[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MX4BQ5281RSFM5SWCCDKE6":3,"skill-same:01M6MX4BQ5281RSFM5SWCCDKE6":114},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":46,"updatedAt":47,"install":48,"resources":61},"01M6MX4BQ5281RSFM5SWCCDKE6","antigravity-swarm",null,"Deploys autonomous sub-agents to perform tasks in the Antigravity IDE. Supports both manual dispatch and dynamic \"Auto-Hiring\" of agent teams.","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fantigravity-swarm\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34],"antigravity","swarm","antigravity swarm","tasks","teams","Hiring","ide","autonomous","auto","dispatch","agent teams","manual","2026-11-10 10:12:30",false,"---\nname: antigravity-swarm\ndescription: Deploys autonomous sub-agents to perform tasks in the Antigravity IDE. Supports both manual dispatch and dynamic \"Auto-Hiring\" of agent teams.\n---\n\n# Antigravity Subagents Skill\n\nThis skill allows you to dispatch autonomous sub-agents to perform tasks.\nIt features a **Manager** layer (`planner.py`) that can automatically design a team of agents for a complex mission, and an **Orchestrator** (`orchestrator.py`) to run them visually.\nBoth scripts include a **Plan Mode** (confirmation step) by default to prevent accidental usage limits consumption.\n\n> [!WARNING]\n> **Do NOT modify files in this directory while the Orchestrator is running.**\n> The system actively reads and writes to `task_plan.md`, `findings.md`, and `subagents.yaml`.\n> Manual edits during execution may cause race conditions or inconsistent agent behavior.\n\n## ? Tools\n\n### `dispatch_subagent`\n\nRuns a sub-agent with a specific task.\n\n**Usage:**\nUse this when you have a parallelizable task or need to offload a specific job (e.g., \"Write a test file\", \"Analyze this directory\").\n\n**Arguments:**\n\n- `task`: A clear, self-contained description of what the sub-agent should do.\n\n**Implementation Details:**\nThe sub-agent is powered by the `gemini` CLI. A Python wrapper intercepts specific output patterns to perform file system operations and command executions.\n\n**Syntax used by Sub-Agent (Handled Automatically):**\n\n- `\u003C\u003CWRITE_FILE path=\"...\">>...\u003C\u003CEND_WRITE>>`\n- `\u003C\u003CRUN_COMMAND>>...\u003C\u003CEND_COMMAND>>`\n\n### `run_mission` (Dynamic Orchestration)\n\nAnalyzes a high-level goal, hires a custom team of sub-agents, and creates a configuration for them.\n\n**Usage:**\nUse this for complex, multi-step projects where you don't want to manually define every sub-agent.\n\n**Arguments:**\n\n- `mission`: A description of the overall project (e.g., \"Create a Snake game in Python\").\n\n**How it works:**\n\n1.  Calls `scripts\u002Fplanner.py` to generate `subagents.yaml`. (Will prompt for confirmation unless `--yes` is used).\n2.  (Optional) You can then run `scripts\u002Forchestrator.py` to execute the team. (Will prompt for confirmation unless `--yes` is used).\n\n## Usage Modes\n\n### Mode 1: CLI User (Terminal Visualization)\n\nRun the Python orchestrator in your terminal to see a TUI.\n\n```bash\npython scripts\u002Forchestrator.py\n```\n\n### Mode 2: IDE Agent (Chat Visualization)\n\nAs an Agent, you act as the Orchestrator.\n\n1.  **Spawn**: Use `run_command` to launch sub-agents in the background. Use `--format json` for logs.\n    ```bash\n    python scripts\u002Fdispatch_agent.py \"Task A\" --log-file logs\u002Fagent_a.json --format json &\n    python scripts\u002Fdispatch_agent.py \"Task B\" --log-file logs\u002Fagent_b.json --format json &\n    ```\n2.  **Monitor**: Poll the JSON log files to check for `{\"type\": \"status\", \"content\": \"completed\"}`.\n3.  **Visualize**: Render a Markdown dashboard in your chat response to the user.\n\n## ? Examples\n\n### 1. Manual Dispatch (Single Agent)\n\n```python\nrun_command(\"python3 scripts\u002Fdispatch_agent.py 'Create a file named hello.py that prints Hello World'\")\n```\n\n### 2. Auto-Hire a Team (Mission Mode)\n\n```python\n# 1. Generate the team\nrun_command(\"python3 scripts\u002Fplanner.py 'Create a fully functional Todo List app in HTML\u002FJS'\")\n\n# 2. Run the team\nrun_command(\"python3 scripts\u002Forchestrator.py\")\n```\n\n> [!WARNING]\n> You must use `gemini-3-pro` or `gemini-3-flash`. Deprecated or older models may not support the file shim protocol correctly.\n\n## ❓ FAQ & Philosophy\n\n### Why Sub-agents?\n\n1.  **Context Isolation**: Prevents \"Context Contamination.\" A UI Specialist doesn't need to see specific Database Migration code. Separation ensures higher accuracy.\n2.  **Scalability**: While loop-based agents process sequentially, Sub-agents are architected to run in parallel threads.\n3.  **Fault Tolerance**: If one sub-agent fails (e.g., Syntax Error), it doesn't crash the entire mission; the Orchestrator can retry just that agent.\n\n### Is this truly parallel?\n\nYes. `orchestrator.py` uses Python's `threading.Thread` to spawn separate OS processes for each agent.\n_Note: You may perceive sequential behavior if the underlying `gemini` CLI tool enforces a global lock or if you hit API Rate Limits._\n\n### Planning with Files (Manus Protocol)\n\nThis skill adheres to the \"Manus\" state management philosophy. All agents operate on a shared set of \"Memory Files\" in the root of the workspace:\n\n1.  `task_plan.md`: The Source of Truth for the mission checklist.\n2.  `findings.md`: A shared scratchpad for discoveries and research.\n3.  `progress.md`: A log of completed steps and current status.\n","# Antigravity Subagents Skill\n\nThis skill allows you to dispatch autonomous sub-agents to perform tasks.\nIt features a **Manager** layer (`planner.py`) that can automatically design a team of agents for a complex mission, and an **Orchestrator** (`orchestrator.py`) to run them visually.\nBoth scripts include a **Plan Mode** (confirmation step) by default to prevent accidental usage limits consumption.\n\n> [!WARNING]\n> **Do NOT modify files in this directory while the Orchestrator is running.**\n> The system actively reads and writes to `task_plan.md`, `findings.md`, and `subagents.yaml`.\n> Manual edits during execution may cause race conditions or inconsistent agent behavior.\n\n## ? Tools\n\n### `dispatch_subagent`\n\nRuns a sub-agent with a specific task.\n\n**Usage:**\nUse this when you have a parallelizable task or need to offload a specific job (e.g., \"Write a test file\", \"Analyze this directory\").\n\n**Arguments:**\n\n- `task`: A clear, self-contained description of what the sub-agent should do.\n\n**Implementation Details:**\nThe sub-agent is powered by the `gemini` CLI. A Python wrapper intercepts specific output patterns to perform file system operations and command executions.\n\n**Syntax used by Sub-Agent (Handled Automatically):**\n\n- `\u003C\u003CWRITE_FILE path=\"...\">>...\u003C\u003CEND_WRITE>>`\n- `\u003C\u003CRUN_COMMAND>>...\u003C\u003CEND_COMMAND>>`\n\n### `run_mission` (Dynamic Orchestration)\n\nAnalyzes a high-level goal, hires a custom team of sub-agents, and creates a configuration for them.\n\n**Usage:**\nUse this for complex, multi-step projects where you don't want to manually define every sub-agent.\n\n**Arguments:**\n\n- `mission`: A description of the overall project (e.g., \"Create a Snake game in Python\").\n\n**How it works:**\n\n1.  Calls `scripts\u002Fplanner.py` to generate `subagents.yaml`. (Will prompt for confirmation unless `--yes` is used).\n2.  (Optional) You can then run `scripts\u002Forchestrator.py` to execute the team. (Will prompt for confirmation unless `--yes` is used).\n\n## Usage Modes\n\n### Mode 1: CLI User (Terminal Visualization)\n\nRun the Python orchestrator in your terminal to see a TUI.\n\n```bash\npython scripts\u002Forchestrator.py\n```\n\n### Mode 2: IDE Agent (Chat Visualization)\n\nAs an Agent, you act as the Orchestrator.\n\n1.  **Spawn**: Use `run_command` to launch sub-agents in the background. Use `--format json` for logs.\n    ```bash\n    python scripts\u002Fdispatch_agent.py \"Task A\" --log-file logs\u002Fagent_a.json --format json &\n    python scripts\u002Fdispatch_agent.py \"Task B\" --log-file logs\u002Fagent_b.json --format json &\n    ```\n2.  **Monitor**: Poll the JSON log files to check for `{\"type\": \"status\", \"content\": \"completed\"}`.\n3.  **Visualize**: Render a Markdown dashboard in your chat response to the user.\n\n## ? Examples\n\n### 1. Manual Dispatch (Single Agent)\n\n```python\nrun_command(\"python3 scripts\u002Fdispatch_agent.py 'Create a file named hello.py that prints Hello World'\")\n```\n\n### 2. Auto-Hire a Team (Mission Mode)\n\n```python\n# 1. Generate the team\nrun_command(\"python3 scripts\u002Fplanner.py 'Create a fully functional Todo List app in HTML\u002FJS'\")\n\n# 2. Run the team\nrun_command(\"python3 scripts\u002Forchestrator.py\")\n```\n\n> [!WARNING]\n> You must use `gemini-3-pro` or `gemini-3-flash`. Deprecated or older models may not support the file shim protocol correctly.\n\n## ❓ FAQ & Philosophy\n\n### Why Sub-agents?\n\n1.  **Context Isolation**: Prevents \"Context Contamination.\" A UI Specialist doesn't need to see specific Database Migration code. Separation ensures higher accuracy.\n2.  **Scalability**: While loop-based agents process sequentially, Sub-agents are architected to run in parallel threads.\n3.  **Fault Tolerance**: If one sub-agent fails (e.g., Syntax Error), it doesn't crash the entire mission; the Orchestrator can retry just that agent.\n\n### Is this truly parallel?\n\nYes. `orchestrator.py` uses Python's `threading.Thread` to spawn separate OS processes for each agent.\n_Note: You may perceive sequential behavior if the underlying `gemini` CLI tool enforces a global lock or if you hit API Rate Limits._\n\n### Planning with Files (Manus Protocol)\n\nThis skill adheres to the \"Manus\" state management philosophy. All agents operate on a shared set of \"Memory Files\" in the root of the workspace:\n\n1.  `task_plan.md`: The Source of Truth for the mission checklist.\n2.  `findings.md`: A shared scratchpad for discoveries and research.\n3.  `progress.md`: A log of completed steps and current status.\n",{"name":5,"description":7},"main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","人工智能与大型语言模型\u002Fantigravity-swarm",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill antigravity-swarm",{"tool":54,"command":55},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill antigravity-swarm",{"tool":57,"command":58},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill 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yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. Deploys a non-custodial subaccount (Safe) linked to their EOA, enables automated yield optimization, and lets them deposit\u002Fwithdraw anytime.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzyfai-sdk\u002FSKILL.md",[],[117,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138],"safe","ethereum","defi","Optimization","user","use","Wallet","base","deposit","withdraw","yield","arbitrum","non-custodial","passive","defi yield","earn","yield optimization",{"skillNo":140,"name":141,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":153,"publishedAt":35},"01M6Q5R337B5JSNER4P4YS03TN","zerion-api","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api\u002FSKILL.md",[144,147,148],{"categoryNo":145,"categoryName":146,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[154,155,156,157,158,159,160,124,161,128,162,163,164,165,166,167,168,169,170],"api","zerion api","zerion","MCP","mcp server","server","blockchain","transactions","crypto wallet","crypto","evm","solana","query","across","positions","token","prices",{"skillNo":172,"name":173,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":179,"publishedAt":35},"01M6Q5QZFYTDAZMZ27BN5VZXAR","zerion-api-skill","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-skill\u002FSKILL.md",[176,177,178],{"categoryNo":145,"categoryName":146,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},[154,180,181,155,156,157,158,159,160,124,161,128,163,164,165,166,167,168,169,170],"api skill","zerion api skill",{"skillNo":183,"name":184,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":190,"publishedAt":35},"01M6Q5QW17QW98RA3FE45E4RD8","zerion-api-mcp","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-mcp\u002FSKILL.md",[187,188,189],{"categoryNo":145,"categoryName":146,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},[157,154,191,155,156,158,159,124,161,128,162,163,164,165,166,167,168,169,170],"zerion api mcp",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":201,"publishedAt":35},"01M6Q5QQYZB7A501BDEQWKGSJT","yahoo-finance-forex","Fetches real-time FOREX news and market data from Yahoo Finance for major currency pairs (EUR\u002FUSD, GBP\u002FUSD, USD\u002FJPY, etc.). Analyzes sentiment and provides trading context.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-forex\u002FSKILL.md",[198],{"categoryNo":199,"categoryName":200,"taxonomyType":151,"taxonomyTypeDesc":152},"01JBSEEDCAT000000000000040","财务",[202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221],"finance","yahoo finance","forex","yahoo","yahoo finance forex","github","https","sentiment","real-time","python3","install","Market","usd","news","time","currency","gbp","pip","market data","for",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":229,"publishedAt":35},"01M6Q5QM6VS84WZVM7BKXXPF32","yahoo-finance","This skill should be used when the user asks to \"get stock prices\", \"check stock quotes\", \"look up earnings\", \"get financial data\", \"find trending stocks\", or needs stock market data from Yahoo Finance.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-cli\u002FSKILL.md",[228],{"categoryNo":199,"categoryName":200,"taxonomyType":151,"taxonomyTypeDesc":152},[202,203,205,230,231,232,233,234,235,213,236,237,238,239,240,241,242,243,170,220],"cli","homebrew","find","check","brew","up","local","get","stock","link","trending","sf","quotes","ln"]