2.4K skills · sorted by relevance
Use when working with Aws Fargate — aWS Fargate task and service analysis covering ECS cluster inventory, Fargate task status, CPU and memory utilization, task definition review, networking configuration, service auto-scaling policies, and container health checks. Use for Fargate-specific workload optimization.
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Use when working with Azure Functions Deep — deep Azure Functions analysis covering function app inventory, trigger types, execution metrics, scaling behavior, slot deployments, application insights integration, and consumption plan cost tracking. Provides optimization guidance for cold starts and memory usage.
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Use when working with Aws Rightsizing — analyze EC2, RDS, EBS, and Lambda resource utilization to identify right-sizing opportunities. Uses CloudWatch metrics with anti-hallucination rules for burstable instances, memory metrics, peak vs average analysis, and estimated monthly savings calculations.
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Use when working with Trino — trino (Presto) query analysis, catalog management, worker health, memory allocation, and query optimization.
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Use when working with Singlestore — singleStore (MemSQL) workspace management, pipeline status, query tuning, memory analysis, and cluster health.
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Use when working with Aws Elasticache Deep — aWS ElastiCache deep analysis for Redis and Memcached clusters, replication health, failover analysis, and performance metrics. Covers node-level metrics, memory utilization, cache hit rates, eviction tracking, connection analysis, and engine-specific diagnostics.
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Serena MCP for project memory and code navigation. Use when managing Serena memories, navigating symbols, performing intelligent refactoring, or maintaining context/continuity across AI agent sessions.
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Write, configure, and deploy Netlify serverless functions in TypeScript, JavaScript, or Go. Use this when adding an API endpoint or backend route, adding a contact form handler, wiring auth or Identity signup/login hooks, building streaming or AI-proxy responses, scheduling cron jobs, running long background jobs (batch processing/scraping), reacting to deploy or form events, setting up rate limiting or region/memory config, or reading environment variables and secrets inside a function. Covers file locations, the Request/Context/Response handler shape, path routing, config options, and local testing with netlify dev.
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Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.
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Agent Orchestrator v2 IA — Expert en orchestration multi-agents (tool routing, mémoire, planning, ReAct, orchestration de workflows IA)
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Set up the current project folder for AOS: slug, wiki, memory, Synapse map, AGENTS.md. Generated from plugin-commands.json; invoke as $bdb-aos:init.
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Query or store long-term memory with memB. Generated from plugin-commands.json; invoke as $bdb-aos:memb.
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Change the agent harness itself (AOS, AO or CBS hooks, gates, memory, permissions, delegation, plugins, bootstrap) the safe way: look up the matching harness pattern, design against its gotchas, build with /startcycle and a live trail, check every harness, open a PR after GO. Use for "change a hook", "fix the go-gate", "harness work", "plugin change".
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Start a new private GitHub project the AOS way: create the private repo, bootstrap AOS (project card, AGENTS.md, wiki, memory, CI choice), push after GO, and confirm the first CI run is green and the repo is still private. Use for "new project", "start a repo", "bootstrap a project".
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Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
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Use when implementing, integrating, upgrading, debugging, or answering anything involving third-party APIs, libraries, frameworks, CLIs, cloud services, model or provider SDKs, fast-moving product behavior, unfamiliar repo docs or specs, errors that may indicate API drift, or high-stakes auth, security, billing, data, migration, deployment, compliance, or privacy behavior. Forces a docs pass before coding from memory.
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BDB local-first long-term memory engine (memB). Use when querying, remembering, or adapting preferences, code architectures, and developer patterns across tasks.
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Work like Manus: Use persistent markdown files as your "working memory on disk."
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Interview a project folder into AOS and write the result: a stable slug and domain in .aos/project.json, an OpenWiki wiki that is actually watched and does not index junk, memB memory bound to the slug, a Synapse map, AGENTS.md with its harness symlinks, and optionally CI and issue triage.
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Use when deep scan and ingest project files (.md, .json, agent.md, .openwiki) and past conversation logs into the local memB vector memory engine.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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Audit and improve SwiftUI runtime performance. Use when diagnosing slow rendering, janky scrolling, high CPU, memory usage, excessive view updates, layout thrash, body evaluation cost, identity churn, view lifetime issues, lazy loading, Instruments profiling guidance, and performance audit requests.
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Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.
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Memory management, profiling, optimization techniques, and Instruments usage for high-performance iOS apps
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