Results for “raspberry-pi”

Install, configure, and extend Pi, a terminal coding harness, with support for custom providers, models, extensions, skills, packages, themes, SDK integration, RPC mode, JSON event streams, and ecosystem packages for subagent delegation, MCP servers, interactive forms, and web access.

Fix PyPI package name collisions when local package name exists on PyPI

Manages Python package publishing, versioning, and distribution builds on PyPI, including dependency resolution, index configuration, and package health analysis.

High-fidelity ASCII/Braille rendering via the Risomorphism-1911 pipeline — edge-aware downsampling, presets, quality gates, and eikon mirror workflows

Manage schemas, subjects, compatibility levels, modes, and contexts in the Redpanda Schema Registry via the rpk registry CLI. Register or fetch Avro, Protobuf, and JSON schemas, set compatibility levels, change registry mode, and work with schema contexts.

Install the rpk CLI on macOS, Linux, or Docker, and configure profiles, connection settings, SASL/TLS credentials, and Enterprise licenses for Redpanda clusters.

Reviews code using pi CLI with read-only tools. Delegates the review to pi (dev/pi) with a structured review rubric, running in read-only mode to prevent accidental edits. By default reviews uncommitted working tree changes (git diff HEAD) with pi restricted to the read tool; explicit targets (--branch/--diff/@file/PR) or --explore widen pi to read,grep,find,ls. Use when the user asks to "review code with pi", "pi review", "have pi review", "let pi review", or invokes /pi:review.

Set up and track KPI dashboards with targets, actuals, and trend analysis. TRIGGERS - Use when user wants to define KPIs, track metrics, or create a scorecard.

Guides the user through configuring pi — provider, model, base URL, and API key. Use when the user asks to "setup pi", "configure pi", "pi setup", "set up pi provider", "pi config", "change pi model", or invokes /pi:setup. Only run this skill when the user explicitly requests pi setup — never auto-invoke.

Delegates a coding task to pi (dev/pi), a minimal terminal coding harness. This skill should be used when the user asks to "use pi", "run pi", "delegate to pi", "let pi handle this", "ask pi to", "have pi do", or invokes /pi:delegate. It bridges the current Claude Code context to the pi CLI, passing relevant files, git state, and the task description for execution by the pi-agent.

Manage Vapi voice agents, calls, phone numbers, tools, and webhooks via REST API or CLI.

Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models following established patterns.

Deploy a cuOpt REST server and solve routing optimization problems using Python or curl.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Write, review, debug, and test Python FastAPI applications with path operations, dependencies, Pydantic models, lifespan, middleware, background tasks, exception handling, and ASGI tests.

Scaffold, test, document, and debug REST and GraphQL APIs from the command line, including curl testing, OpenAPI spec generation, mocking, and troubleshooting.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Integrate REST APIs in Python using requests, httpx, or official SDKs, handling authentication, rate limiting, pagination, and errors.

Manage Redpanda topics and produce or consume records from the CLI using the `rpk topic` command group. Use when creating or deleting topics with custom partitions, replication, or configs; altering topic configs or retention; adding partitions; trimming or deleting records; or using `rpk topic produce`/`consume` with.

Development with Mpi: tools and best practices

Use DeepAPI for supported scraping, research, and email workflows with explicit credentials and approval.

Provides production-grade FastAPI development patterns including project structure, Pydantic v2 schemas, dependency injection, async handlers, authentication, authorization, transactional service layers, and testing with httpx and pytest.

Scrapes public web data from LinkedIn, Twitter, GitHub, YouTube, and other sources, sends and reads email, performs deep research, generates images, and searches the web through the DeepAPI service.

Use DeepAPI for supported scraping, research, and email workflows with explicit credentials and approval.

Manage received (inbound) emails and attachments via Resend API. Use when user asks about their emails, received messages, or email attachments.

Operate Redpanda through its Kafka-compatible API: produce and consume records, manage topics and consumer groups, run transactions, and configure tiered storage using rpk and Kafka clients.

Provisions and manages Redpanda Cloud Serverless clusters via the public Control Plane API, including authentication, cluster lifecycle, and data-plane operations.

Run any model on RunComfy from the command line. The `runcomfy` CLI is one binary, one auth, hundreds of model endpoints — image generation, image edit, video generation, image-to-video, lip-sync, face swap, video edit, inpainting, outpainting, extend, ControlNet, relight, upscale, LoRA training and more. Submit a request, poll for status, download the output. This skill teaches the agent how to install, authenticate, discover model schemas, invoke models, stream / poll / no-wait, script in JSON output mode, and handle errors. Triggers on "runcomfy cli", "install runcomfy", "runcomfy login", "runcomfy run", "runcomfy whoami", "runcomfy api", or any explicit ask to call a RunComfy model from a script or terminal. Sibling skills (ai-image-generation, ai-video-generation, image-edit, video-edit, face-swap, lipsync, image-to-video, image-inpainting, image-outpainting, video-extend, controlnet-pose, relight) all dispatch through this CLI.

Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.

Run one bounded Plan, Implement, and fresh Validate experiment, then report and stop. Triggers: "run rpi", "feed this through the loop", "research-plan-implement".

DSPy: declarative LM programs, auto-optimize prompts, RAG.