301건 · 관련도순
Upgrade React applications to latest versions, migrate from class components to hooks, and adopt concurrent features. Use when modernizing React codebases, migrating to React Hooks, or upgrading to latest React versions.
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Manage major dependency version upgrades with compatibility analysis, staged rollout, and comprehensive testing. Use when upgrading framework versions, updating major dependencies, or managing breaking changes in libraries.
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Migrate from AngularJS to Angular using hybrid mode, incremental component rewriting, and dependency injection updates. Use when upgrading AngularJS applications, planning framework migrations, or modernizing legacy Angular code.
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Use when the user gives a topic and wants an automated topic-driven narrated explainer, podcast, or knowledge-summary video (Bilibili / YouTube / Xiaohongshu / Douyin / WeChat Channels), or asks to learn visual design patterns from a reference video/image. Trigger when the user mentions creating a knowledge video, narrated explainer, video podcast, or animated infographic-style video from a topic — even if they don't say "video podcast" explicitly. Also trigger when the user wants to regenerate, re-render, rebuild, update, or iterate on a narrated video this skill already produced — e.g. they edited the script/prompt, changed the visuals, or swapped the background music and want the final video remade (reuse the existing videos/{name}/ directory, never start a new project). Do NOT trigger for generic video editing, trimming, format conversion, color grading, or non-narrative video tasks. Produces 4K video via research → script → TTS → Remotion → MP4 + BGM.
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Scaffold a graded problem set with sections, problems, worked solutions, and short "why this matters" explainers across analytical, empirical, and coding types. Use when user says "make a problem set on X", "scaffold exercises for this lecture", "create practice problems", "generate homework with a solution key", "build a graded assignment on topic Y". Emits a clean student set plus a separate solution key — NOT for grading submissions or auto-checking student answers.
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Use when asked 等保怎么做, 等保 2.0 定级, 等保备案和测评流程, 等保三级要求, 我们的系统需要过等保吗, or prepare an information system for China's Multi-Level Protection Scheme (MLPS 2.0, 网络安全等级保护). Produces a grading recommendation (定级) with the reasoning for the expert review, the filing (备案) and assessment (测评) steps and their usual timing, a gap checklist across the ten control areas of GB/T 22239-2019, and a remediation plan, marked as preparation for a licensed assessor.
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Audit a repo's agent context — CLAUDE.md files, codebase docs, skills, and tool/MCP designs — against Anthropic's Claude 5 context-engineering guidance ("unhobbling": Anthropic cut ~80% of Claude Code's system prompt with no eval loss). Finds overconstraint, conflicting instructions, redundancy, stale facts, and missing "unknown knowns"; produces a scored findings report with concrete rewrites, then applies approved fixes. Use when someone says "audit my CLAUDE.md", "context audit", "unhobble this repo", "review our agent docs/skills/tools", or after upgrading to Claude 5-generation models.
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Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.
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Writes turn-level tests for a LiveKit agent in the user's normal test suite: pytest (Python) or Vitest (Node.js). Use when the user asks to "write tests for my agent", "add a test for this tool", "test the handoff", "pin this bug", "why does my agent test fail", or after building or changing agent behavior that needs regression coverage. Covers the SDK's test session harness, assertions on messages, tool calls and handoffs, LLM judging of a reply against an intent, mocking tools, multi-turn tests, and judging whole conversations with the built-in judges. For interactive poking use debugging-livekit-agents. For grading whole conversations at scale use running-livekit-simulations.
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Guide for upgrading Stripe API versions, webhook endpoints, server-side SDKs, Stripe.js, and mobile SDKs
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Use when moving skills between library workspaces or upgrading from a personal library to a team library. Export from one workspace, import into another.
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Detect quality and efficiency regressions over time using Agent Monitor data — rising error rate (APIError events), falling cache hit rate, growing compaction frequency, and climbing cost-per-session. Splits history into an earlier baseline window and a recent window and reports which metrics are getting worse, by how much, and where. Use when checking whether things are degrading or trending in the wrong direction.
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Use when making multiple independent ad or campaign edits from existing footage with Scenario: hook, CTA, copy, end-card, placement, or targeted visual variants. Also for revising one variant or resuming a partially completed batch. Not for a new commercial from a still image, ordinary single-clip grading, or automatic Cartesian combinations.
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Use when lighting or rendering in Unreal Engine 5.8: physical light units and exposure (EV100, lux, lumens), Lumen GI and reflections, MegaLights, virtual shadow maps, sky, fog and clouds, post process and color grading, the path tracer, or Movie Render Graph stills. Triggers: "Lumen is noisy", "MegaLights noise", "the interior is too dark", "light leaks", neon signs, a night or dusk scene, "fireflies", "path tracer vs Lumen", "render a still", EXR settings.
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Use when setting up URP (or choosing URP vs HDRP), configuring URP assets and quality tiers for PC and phones, lighting a stylized or realistic Unity scene (sun, sky, baked or mixed lightmaps, Shadowmask, Adaptive Probe Volumes, reflection probes), or fixing blurry shadows, light leaks, gray bakes, washed-out images, tonemapping and grading, bloom too expensive on mobile, TAA, STP or MSAA choices, GPU Resident Drawer setup, shader warm-up hitches, or a custom shader that vanished or lost its baked light.
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Use when editing footage that already exists on Scenario through MCP with a tool model instead of a new generation: 3D LUT grading, color correction, film grain, vignette, blur, sharpen, glow, tint, desaturate, posterize, solarize, cubism, crystallize, oilify, chromatic aberration or dodge and burn on a clip; trimming to a time range, splitting at cut points, reversing, resizing, re-encoding, frame extraction, image sequences, background removal, segmentation masks, subjects as layers.
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Audit and improve an entire agentic-development setup, including skills, context, memory, tools, models, permissions, hooks, workflow habits, and secrets hygiene. Invoke for setup reviews, workflow grading, Goblin Mode, or questions about what to improve. Supports shallow, deep, ultra, and yolo modes. Use domain-specific skills for a single model, report, or tenant.
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Cluster API lifecycle management for provisioning, scaling, and upgrading Kubernetes clusters. Use when managing cluster infrastructure or multi-cluster operations.
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Live-site visual QA audit with fix loop. 10-category checklist (~80 items) including AI Slop Detection (10 blacklisted patterns). Atomic commits per fix. Before/after screenshots. A-F grading. Parallel cross-model code-level design audit. Modes: Full, Quick, Deep, Diff-aware, Regression.
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Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.
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Verifies UK case-law citations, pinpoint references and quotations against the official public register — The National Archives' Find Case Law — and statutory references against legislation.gov.uk, before a document that cites them is relied on, served or filed. Every check returns a graded verdict: VERIFIED, MISMATCH (the citation resolves to a different case — the classic AI miscitation), NOT ON REGISTER, OUTSIDE COVERAGE (the register cannot answer — absence proves nothing), or UNCHECKABLE (a law-report citation). The grading exists because there are two ways to get this wrong: citing a case that does not exist, and accusing a real case of not existing. Use when asked to check citations, verify a case exists, confirm a quotation is verbatim, check a pinpoint paragraph, audit a draft's authorities, screen a document for hallucinated cases, or check a statutory provision is in force. Not a substitute for reading the judgment: existence is not authority.
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Turns a legal question, a set of draft questions, or source material such as a statute, jury instruction, rubric, contract checklist, or coding manual into a battery of narrow questions that TypeSafe's Jev, a fast classifier that returns probabilities rather than prose, can answer reliably across many documents. The skill breaks conclusions such as negligence, felony murder, or standing into element-level questions, writes the governing rule into each question, and delivers the questions alone, in Jev's request format or in plain English. Built for legal work that asks the same questions of many texts: coding statutes across jurisdictions, coding opinions or case files for empirical research, checking contracts against a review checklist, screening intake narratives, and grading student work against a rubric. Use when asked to write Jev questions, build a classifier for legal documents, or turn a legal test into yes-or-no questions. Running the questions requires Jev access; writing them does not.
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Route a Bevy 0.20 educational loop. Use when prompts and choices are components, lesson totals are one resource, and grading must run after selection in one chain. Keywords: Bevy, Educational, LessonProgress, Choice, chain, genre.
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Analyze images, video, speech, motion, products, and websites; route local vision, audio, depth, tracking, matting, identity, and restoration models; auto-edit, replicate, enhance, caption, voice, assemble, annotate, validate, and export editable Timeline Studio projects and videos. Use for reference-video remakes, filter and repeated-shot reconstruction, subject-aware reframing, clip splitting, source-time speed curves, Color Wheels grading, ramps and holds, person or product cutout, person or object outline, authorized face swap, shot and timing reconstruction, timeline markers, chapters, beat cues, review notes and ranges, supplied or web-sourced footage, AI video platform selection, cleanup, highlights, product promotion, website walkthroughs, image-to-video assisted edits, optical-flow editing, depth-assisted relighting, grayscale depth-video conversion, depth/2.5D/transition finishing, AI voiceover, subtitles, short-form production, .timeline automation, or editor evaluation.
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