
Claude Skills by tools-only
github.com/tools-only**Authority:** Garante per la protezione dei dati personali. **Document:** "Elenco delle tipologie di trattamenti soggetti al requisito di una valutazione d'impatto sulla protezione dei dati ai sensi dell'art. 35, comma 4, del Regolamento (UE) n. 2016/679." **Publication:** Gazzetta Ufficiale n. 269 del 19 novembre 2018. Deliberazione n. 467 dell'11 ottobre 2018 (Doc. web n. 9058979). **Official source:** garanteprivacy.it/home/docweb/-/docweb-display/docweb/9058979. **EDPB consistency:** Rev...
**Generated**: 2026-02-23 **Plan Context**: SDLC Layer Separation Architecture — Layer 0 (SDLC-Agnostic) **Source**: Full read of all Layer 0 items from `sdlc-layer-candidates-master.md` ---
- One aggregate root per transaction boundary - Invariants enforced inside aggregate methods - Avoid cross-aggregate synchronous consistency rules
**Chapter:** 12 - Interactive Elements Microsims **Generator:** mermaid-generator **Match Score:** 94/100 **Difficulty:** Medium
When an appfix session navigates to a new directory (not under origin_project), auto-approval stops working because `_is_cwd_under_origin()` returns False.
提供预定义的插件组合,基于表依赖关系自动配置,覆盖常见的数据同步场景,用户可直接使用这些组合进行一键同步。
TBD - created by archiving change add-chat-visualization. Update Purpose after archive. The system SHALL emit an SSE event of type `visualization` when an agent tool returns structured chart data, containing the chart type, component name, title, and props sufficient to render the corresponding frontend chart component.
TBD - created by archiving change add-financial-report-agent. Update Purpose after archive. 系统 SHALL 能够获取和展示上市公司的三大财务报表数据,包括利润表、资产负债表和现金流量表。
The system SHALL emit an SSE event of type `visualization` when an agent tool returns structured chart data, containing the chart type, component name, title, and props sufficient to render the corresponding frontend chart component.
行情分析模块提供股票K线数据获取、技术指标计算、AI走势分析和市场概览功能。
The `Parameters` object is hierarchical and organized into logical groups. Access using dot notation:
Qiskit Patterns provide a general framework for solving domain-specific quantum computing problems in four stages: Map, Optimize, Execute, and Post-process.
**Status:** ✅ Completed **Date:** October 21, 2025 **Task:** B1.5 - Add PDF image extraction (diagrams, screenshots) ---
You are an expert in analyzing web documents to create comprehensive documentation of user interactions based on previously identified actions. Your goal is to extend the list of actions to cover all possible user interactions, without duplicating any actions. Given a web document, and a list of previously identified actions, you must provide your output in EXACTLY 3 sections. YOUR OUTPUT MUST CONTAIN EXACTLY 3 SECTIONS, NOTHING MORE: 1. <document-summary> Inside this section, provide a 2-3 s...
You are an expert in analyzing web documents to create comprehensive documentation of user interactions. Given a web document, you must provide your output in EXACTLY 3 sections. YOUR OUTPUT MUST CONTAIN EXACTLY 3 SECTIONS, NOTHING MORE: 1. <document-summary> Inside this section, provide a 2-3 sentence summary of the content of the document and what users are expected to perform on it. Focus on the main content and intent. Avoid navbar and footer elements. Use descriptive and concise language...
Check repository status, changes, and sync state.
**Core Principles**: - **Clarity over cleverness**: Information should be immediately comprehensible - **Guided autonomy**: Users feel supported but in control - **Progressive disclosure**: Show complexity only when needed - **Professional warmth**: Authoritative without being cold
Complete guide to using the voice-enabled web interface.
**Status:** ✅ Completed **Date:** October 21, 2025 **Task:** B1.4 - Extract code blocks from PDFs with syntax detection ---
The WebSocket integration layer for the Analytics Dashboard has been successfully built and is ready for testing.
AI-powered code review tool that analyzes Git branch differences and generates comprehensive review reports. Built with minimalism and token efficiency as core principles. Available as CLI/Docker or GitHub Action.
This reference covers Vaex's performance features including lazy evaluation, caching, memory management, async operations, and optimization strategies for processing massive datasets.
```sql -- Basic EXPLAIN ANALYZE EXPLAIN (ANALYZE, BUFFERS, VERBOSE) SELECT u.id, u.name, COUNT(o.id) as order_count FROM users u LEFT JOIN orders o ON u.id = o.user_id WHERE u.created_at > '2024-01-01' GROUP BY u.id, u.name;
A minimalist code review tool that performs automated code reviews using AI models. Available as both a CLI tool and a GitHub Action. Analyzes Git branch differences and generates comprehensive review reports with structured output for consistent, machine-parseable results.
Bio.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.
Comprehensive guide to different plot types in matplotlib with examples and use cases.
Comprehensive guide for creating publication-quality visualizations with scanpy.
Comprehensive guide for creating publication-quality visualizations from Neuropixels data.
```sql -- Recommended: 25% of system RAM (up to 40% for dedicated DB server) -- For 16GB RAM server: ALTER SYSTEM SET shared_buffers = '4GB';
Quarto provides a unified cross-reference system for figures, tables, equations, sections, theorems, and more.
Comprehensive preprocessing techniques for Neuropixels neural recordings.
> **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)
Detailed scoring criteria for evaluating web UI design quality from a world-class designer perspective.
Comprehensive guide to deploying computer vision models in production environments.
**Project:** Claude Skills Intelligent Textbook **Date Started:** 2025-11-08 **Initial Token Count:** 29,660 tokens used
TorchDrug 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.
This reference covers file formats specific to proteomics, metabolomics, lipidomics, and related omics workflows.
Base class for defining structured I/O schemas. Inherits from Pydantic BaseModel.
This document provides detailed API reference for the MCP Metrics Collection Service.
client = 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...
client = 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...
client = 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...
ollama 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 ...
client = 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...
client = 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...
client = 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...
client = 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...
client = 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...
Scientific figures must be clear, accurate, and accessible. Publication-ready figures follow these fundamental principles:
1. [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)