[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7ED77BC46P3T660SWWDTXWQ":3,"skill-same:01M7ED77BC46P3T660SWWDTXWQ":57},{"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":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":6,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":41,"updatedAt":42,"install":43,"resources":56},"01M7ED77BC46P3T660SWWDTXWQ","pipecat-friday-agent",null,"Build a low-latency, Iron Man-inspired tactical voice assistant (F.R.I.D.A.Y.) using Pipecat, Gemini, and OpenAI.","lza6\u002FArmor-piercing-system-prompts","lza6","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F128880206?v=4","01M7EB3K7WPX5T28EJ2NVHE6EG","Chinese-tingfeng","01M7EB3K97E1KQ16QWKJMFNGX8",11,"MIT","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F03-逆向工程\u002Fpipecat-friday-agent\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: pipecat-friday-agent\ndescription: \"Build a low-latency, Iron Man-inspired tactical voice assistant (F.R.I.D.A.Y.) using Pipecat, Gemini, and OpenAI.\"\ncategory: voice-agents\nrisk: safe\nsource: community\ndate_added: \"2026-03-10\"\ntags: [pipecat, voice, gemini, openai, python]\ntools: [pipecat]\n---\n\n# Pipecat Friday Agent\n\n## Overview\n\nThis skill provides a blueprint for building **F.R.I.D.A.Y.** (Replacement Integrated Digital Assistant Youth), a local voice assistant inspired by the tactical AI from the Iron Man films. It uses the **Pipecat** framework to orchestrate a low-latency pipeline:\n- **STT**: OpenAI Whisper (`whisper-1`) or `gpt-4o-transcribe`\n- **LLM**: Google Gemini 2.5 Flash (via a compatibility shim)\n- **TTS**: OpenAI TTS (`nova` voice)\n- **Transport**: Local Audio (Hardware Mic\u002FSpeakers)\n\n## When to Use This Skill\n\n- Use when you want to build a real-time, conversational voice agent.\n- Use when working with the Pipecat framework for pipeline-based AI.\n- Use when you need to integrate multiple providers (Google and OpenAI) into a single voice loop.\n- Use when building Iron Man-themed or tactical-themed voice applications.\n\n## How It Works\n\n### Step 1: Install Dependencies\n\nYou will need the Pipecat framework and its service providers installed:\n```bash\npip install pipecat-ai[openai,google,silero] python-dotenv\n```\n\n### Step 2: Configure Environment\n\nCreate a `.env` file with your API keys:\n```env\nOPENAI_API_KEY=your_openai_key\nGOOGLE_API_KEY=your_google_key\n```\n\n### Step 3: Run the Agent\n\nExecute the provided Python script to start the interface:\n```bash\npython scripts\u002Ffriday_agent.py\n```\n\n## Core Concepts\n\n### Pipeline Architecture\nThe agent follows a linear pipeline: `Mic -> VAD -> STT -> LLM -> TTS -> Speaker`. This allows for granular control over each stage, unlike end-to-end speech-to-speech models.\n\n### Google Compatibility Shim\nSince Google's Gemini API has a different message format than OpenAI's standard (which Pipecat aggregators expect), the script includes a `GoogleSafeContext` and `GoogleSafeMessage` class to bridge the gap.\n\n## Best Practices\n\n- ✅ **Use Silero VAD**: It is robust for local hardware and prevents background noise from triggering the LLM.\n- ✅ **Concise Prompts**: Tactical agents should give short, data-dense responses to minimize latency.\n- ✅ **Sample Rate Match**: OpenAI TTS outputs at 24kHz; ensure your `audio_out_sample_rate` matches to avoid high-pitched or slowed audio.\n- ❌ **No Polite Fillers**: Avoid \"Hello, how can I help you today?\" Instead, use \"Systems nominal. Ready for commands.\"\n\n## Troubleshooting\n\n- **Problem:** Audio is choppy or delayed.\n  - **Solution:** Check your `OUTPUT_DEVICE` index. Run a script like `test_audio_output.py` to find the correct hardware index for your OS.\n- **Problem:** \"Validation error\" for message format.\n  - **Solution:** Ensure the `GoogleSafeContext` shim is correctly translating OpenAI-style dicts to Gemini-style schema.\n\n## Related Skills\n\n- `@voice-agents` - General principles of voice AI.\n- `@agent-tool-builder` - Add tools (Search, Lights, etc.) to your Friday agent.\n- `@llm-architect` - Optimizing the LLM layer.\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.\n","# Pipecat Friday Agent\n\n## Overview\n\nThis skill provides a blueprint for building **F.R.I.D.A.Y.** (Replacement Integrated Digital Assistant Youth), a local voice assistant inspired by the tactical AI from the Iron Man films. It uses the **Pipecat** framework to orchestrate a low-latency pipeline:\n- **STT**: OpenAI Whisper (`whisper-1`) or `gpt-4o-transcribe`\n- **LLM**: Google Gemini 2.5 Flash (via a compatibility shim)\n- **TTS**: OpenAI TTS (`nova` voice)\n- **Transport**: Local Audio (Hardware Mic\u002FSpeakers)\n\n## When to Use This Skill\n\n- Use when you want to build a real-time, conversational voice agent.\n- Use when working with the Pipecat framework for pipeline-based AI.\n- Use when you need to integrate multiple providers (Google and OpenAI) into a single voice loop.\n- Use when building Iron Man-themed or tactical-themed voice applications.\n\n## How It Works\n\n### Step 1: Install Dependencies\n\nYou will need the Pipecat framework and its service providers installed:\n```bash\npip install pipecat-ai[openai,google,silero] python-dotenv\n```\n\n### Step 2: Configure Environment\n\nCreate a `.env` file with your API keys:\n```env\nOPENAI_API_KEY=your_openai_key\nGOOGLE_API_KEY=your_google_key\n```\n\n### Step 3: Run the Agent\n\nExecute the provided Python script to start the interface:\n```bash\npython scripts\u002Ffriday_agent.py\n```\n\n## Core Concepts\n\n### Pipeline Architecture\nThe agent follows a linear pipeline: `Mic -> VAD -> STT -> LLM -> TTS -> Speaker`. This allows for granular control over each stage, unlike end-to-end speech-to-speech models.\n\n### Google Compatibility Shim\nSince Google's Gemini API has a different message format than OpenAI's standard (which Pipecat aggregators expect), the script includes a `GoogleSafeContext` and `GoogleSafeMessage` class to bridge the gap.\n\n## Best Practices\n\n- ✅ **Use Silero VAD**: It is robust for local hardware and prevents background noise from triggering the LLM.\n- ✅ **Concise Prompts**: Tactical agents should give short, data-dense responses to minimize latency.\n- ✅ **Sample Rate Match**: OpenAI TTS outputs at 24kHz; ensure your `audio_out_sample_rate` matches to avoid high-pitched or slowed audio.\n- ❌ **No Polite Fillers**: Avoid \"Hello, how can I help you today?\" Instead, use \"Systems nominal. Ready for commands.\"\n\n## Troubleshooting\n\n- **Problem:** Audio is choppy or delayed.\n  - **Solution:** Check your `OUTPUT_DEVICE` index. Run a script like `test_audio_output.py` to find the correct hardware index for your OS.\n- **Problem:** \"Validation error\" for message format.\n  - **Solution:** Ensure the `GoogleSafeContext` shim is correctly translating OpenAI-style dicts to Gemini-style schema.\n\n## Related Skills\n\n- `@voice-agents` - General principles of voice AI.\n- `@agent-tool-builder` - Add tools (Search, Lights, etc.) to your Friday agent.\n- `@llm-architect` - Optimizing the LLM layer.\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.\n",{"name":5,"risk":24,"tags":25,"tools":31,"source":32,"category":33,"date_added":34,"description":7},"safe",[26,27,28,29,30],"pipecat","voice","gemini","openai","python",[26],"community","voice-agents","2026-03-10","main","d69096e239f2a89aabc2cd078c47664a9d3b1e11","03-逆向工程\u002Fpipecat-friday-agent",3,"https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts","https:\u002F\u002Fgithub.com\u002Flza6","个人vx：Tf00798\r\n个人✈：@TFGY999","2026-10-08 14:29:22",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add lza6\u002FArmor-piercing-system-prompts --skill pipecat-friday-agent",{"tool":49,"command":50},"bunx","bunx skills add lza6\u002FArmor-piercing-system-prompts --skill pipecat-friday-agent",{"tool":52,"command":53},"pnpm","pnpm dlx skills add lza6\u002FArmor-piercing-system-prompts --skill pipecat-friday-agent","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F03-逆向工程\u002Fpipecat-friday-agent\u002FSKILL.md",true,[],[58,65,72,79,87,94],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"publishedAt":19},"01M7EXRM12046G0GM1WGMMFXCN","ci-cd-and-automation","Automates CI\u002FCD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fci-cd-and-automation\u002FSKILL.md",[],[],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":19},"01M7EXRFYYSJ3Y15AGPDDEWWQD","chrome-devtools","Uses Chrome DevTools via MCP for efficient debugging, troubleshooting and browser automation. Use when debugging web pages, automating browser interactions, analyzing performance, or inspecting network requests. This skill does not apply to `--slim` mode (MCP configuration).","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fchrome-devtools\u002FSKILL.md",[],[],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":19},"01M7EXR6EYPE4X5X1BTAE6QJRV","chrome-cdp","Drive a real Chrome via the Chrome DevTools Protocol (CDP). Ensures a\ndebuggable Chrome is running (launching it if needed) so the agent can\nnavigate, read page content, inspect the DOM, run JavaScript, and take\nscreenshots — without the user typing any launch command. Includes a\nfirst-class \"review a live running app\" flow: point Chrome at the app under\ndevelopment and inspect\u002Fscreenshot the REAL page (what a static-artifact tool\ncannot do). Backed by Glimpse and the chrome-devtools MCP server. Use when\nasked to \"use chrome\", \"open chrome\", \"drive my browser\", \"read the page in\nchrome\", \"review the running app\", \"screenshot the app\", or \"\u002Fchrome-cdp\".","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fchrome-cdp\u002FSKILL.md",[],[],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":83,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":19},"01M7EXR1Y7E79KQKG4J798XGH6","check-identity-pack","Run an AFP 100-point or AUSTRAC safe-harbour identity check over a set of documents, and report exactly what's missing. Use when the user asks to check identity documents, verify someone's ID for onboarding, or assess whether a document pack satisfies Australian identity requirements.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fcheck-identity-pack\u002FSKILL.md",[],[],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":19},"01M7EXQXW7C7PGREJW9ZZTZ16H","changelog","Auto-generates a changelog from git commits, sprint data, and design documents. Produces both internal and player-facing versions.","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fchangelog\u002FSKILL.md",[],[],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":19},"01M7EXQMH2ZME3ANN8Q8MEWPFB","cellxgene-census","Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs\u002Fdownloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fcellxgene-census\u002FSKILL.md",[],[]]