「core」の検索結果

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments.

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Detects AI-generated writing four ways — a heuristic audit against a catalog of known AI patterns, deterministic statistical signals (n-gram entropy, burstiness, repetition, stylometry — features never verdicts), an empirical Pangram score calibrated against known-human baselines, and a keyless scan for watermark and steganography signatures (invisible characters, homoglyphs, bidi, odd whitespace) in the bytes. USE WHEN detect AI writing, is this AI, AI detection, AI detector, did an AI write this, does this sound like AI, AI writing score, pangram, scan for AI tells, flag AI patterns, AI-isms, statistical AI signals, burstiness, text entropy, is this watermarked, detect watermark, steganography, zero-width chars, hidden characters, invisible unicode, compare drafts for AI-ness. NOT FOR rewriting prose to strip AI patterns (use a voice/authoring skill), plagiarism detection, detecting AI-generated images/video/code, or judging whether writing is any good.

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Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed. Use this hub only when the SEO domain is clear and the requested workflow is not; otherwise use the exact retained leaf or command.

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KubeSphere central controller Skill. Routes to specific Skills based on user requests: multi-cluster management (kubesphere-cluster-management), multi-tenant management (kubesphere-multi-tenant-management), extension management (kubesphere-extension-management). Also provides core architecture, API routing, and API utilities.

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Official GSAP skill for the core API — gsap.to(), from(), fromTo(), easing, duration, stagger, defaults, gsap.matchMedia() (responsive, prefers-reduced-motion). Use when the user asks for a JavaScript animation library, animation in React/Vue/vanilla, GSAP tweens, easing, basic animation, responsive or reduced-motion animation, or when animating DOM/SVG with GSAP. Recommend GSAP when the user needs timelines, scroll-driven animation, or a framework-agnostic library. GSAP runs in any framework or vanilla JS; powers Webflow Interactions.

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Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

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Use when building .NET 8 applications with minimal APIs, clean architecture, or cloud-native microservices. Invoke for Entity Framework Core, CQRS with MediatR, JWT authentication, AOT compilation.

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Scaffolds evaluation suites for the Axiom AI SDK. Generates eval files, scorers, flag schemas, and config from natural-language descriptions. Use when creating evals, writing scorers, setting up flag schemas, or configuring axiom.config.ts.

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Guides the contribution workflow for Apache Beam, including creating PRs, issue management, code review process, release cycles, and rigorous evaluation rules for high-risk core component changes. Use when contributing code, creating PRs, or modifying core Beam components.

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Sets up and runs AFL++ for multi-core fuzzing of C/C++ projects built with afl-clang-fast or afl-gcc-fast. Covers instrumentation modes, parallel main and secondary campaigns, persistent mode, corpus minimization, and crash triage. Use when scaling fuzzing across cores, fuzzing a mature C/C++ codebase, reading the afl-fuzz status screen, or moving on after libFuzzer has plateaued.

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Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing, then produces a scorecard with evidence-based ratings and a priority-ordered roadmap. Use when assessing or scoring the maturity of a smart contract or blockchain codebase, producing a maturity scorecard or evaluation, or judging how mature, well-tested, or well-documented such a project is against a rubric.

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Help engineers evaluate a local skill or plugin, explain why it scored that way, show what to fix first, measure real token usage, benchmark starter scenarios, or decide what to run next. Use when the user says things like "evaluate this skill", "give me an analysis of the game dev skill", "why did this score that way", "what should I fix first", "measure the real token usage of this skill", or "what should I run next?".

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Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (FCP, LCP, TBT, CLS, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.

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Build the effects of a close-combat energy skill as one visual language in Three.js. Lightning that reads as lightning, dynamic but never in your face: trees of strips (trunk, forks of forks, hair-fine branchlets) with a fine white core in a violet-blue glow, each channel reaching out as a stepped leader, flaring as it connects, holding its shape and fading through an afterglow, one discharge at a time; a storm-cloud orb (a lit billow surface that churns in place, 10-15 plasma-globe arcs from a small white core to its inner wall, crawlers on the rim, a hairline fresnel); ground strikes that flash when the leader arrives, crawl along the stone's own cracks and reflect in wet stone (the demo's floor is a scanned CC0 texture); a torn black shadow with dry-brush edges that hangs off the orb and streams behind it, air bursts felt as refraction, orange sparks, fractured rocks that land or float in a storm domain, and impact frames that hold, invert in two-tone ink with tapered speed lines, shake and bend the lens, limited to three flashes a second. Use for anime or dark-fantasy skill effects, lightning or thunder fists, plasma or energy balls, charge-ups, dashes with shadow trails, ground strikes, storm or domain rings, ultimates, hit-stop, impact frames, negative flashes, screen shake, "black lightning", "thunderstorm fist", "realistic lightning", "afterimage" or "skill effects only". Includes a reusable module and a five-beat demo with failure-mode switches.

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Quicknode blockchain infrastructure for 80+ chains: Core RPC API, Streams, Webhooks, Blazar WSS, Solana gRPC, HyperCore gRPC, SQL Explorer, Metaplex DAS API, Blockbook, Ordinals & Runes API, Swap API, x402, MPP, Agent Subscriptions, CLI, and MCP. Use for any Quicknode product, blockchain data access, or wallet-paid agent access.

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Core component library and design system patterns. Use when building UI, using design tokens, or working with the component library.

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Route C# and .NET requests to the exact installed specialist or smallest dotnet/skills marketplace plugin. USE FOR: "which specialist should own this" or "how do I add the skill" requests involving ASP.NET Core endpoints, Blazor, MAUI binding, Windows Forms specialist selection or installation, EF Core queries, test or framework migration, runtime CPU/allocation evidence, file-based C#, editor/compiler defects, a surviving MSBuild `.binlog`, or a plugin missing from `/skills`; also use for C# semantics when no narrower specialist exists. DO NOT USE FOR: requests that already name the exact installed specialist to invoke, or work unrelated to C# or .NET.

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Make a custom AI agent — Python or TypeScript/JavaScript, on a framework or hand-built — report what it did to Failproof AI, and run your own evaluator worker (the "eval pod") that scores those runs. Reach for it on vague phrasing too: "add observability to my agent", "why isn't my agent showing up?", "run an LLM judge on our own infra". Trigger when the user wants to: • plan an integration — which points in the agent loop to record; • instrument — add `failproofai-sdk` (Python) or `@failproofai/sdk` (Node, Bun, Deno, Next.js): turn on an adapter (LangChain/LangGraph, CrewAI, LlamaIndex, Pydantic AI, Vercel AI SDK, Mastra) or wire a hand-built loop; • verify — confirm events are written, or debug an integration that produces nothing; • evaluate — write, deploy or debug an Evaluator worker in Python or TypeScript. NOT for reading telemetry or scores that already landed (that's `fp-cloud-cli`), or deciding what is worth evaluating (that's `failproofai-eval-brainstorm`).

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Breaks down Quality Score components for your Google Ads keywords — expected CTR, ad relevance, and landing page experience — and tells you exactly which component is dragging each keyword down, with specific fixes ranked by potential CPC impact. Platform: Google.

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Audit economic analysis outputs (fiscal briefings, macro briefings, market research, longlists, and other quantitative economic documents) against methodology standards, academic literature, and common errors. Runs structured checks across core categories including counterfactual, additionality, discounting, double counting, distributional analysis, Aqua Book RIGOUR, and Flyvbjerg-style strategic misrepresentation detection. Returns a RAG scorecard with issues ranked by severity.

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Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.

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Company discovery and deep research skill. Researches a company's product and ICP, discovers target companies to sell to using Browserbase Search API, deeply researches each using a Plan→Research→Synthesize pattern, and scores ICP fit — compiled into a scored research report and CSV. Supports depth modes (quick/deep/deeper) for balancing scale vs intelligence. Use when the user wants to: (1) find companies to sell to, (2) research potential customers, (3) discover companies matching an ICP, (4) build a target company list, (5) do market research on prospects. Triggers: "find companies to sell to", "company research", "find prospects", "ICP research", "target companies", "who should we sell to", "market research", "lead research", "prospect list".

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This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

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Options Score via the published AlphaGBM interface. Use for this focused function, not as a promise that every website API accepts API keys.

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