用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/ranbot-ai/awesome-skills --skill daily命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Generate project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, c
Run protected AAS maintainer sweeps, PR merge batches, canonical sync, Core preview checks, and scripted releases. Use for repository maintenance, main alignment, CLI/MCP/Workbench changes, or release
Provision backend infra through Cohesivity (cohesivity.ai): Postgres, hosting, auth, storage, and AI model APIs over one HTTP API. Use when a .cohesivity file exists or a project needs a backend.
正在显示 SKILL.md
基于 SOC 职业分类
| name | daily |
| description | Documentation and capabilities reference for Daily |
| category | Document Processing |
| source | antigravity |
| tags | ["python","javascript","typescript","react","api","ai","agent","llm","workflow","design"] |
| url | https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/daily |
Pipecat enables agents to build production-ready voice and multimodal AI applications with real-time processing. Agents can orchestrate complex AI service pipelines that handle audio, video, and text simultaneously while maintaining ultra-low latency (500-800ms round-trip). The framework abstracts away the complexity of coordinating multiple AI services, network transports, and audio processing, allowing agents to focus on application logic.
Key capabilities include:
Agents can construct pipelines that connect frame processors in sequence to handle real-time data flow:
pipeline = Pipeline([
transport.input(), # Receives user audio
stt, # Speech-to-text conversion
context_aggregator.user(), # Collect user responses
llm, # Language model processing
tts, # Text-to-speech conversion
transport.output(), # Sends audio to user
context_aggregator.assistant(), # Collect assistant responses
])
Agents can create custom frame processors to handle specialized logic, work with parallel pipelines for conditional processing, and manage frame types (SystemFrames for immediate processing, DataFrames for ordered queuing).
Agents can integrate 15+ speech-to-text providers including OpenAI, Google Cloud, Deepgram, AssemblyAI, Azure, and Whisper. Services support:
Agents can choose from 30+ text-to-speech providers including OpenAI, Google Cloud, ElevenLabs, Cartesia, LMNT, and PlayHT. Features include:
Agents can integrate with 20+ LLM providers including OpenAI, Anthropic, Google Gemini, Groq, Perplexity, and open-source models via Ollama. Capabilities include:
Agents can enable LLMs to call external functions and APIs during conversations:
# Define functions using standard schema
weather_function = FunctionSchema(
name="get_current_weather",
description="Get the current weather in a location",
properties={"location": {"type": "string"}},
required=["location"]
)
# Register function handlers
async def fetch_weather(params: FunctionCallParams):
location = params.arguments.get("location")
weather_data = await weather_api.get_weather(location)
await params.result_callback(weather_data)
llm.register_function("get_current_weather", fetch_weather)
Function results are automatically stored in conversation context, enabling multi-step interactions and real-time data access.
Agents can manage conversation context automatically or manually:
LLMMessagesAppendFrame and LLMMessagesUpdateFrameAgents can configure sophisticated turn-taking strategies