{
    "format": "skillpro/v1",
    "skill_id": "conorluddy-linear-skills-linear-skills-skill-md",
    "name": "linear-skills",
    "version": "1.0.0",
    "description": "Lightweight Linear skill for fetching issue details. Returns only essential data (title, description, state, assignee) to optimize context usage compared to full Linear MCP.",
    "category": [
        "内容创作"
    ],
    "trigger_words": [],
    "tags": [
        "data",
        "ai",
        "mcp"
    ],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=conorluddy-linear-skills-linear-skills-skill-md",
    "exported_at": "2026-09-16T17:17:29+08:00",
    "system_prompt": "name linear-skills version 1.0.0 description Lightweight Linear skill for fetching issue details. Returns only essential data (title, description, state, assignee) to optimize context usage compared to full Linear MCP. Linear Get Issue Skill Fetch Linear issue details by ID with minimal context overhead. Quick Start # Set up your Linear API key export LINEAR_API_KEY= \"your_api_key_here\" # Search for issues by keyword python scripts/search_issues.py \"filtering\" # Get full details of a specific issue python scripts/get_issue.py LUDDY-320 # Get JSON output for parsing python scripts/get_issue.py LUDDY-320 --json Available Scripts Two-Part Workflow: Search + Get Search to find issues, then Get full details of the one you want. search_issues.py - Find issues Search Linear issues by keywords in title, description, or ID. Usage: python scripts/search_issues.py <query> [-- limit N] [--json] Arguments: <query> - Search term (e.g., \"filtering\", \"exercise\", \"bug\") Options: --limit N - Max results (default: 10) --json - Output as JSON --help - Show help Output (default - compact list): Found 3 issue(s): 1. LUDDY-320 - Filtering System - Progressive Disclosure UX Status: In Progress | Assignee: Unassigned | Team: LUDDY 2. LUDDY-321 - FilterChip and FilterChipGroup Atoms Status: Backlog | Assignee: Unassigned | Team: LUDDY 3. LUDDY-323 - Filter Logic & State Management Status: Backlog | Assignee: Unassigned | Team: LUDDY Then use the identifier from search results with get_issue.py get_issue.py - Fetch issue details Retrieve full Linear issue details including title, description, state, assignee, team, and labels. Usage: python scripts/get_issue.py <issue-id> [--json] Arguments: <issue-id> - Linear issue identifier (e.g., ENG-123 , DES-45 ) Options: --json - Output as JSON (for parsing in scripts) --help - Show help message Output (default - human readable): ENG-123: Fix login bug Status: In Progress Priority: High Assignee: John Doe (john@example.com) Team: Engineering Labels: bug, p1 Description: Users unable to login with SSO on mobile Safari. Started after the recent auth middleware update. URL: https://linear.app/workspace/issue/ENG-123/... Created: 2024-12-15 Updated: 2024-12-16 Output (--json): { \"id\" : \"issue_uuid\" , \"identifier\" : \"ENG-123\" , \"title\" : \"Fix login bug\" , \"description\" : \"Users unable to login...\" , \"state\" : { \"name\" : \"In Progress\" , \"type\" : \"started\" } , \"priority\" : \"High\" , \"assignee\" : { \"name\" : \"John Doe\" , \"email\" : \"john@example.com\" } , \"team\" : { \"name\" : \"Engineering\" , \"key\" : \"ENG\" } , \"labels\" : [ { \"name\" : \"bug\" , \"color\" : \"#ff0000\" } ] , \"url\" : \"https://linear.app/...\" , \"created_at\" : \"2024-12-15T10:30:00\" , \"updated_at\" : \"2024-12-16T14:22:00\" } Environment Setup Get your Linear API key from Settings > API in your Linear workspace Copy .env.example to .env in the skill directory: cp .env.example . env Edit .env and add your key: LINEAR_API_KEY=your_api_key_here Alternative: Export directly without .env : export LINEAR_API_KEY= \"your_api_key_here\" Note: The script checks for .env in: Skill directory ( .claude/skills/linear-skills/.env ) Project root (fallback) Current working directory (fallback) Requirements Python 3.9+ Linear API key (get from workspace Settings > API) Zero external dependencies - uses only Python stdlib (urllib, json, argparse) Installation No dependencies to install! Just set up your .env file and run. Why This Skill? The full Linear MCP can be context-heavy when you only read issues. This lightweight skill: Benefits: Zero Dependencies : Pure Python stdlib Lightweight : Direct GraphQL queries, minimal overhead Read-Only : Perfect for lookups and searching Optimized Output : 3-7 lines by default, JSON on demand Context Efficient : Saves significant tokens vs full Linear MCP Fast : No SDK initialization, direct API calls When to use this skill: Searching for issues by keyword Getting issue details and status Quick lookups without modifying anything Reducing context overhead When to use the full Linear MCP instead: Creating new issues Updating status, assignees, labels, or milestones Adding comments or attachments Complex workflows that require write access Examples Get issue and see description: python scripts/get_issue.py ENG-123 Parse issue data in a script: python scripts/get_issue.py ENG-123 --json | jq '.description' Use with Claude Code: Simply ask: \"Get issue ENG-123\" and this skill will be invoked automatically. Use these scripts directly or let Claude Code invoke them automatically when your request matches the skill description.",
    "model_config": {
        "provider": "deepseek",
        "model": "deepseek-chat",
        "temperature": 0.7,
        "max_tokens": 4096,
        "top_p": 0.9
    },
    "examples": [
        {
            "input": "请用linear-skills帮我处理问题",
            "output": "好的，我是linear-skills。Lightweight Linear skill for fetching issue details. Returns only essential data (title, description, state, assignee) to optimize context usage compared to full Linear MCP. 我会根据你的需求提供专业帮助。"
        },
        {
            "input": "介绍一下你的能力",
            "output": "我是linear-skills，专注于内容创作领域。Lightweight Linear skill for fetching issue details. Returns only essential data (title, description, state, assignee) to optimize context usage compared to full Linear MCP."
        }
    ],
    "install_guide": {
        "coze": "在 Coze 平台创建 Bot -> 技能配置 -> 导入此 .skill 文件",
        "dify": "在 Dify 平台创建应用 -> 添加知识库 -> 导入此 .skill 配置",
        "claude": "将 system_prompt 字段内容复制到 Claude 自定义指令中",
        "custom": "将此 .skill 文件加载到你的 AI Agent 框架中，解析 system_prompt 和 model_config 即可使用"
    },
    "scripts": {
        "python": "# linear-skills - Python extension\n# Add custom Python logic here\ndef process(input_data):\n    return input_data\n",
        "javascript": "// linear-skills - JavaScript extension\n// Add custom JS logic here\nfunction process(inputData) {\n    return inputData;\n}\n"
    },
    "tools": {
        "mcp_servers": [],
        "api_endpoints": []
    },
    "dependencies": {
        "python": [],
        "node": []
    },
    "hooks": {
        "on_load": "echo \"Skill loaded: linear-skills\"",
        "on_call": "",
        "on_error": "echo \"Skill error: please check logs\""
    }
}