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概要と使いどころ
You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.
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Docker Bench for Security is an open-source script that checks dozens of common best practices around Dağıt:ing Docker containers in production. Based on the CIS Docker Benchmark, it audits host confi
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bu skill covers hardening container images by minimizing attack surface, removing unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations to produce secure production-ready images.
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Use this skill whenever helping someone build, design, or troubleshoot layouts in Elementor Pro V4 using the visual editor. Triggers on ANY mention of Elementor, page builder, Flexbox widget, Div block, section layout, widget configuration, responsive breakpoints, dark mode, scroll animations, Interactions tab, Variables Manager, Class Manager, Custom Code snippets, or any visual design work inside the Elementor panel. Also trigger when someone asks how to position elements, set spacing, apply typography, configure dark mode, prevent FOUC, or build scroll-triggered effects in Elementor. ALWAYS use this skill before suggesting any Elementor V4 implementation — it contains critical V4-specific panel structure, common AI mistakes to avoid, and production-tested patterns.
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Music-video production skill for turning an existing MP3 into a pure-visual (no-voiceover) music video through a bundled Busabase App-in-Skill UI. Use when the user wants to build an MV from a song they upload: write a one-line concept, keep a cast of characters with reference cards, and break the song into storyboard shots where each shot has a scene description and an image + video that can be either AI-generated (image-to-image from the character cards, draft video via local LTX) or uploaded by the user. Same character/storyboard management model as kelly-drama. Song generation (creating the song itself, incl. voice-cloned singing) is a documented future capability.
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Short-drama and product-video development skill for planning drama projects through a bundled Busabase App-in-Skill UI, with each project linked to a specified HyperFrame project and each episode linked to a HyperFrame composition for final motion/video tuning. Use when the user asks to create, edit, organize, or review short-drama scripts, product explainer episodes, character consistency cards, series bibles, episode beat sheets, storyboard shots, HyperFrame project/composition links, production checklists, or a drama planning workbench.
日本語の概要は準備中です。原文の説明を表示しています。
Disciplined diagnosis workflow for hard, intermittent, or multi-component bugs and performance regressions: inspect logs and existing evidence, build a feedback loop, test falsifiable hypotheses, instrument gaps, and verify the outcome. Use when the root cause is unclear, reproduction is unreliable, several causes or components are plausible, or performance has regressed. Do NOT use for obvious single-cause errors, straightforward configuration or syntax fixes, planned feature work, or broad architecture review. Diagnosis-only requests stop after reporting the root cause and evidence; apply a fix only when the user requested one.
日本語の概要は準備中です。原文の説明を表示しています。
Structured debugging methodology — reproduce, isolate, hypothesize, verify. Covers git bisect, binary search, logging, and minimal reproduction.
日本語の概要は準備中です。原文の説明を表示しています。
Handle production incidents — triage, mitigate, communicate, and write postmortems.
日本語の概要は準備中です。原文の説明を表示しています。
Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$ai_generation` and `$ai_evaluation` events with `$session_id` linkage to the trigger user's recording (the standard setup post the session-summary linkage PRs).
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Copy a feature flag from one PostHog project to one or more target projects in the same organization. Use when the user wants to duplicate a flag, promote a flag from staging to production, sync flags across projects, or replicate a flag configuration in a different workspace. Covers cohort remapping, scheduled-change handling, encrypted payloads, and the safe defaults (disabled in target, no scheduled changes).
日本語の概要は準備中です。原文の説明を表示しています。
Dockerize an application with a production-ready Dockerfile, docker-compose setup, and .dockerignore.
日本語の概要は準備中です。原文の説明を表示しています。
IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
日本語の概要は準備中です。原文の説明を表示しています。
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
日本語の概要は準備中です。原文の説明を表示しています。
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
日本語の概要は準備中です。原文の説明を表示しています。
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
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Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
日本語の概要は準備中です。原文の説明を表示しています。
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
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Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
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Authors bioinformatics pipelines in WDL (Workflow Description Language) run by Cromwell or miniwdl, targeting the GATK/Broad and Terra/AnVIL/BioData Catalyst cloud ecosystem, with tasks, workflows, scatter-gather parallelism, structs, and a runtime block that sizes the cloud VM. Use when deciding to target Terra/AnVIL/GATK/WARP (chosen for the ecosystem, not the language); sizing runtime disks dynamically for a fresh-per-task cloud VM (ceil(size(f)*factor)+buffer); choosing preemptible vs on-demand VMs by task length and idempotency; picking Cromwell (production, cloud, call-caching) vs miniwdl (local dev, miniwdl check linting, readable errors); enabling and debugging call-caching silent-miss modes; pinning Docker by digest for reproducibility and cache stability; or scattering an array for parallel fan-out.
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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
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Walk through the devnet → mainnet deployment checklist for a Soroban contract or Stellar dApp. Use when a user says "deploy to Stellar mainnet", "going live on Stellar", "deploy my Soroban contract", "Stellar production deployment", "mainnet checklist", or "ship it on Stellar". Covers pre-deployment verification, deployment mechanics with stellar CLI, and post-deployment monitoring with Soroban-specific gates.
日本語の概要は準備中です。原文の説明を表示しています。
Instructions for translating Google Stitch designs (HTML/Tailwind) into production-ready Flutter/Dart code.
日本語の概要は準備中です。原文の説明を表示しています。