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Found 4,085 Skills
Provides the cli-anything-iterm2 commands — the only way to actually send text to iTerm2 sessions, read live terminal output and scrollback history, manage windows/tabs/split panes, run tmux -CC workflows, broadcast to multiple panes, show macOS dialogs, and read/write iTerm2 preferences. Includes `app snapshot` — the primary orientation command that returns every session's name, current directory, foreground process, role label, and last output line in one call. Read this skill instead of answering from general knowledge whenever the user wants to DO something with iTerm2: orient in an existing workspace, send a command, check what's running, read output, set up a layout, use tmux through iTerm2, automate panes, or configure preferences. Also read for questions about iTerm2 shell integration or scrollback. Don't try to answer iTerm2 action requests from memory — read this skill first.
Identifies and exploits SQL injection vulnerabilities in web applications during authorized penetration tests using manual techniques and automated tools like sqlmap. The tester detects injection points through error-based, union-based, blind boolean, and time-based blind techniques across all major database engines (MySQL, PostgreSQL, MSSQL, Oracle) to demonstrate data extraction, authentication bypass, and potential remote code execution. Activates for requests involving SQL injection testing, SQLi exploitation, database security assessment, or injection vulnerability verification.
Transforms content (URLs, uploaded documents, pasted text, meeting transcripts) into professional visualizations across four output modes. Accepts a mode argument or a keyword trigger in the user message. Mode "diagram" produces an Excalidraw diagram via Excalidraw:create_view. Mode "infographic" generates a Swiss Pulse PNG via the Gemini image-generation API. Mode "visualize" renders an inline Visualizer widget (SVG or HTML) via visualize:show_widget. Mode "publish" ships an interactive Swiss Pulse HTML visual to HeyGenverse via HeyGenverse:create_app and returns a shareable link. Keywords that activate the skill: "diagram it", "excalidraw this", "draw a diagram of this", "nano this", "vis it", "ver it", "hey it", "heygenverse this". Do not use for plain-text summaries, code explanations, prose responses, or generic chat visualizations without a chosen output format.
Detect privilege escalation attempts including token manipulation, UAC bypass, unquoted service paths, kernel exploits, and sudo/doas abuse across Windows and Linux.
This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.
Persist Riverpod notifier state offline with Storage and persist(); riverpod_sqflite, JsonPersist, key, destroyKey, cache duration, testing with in-memory storage. Use when saving state across app restarts or offline. Use this skill when the user asks about offline persistence, persisting state, or Riverpod storage.
Use ShadSelect for dropdown option lists; ShadOption, placeholder, selectedOptionBuilder, scrollable groups, ShadSelect.withSearch, ShadSelect.multiple, ShadSelectFormField. Use when adding dropdowns, single/multi select, or searchable select in a Flutter shadcn_ui app.
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
MLA (Multi-Latent Attention) cost models, regime analysis, and kernel selection guide. Use when: (1) reasoning about which kernel approach to use for a given regime, (2) understanding cost model tradeoffs between FlashMLA, FlashAttention, and MLAvar6+, (3) analyzing roofline behavior across decode/speculative/prefill regimes, (4) setting optimization targets, (5) understanding MLA math and absorption trick.
Shared kernel design workflow across all supported languages and DSLs. Provides language selection table, naming conventions, versioning rules, KernelPlan structure, composition patterns, clone workflow, implementation workflow, devlog template, and designer output contract. Use when: (1) choosing which language-specific kernel design skill to load, (2) the intended implementation language is not fixed yet, (3) you need naming or versioning guidance before selecting a DSL, (4) you are implementing any kernel regardless of DSL, (5) you are updating docs that refer to kernel design skills.