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Found 167 Skills
Generates Bruno collection files (.bru) from Express, Next.js, Fastify, or other API routes. Creates organized collections with environments, authentication, and folder structure for the open-source Bruno API client. Use when users request "generate bruno collection", "bruno api testing", "create bru files", or "bruno import".
Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.
Points to the BlockchainSpider open-source Python/Scrapy toolkit for collecting on-chain data—transfer subgraphs around an address or tx, EVM and Solana block/transaction ingestion, receipts/logs, and optional label plugins. Use when the user wants to build datasets, offline traces, or research pipelines alongside blockchain-analytics-operations and solana-tracing-specialist—not as a substitute for RPC provider ToS, rate limits, or legal review of sensitive crawls.
Omi AI wearable platform help — open-source AI necklace for all-day conversation capture (in-person + online meetings), Developer API (`api.omi.me/v1/dev`, Bearer token, 100 req/min), app marketplace with webhook integrations, memories/conversations/action-items endpoints. Use when setting up an Omi wearable for meeting capture, building a custom Omi app or integration, troubleshooting Bluetooth disconnects or transcription accuracy, connecting Omi to Slack or CRM via webhooks, comparing Omi to Plaud or Limitless for in-person recording, or accessing Omi's API to export conversations and action items. Do NOT use for choosing between software-only note-takers without wearable needs (use /sales-note-taker).
dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters.
Transform projects into professional open-source repositories with standard components. Use when users ask to "make this open source", "add open source files", "setup OSS standards", "create contributing guide", "add license", or want to prepare a project for public release with README, CONTRIBUTING, LICENSE, and GitHub templates.
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill instead.
Expert skill for using Future AGI — the open-source end-to-end platform for evaluating, observing, and improving LLM and AI agent applications with tracing, evals, simulations, datasets, gateway, and guardrails.
This skill covers implementing Software Composition Analysis (SCA) using Snyk to detect vulnerable open-source dependencies in CI/CD pipelines. It addresses scanning package manifests and lockfiles, automated fix pull request generation, license compliance checking, continuous monitoring of deployed applications, and integration with GitHub, GitLab, and Jenkins pipelines.
Specialized agent for multi-repository analysis, searching remote codebases, retrieving official documentation, and finding implementation examples using GitHub CLI, Context7, and Web Search. Use proactively when unfamiliar libraries or frameworks are involved, working with external dependencies, or needing examples from open-source projects to understand best practices and real-world implementations.
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
Systematic codebase investigation to extract architectural patterns and implementation details from an existing project, with findings persisted for long-term reuse. Use when the user wants to explore an open-source or existing codebase to understand how it works and inform the development of a new project. Triggers include: "explore this codebase", "investigate this repo", "how does X implement Y", "I want to build X, study how Y does it", "deep dive into this project", "understand how this works".