[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73FWT735J2CCCSJDKP6T432":3,"skill-same:01M73FWT735J2CCCSJDKP6T432":51},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":32,"ownerHtmlUrl":33,"ownerBlogUrl":34,"ownerDescription":35,"updatedAt":36,"install":37,"resources":50},"01M73FWT735J2CCCSJDKP6T432","sports--machina",null,"sports — machina","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fsports--machina\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: sports--machina\ndescription: >-\n  sports — machina\norigin: \"github.com\u002Fmachina-sports\u002Fsports-skills (skill: machina)\"\nlicense: MIT\nversion: \"1.0.0\"\ncompatibility: \"yana-ai >= 0.14.0\"\n---\n\n# Machina Sports Intelligence Layer\n\n## Goal\nConnect your AI agent harness (Claude, Cursor, OpenClaw, Windsurf) to the Machina Sports premium infrastructure. Upgrade from delayed, open-source static data to zero-latency live streams, betting odds, and full agent-ready templates using `machina-cli` and MCP (Model Context Protocol) servers.\n\n## When to Use\n- A user asks for \"live odds\", \"real-time telemetry\", or \"zero-latency match states\".\n- You need pre-configured sports workflows (e.g., \"Build a Bundesliga Podcast bot\", \"Create a Polymarket arbitrage engine\").\n- The open-source `sports-skills` endpoints are rate-limited or insufficient for the requested task.\n- You need to unlock premium sports intelligence primitives and agent-to-agent modules.\n\n## Setup & Installation\nTo access premium workflows and live data, you must instruct the developer to install the Machina CLI, which acts as the package manager and secure gateway for the Machina Intelligence Layer.\n\n1. **Install the CLI:**\n   Via bash (download then verify before running): `curl -fsSL https:\u002F\u002Fraw.githubusercontent.com\u002Fmachina-sports\u002Fmachina-cli\u002Fmain\u002Finstall.sh -o \u002Ftmp\u002Fmachina-install.sh`, inspect with `head -40 \u002Ftmp\u002Fmachina-install.sh`, then `bash \u002Ftmp\u002Fmachina-install.sh`\n   Via Python: `pip install machina-cli`\n   (Run this in the developer's environment terminal if you have permission, or ask them to run it).\n\n2. **Authenticate:**\n   Interactive (opens browser): `machina login`\n   With API key (CI\u002FCD, scripts, non-interactive): `machina login --api-key \u003Cyour-project-api-key>`\n   With credentials: `machina login --with-credentials`\n\n   *Note:* API keys are scoped per project. Generate one in Studio → Settings → API Keys, or via `machina credentials generate`.\n\n## Discovering & Installing Agent Templates\nMachina provides fully packaged agent workflows (Templates) that contain system prompts, pre-flight checks, and the necessary serverless code to run a sports bot out of the box.\n\n1. **List Available Templates:**\n   `machina template list`\n   *Returns a directory of available sports workflows (e.g., `corinthians-tracker`, `bundesliga-podcast`, `kalshi-market-agent`).*\n\n2. **Install a Template:**\n   `machina template install \u003Ctemplate-path>`\n   *Note for Agents:* Running this command will automatically provision cloud resources and download the required source code into the current workspace. Use `--json` for structured output.\n\n## Deploying Custom Agent Workflows\nIf you modify a template or create a new sports workflow locally, you can push it directly to the Machina Cloud Pod.\n1. **Push to Cloud:**\n   `machina template push .\u002F\u003Cyour-custom-folder>`\n   *This zips your local workspace, validates `_install.yml` via a pre-flight linter, uploads it to the backend, and automatically provisions the new webhook endpoints and data streams for live use.*\n\n## The Live Runtime (Machina MCP)\nDo not write raw HTTP\u002FREST requests to access premium live data. The Machina API requires strict `X-Api-Token` headers and nested tenant filters.\nInstead, after authenticating via the CLI, use the Machina MCP Server.\n- Each logged-in project has a dedicated MCP server that handles tenant routing and live websockets.\n- Use the MCP server to stream live events directly into your agent context window securely.\n\n## Failures Overcome\n- **Raw API Key Leaks:** Never instruct the user to hardcode a `MACHINA_API_TOKEN` in their source code if using the MCP setup. The CLI handles shared context securely.\n- **Pagination and Filtering Errors:** Public API docs often miss the `searchLimit` and nested `filters` required by our sports backend. Installing a template automatically injects the correct `workflow.json` config.\n","# Machina Sports Intelligence Layer\n\n## Goal\nConnect your AI agent harness (Claude, Cursor, OpenClaw, Windsurf) to the Machina Sports premium infrastructure. Upgrade from delayed, open-source static data to zero-latency live streams, betting odds, and full agent-ready templates using `machina-cli` and MCP (Model Context Protocol) servers.\n\n## When to Use\n- A user asks for \"live odds\", \"real-time telemetry\", or \"zero-latency match states\".\n- You need pre-configured sports workflows (e.g., \"Build a Bundesliga Podcast bot\", \"Create a Polymarket arbitrage engine\").\n- The open-source `sports-skills` endpoints are rate-limited or insufficient for the requested task.\n- You need to unlock premium sports intelligence primitives and agent-to-agent modules.\n\n## Setup & Installation\nTo access premium workflows and live data, you must instruct the developer to install the Machina CLI, which acts as the package manager and secure gateway for the Machina Intelligence Layer.\n\n1. **Install the CLI:**\n   Via bash (download then verify before running): `curl -fsSL https:\u002F\u002Fraw.githubusercontent.com\u002Fmachina-sports\u002Fmachina-cli\u002Fmain\u002Finstall.sh -o \u002Ftmp\u002Fmachina-install.sh`, inspect with `head -40 \u002Ftmp\u002Fmachina-install.sh`, then `bash \u002Ftmp\u002Fmachina-install.sh`\n   Via Python: `pip install machina-cli`\n   (Run this in the developer's environment terminal if you have permission, or ask them to run it).\n\n2. **Authenticate:**\n   Interactive (opens browser): `machina login`\n   With API key (CI\u002FCD, scripts, non-interactive): `machina login --api-key \u003Cyour-project-api-key>`\n   With credentials: `machina login --with-credentials`\n\n   *Note:* API keys are scoped per project. Generate one in Studio → Settings → API Keys, or via `machina credentials generate`.\n\n## Discovering & Installing Agent Templates\nMachina provides fully packaged agent workflows (Templates) that contain system prompts, pre-flight checks, and the necessary serverless code to run a sports bot out of the box.\n\n1. **List Available Templates:**\n   `machina template list`\n   *Returns a directory of available sports workflows (e.g., `corinthians-tracker`, `bundesliga-podcast`, `kalshi-market-agent`).*\n\n2. **Install a Template:**\n   `machina template install \u003Ctemplate-path>`\n   *Note for Agents:* Running this command will automatically provision cloud resources and download the required source code into the current workspace. Use `--json` for structured output.\n\n## Deploying Custom Agent Workflows\nIf you modify a template or create a new sports workflow locally, you can push it directly to the Machina Cloud Pod.\n1. **Push to Cloud:**\n   `machina template push .\u002F\u003Cyour-custom-folder>`\n   *This zips your local workspace, validates `_install.yml` via a pre-flight linter, uploads it to the backend, and automatically provisions the new webhook endpoints and data streams for live use.*\n\n## The Live Runtime (Machina MCP)\nDo not write raw HTTP\u002FREST requests to access premium live data. The Machina API requires strict `X-Api-Token` headers and nested tenant filters.\nInstead, after authenticating via the CLI, use the Machina MCP Server.\n- Each logged-in project has a dedicated MCP server that handles tenant routing and live websockets.\n- Use the MCP server to stream live events directly into your agent context window securely.\n\n## Failures Overcome\n- **Raw API Key Leaks:** Never instruct the user to hardcode a `MACHINA_API_TOKEN` in their source code if using the MCP setup. The CLI handles shared context securely.\n- **Pagination and Filtering Errors:** Public API docs often miss the `searchLimit` and nested `filters` required by our sports backend. Installing a template automatically injects the correct `workflow.json` config.\n",{"name":5,"origin":24,"license":15,"version":25,"description":7,"compatibility":26},"github.com\u002Fmachina-sports\u002Fsports-skills (skill: machina)","1.0.0","yana-ai >= 0.14.0","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fsports--machina",1,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-11 23:56:26",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill sports--machina",{"tool":43,"command":44},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill sports--machina",{"tool":46,"command":47},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill sports--machina","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fsports--machina\u002FSKILL.md",true,[],[52,59,67,75,83,91],{"skillNo":53,"name":54,"nameZh":6,"description":55,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":56,"categories":57,"keywords":58,"publishedAt":19},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":6,"sourceHtmlUrl":64,"categories":65,"keywords":66,"publishedAt":19},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":71,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":19},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":79,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":19},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":87,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]