Concepts** (no dependencies): 8 - **Concepts with Dependencies**: 192 - **Average Dependencies per Concept**: 1.18Votes: 0GitHub stars: 9
- 181 Bash 879ce942```bash bamCoverage --bam input.bam --outFileName output.bw \ --normalizeUsing RPGC --effectiveGenomeSize 2913022398 \ --binSize 10 --numberOfProcessors 8 ```Votes: 0GitHub stars: 9
- 182 Test 07252468Test your understanding of FAQ generation, quiz creation, Bloom's Taxonomy in assessments, command-line interfaces, and taxonomy analysis scripts with these questions. --- <div class="upper-alpha" markdown> 1. To provide supplementary reference material like traditional appendices 2. To anticipate and address predictable student confusion patterns before they arise 3. To list all possible questions students might encounter in the course 4. To replace primary instruction with question-answer p...Votes: 0GitHub stars: 9
- 182 Test 0a0ab84eTest your understanding of learning graphs, concept nodes, dependency edges, and Claude usage optimization with these questions. --- <div class="upper-alpha" markdown> 1. To create visual diagrams for textbook covers 2. To map conceptual landscape with prerequisite dependencies explicitly 3. To track student progress through course material 4. To organize bibliography references by topic </div> ??? question "Show Answer" The correct answer is **B**. A learning graph is a directed graph data s...Votes: 0GitHub stars: 9
- 182 Test 8e020571Test your understanding of DAG validation, quality metrics, circular dependency detection, and taxonomy distribution analysis with these questions. --- <div class="upper-alpha" markdown> 1. O(V) where V is the number of vertices 2. O(V + E) where V is vertices and E is edges 3. O(V²) for all possible vertex pairs 4. O(E log E) where E is the number of edges </div> ??? question "Show Answer" The correct answer is **B**. The depth-first search (DFS) algorithm with cycle detection runs in O(V + ...Votes: 0GitHub stars: 9
- 182 Test E32ad987Test your understanding of taxonomy categorization, vis-network JSON format, Dublin Core metadata, and Python processing scripts with these questions. --- <div class="upper-alpha" markdown> 1. 1-2 letters for brevity 2. 3-5 letters for balance 3. 6-10 letters for clarity 4. 15+ letters for full descriptiveness </div> ??? question "Show Answer" The correct answer is **B**. TaxonomyID abbreviations should be 3-5 letters, balancing compactness in CSV files and visualizations with sufficient dist...Votes: 0GitHub stars: 9
- 182 Test F2148edbTest your understanding of concept enumeration, dependency mapping, CSV file formats, and taxonomy categorization with these questions. --- <div class="upper-alpha" markdown> 1. 50-75 concepts 2. 100-150 concepts 3. 180-220 concepts 4. 300-400 concepts </div> ??? question "Show Answer" The correct answer is **C**. A semester-length course typically targets approximately 200 concepts (range 180-220), which aligns with cognitive load principles, provides adequate assessment coverage, and fits s...Votes: 0GitHub stars: 9
- 1820 Final Status E4928737**Date:** 2026-02-08 **Branch:** feature/universal-infrastructure-strategy **Status:** ✅ READY FOR PRODUCTION ---Votes: 0GitHub stars: 9
- 1821 Phase4 Completion Summary 17001396**Date:** 2026-02-08 **Branch:** feature/universal-infrastructure-strategy **Status:** ✅ COMPLETED ---Votes: 0GitHub stars: 9
- 1822 Final Qa Verification 811190cb**Date:** February 7, 2026 **Branch:** `feature/universal-infrastructure-strategy` **Status:** ✅ **PRODUCTION READY** ---Votes: 0GitHub stars: 9
- 1823 Action Plan Cbd5217b**Start Date:** February 2, 2026 **Timeline:** 4 weeks **Strategy:** Hybrid approach combining RAG ecosystem + AI coding tools **Status:** ✅ Ready to Execute ---Votes: 0GitHub stars: 9
- 1823 Action Plan Da7e2f31**Start Date:** February 2, 2026 **Timeline:** 4 weeks **Strategy:** Hybrid approach combining RAG ecosystem + AI coding tools **Status:** ✅ Ready to Execute ---Votes: 0GitHub stars: 9
- 183 Generated D12ab459Generated: 2025-11-08Votes: 0GitHub stars: 9
- 1835 Learning Graph Viewer B3ef66a1This skill installs an interactive learning graph viewer application into an intelligent textbook project. Use this skill when working with a textbook that has a learning-graph.json file and needs a visual, interactive graph exploration tool with search, filtering, and statistics capabilities.Votes: 0GitHub stars: 9
- 184 Skill 824bd76bThis skill generates interactive multiple-choice quizzes for each chapter of an intelligent textbook, with questions aligned to specific concepts from the learning graph and distributed across Bloom's Taxonomy cognitive levels to assess student understanding effectively.Votes: 0GitHub stars: 9
- 1848 Context 4f65886eRuns create_changes.py script to extract commit information and categorize changesVotes: 0GitHub stars: 9
- 1849 Readme Zh 83c20fc4llm = OpenAICompatible.load_from_json_file("provider.json")["your_provider"]["model"] @llm_function(llm_interface=llm) async def classify_sentiment(text: str) -> str: """ 分析文本的情感倾向。 Args: text: 要分析的文本 Returns: 情感分类，可为 'positive', 'negative', 或 'neutral' """ pass # Prompt as Code! async def main(): result = await classify_sentiment("这个产品太棒了！") print(f"情感分类: {result}") asyncio.run(main()) ```Votes: 0GitHub stars: 9
- 185 Reference De620390This reference provides guidance on adjusting textbook content for different reading levels based on grade level or educational context.Votes: 0GitHub stars: 9
- 1850 Tool Cf3974f2SimpleLLMFunc 的工具系统为大语言模型提供了调用外部函数和 API 的能力，让 LLM 能够执行计算、查询数据、调用服务等操作。工具系统支持两种创建方式，并能自动将 Python 函数转换为 LLM 可理解的工具描述格式。Votes: 0GitHub stars: 9
- 186 Readme Ffc0056cThe readme-generator skill creates or updates comprehensive README.md files for GitHub repositories following best practices. It generates all essential sections including badges, project overview, site metrics, and getting started instructions.Votes: 0GitHub stars: 9
- 187 Reference 5cb2c3dc> Reference for: Common Ground > Load when: Using --graph flag, generating mermaid diagrams ---Votes: 0GitHub stars: 9
- 188 Quick 55ed2581Quick reference for using the `scientific_report.sty` style package.Votes: 0GitHub stars: 9
- 189 User C8554bbbIf a user tells you there is a problem with the project, review the `target/run_results.json` file to identify which resources failed and why.Votes: 0GitHub stars: 9
- 190 Php 108ebc75```php // routes/web.php use App\Http\Controllers\PostController; use Illuminate\Support\Facades\Route;Votes: 0GitHub stars: 9