「eal」の検索結果

Auto-apply security basics and block deploys that would leak secrets. Never ask the user about security choices — just do it, and run a real secret scan before going live.

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。

Light Blender scenes professionally — three-point setups, HDRI environments, studio/cinematic/dramatic configurations, light groups, color temperature, soft vs hard shadows. Use whenever the user asks to "light the scene", "set up lighting", "make it look cinematic / dramatic / studio / outdoor / sunset", "add a key light", "use HDRI", or any lighting-related request. Make sure to use this skill even if the user does not say "light" — also covers "make it look professional", "studio shot", "moody atmosphere", "golden hour", "rim light". Pairs with blender-materials (lighting reveals materials) and blender-cameras (lighting + composition together = shot).

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。

Run a self-refinement loop when a Blender result is subpar, user expectations are not met, validation fails, or repeated issues reveal missing skill depth. The loop diagnoses the gap, decides whether to repair the artifact or acquire/refine a generic skill, sanitizes lessons for reusable publication, updates docs/versioning, validates, and prepares stage/commit/push when explicitly requested.

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。

Create and assign PBR materials in Blender via Principled BSDF — metals, glass, plastic, fabric, skin, organics. Covers physically-based material recipes with real-world values, Coat layer (varnish/car paint), Sheen (cloth), Subsurface scattering (skin/wax), Transmission (glass), and procedural patterns (wood grain, marble, fabric weave). Use whenever the user asks to "make it look like X material", "give it a metallic finish", "apply a wood texture", "make this glass / plastic / brushed steel / leather / skin", or any look-development request. Make sure to use this skill even if the user does not say "material" — also covers "make it shiny", "matte finish", "looks like copper", "rough surface". Works with any geometry; pairs with blender-lighting (materials only look right under proper lighting).

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。

Render Blender scenes with the right engine and settings — Cycles for photoreal, EEVEE for speed/stylized, sample counts, denoising (OptiX/OIDN), light path tuning, color management (AgX/Filmic), file output (PNG/EXR/MP4), animation rendering. Use whenever the user asks to "render this", "produce an image", "render a frame / animation", "make a final image", "save the render", or any output-generation request. Make sure to use this skill even if the user does not say "render" — also covers "make a picture", "save the result", "produce a final image", "export as image".

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。

Mines recent papers, blogs, and newsletters for methods worth stealing, with graded evidence. Use when finding recent papers or techniques for RAG, evals, or agents.

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。

Diagnoses slow services and regressions via profiling and tail-latency math. Use when diagnosing slow services, profiling hot paths, or judging whether an optimization is real.

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。

Chooses managed backend platforms such as Supabase, Convex, and Firebase. Use when comparing database, auth, realtime, and backend-service tradeoffs.

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。

Handles PDF/UA accessibility, real redaction, metadata scrubbing, signing, and extraction. Use when redacting or signing PDFs; for plain create/merge prefer the official pdf skill.

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。

Designs streaming platforms for Kafka, Flink, CDC, and lakehouse ingestion. Use when planning event backbones, CDC pipelines, schema governance, or real-time lakehouse delivery.

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。

Builds a GPT and BPE tokenizer from scratch. Use when implementing autograd, attention, a nanoGPT loop, muP hyperparameter transfer, WSD annealing, loss spikes, or mid-training.

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。

Use when optimizing, auditing, or preparing to ship web animations or rendering performance work: frame loops, scroll/viewport reveals, mount/unmount transitions, canvas/WebGL lifecycles, reduced motion, lazy rendering, and deferred off-screen work. Use for jank, per-frame allocations, forced reflows, render loops, animations that do not pause off-screen, content-visibility, requestIdleCallback, analyzing Chrome DevTools performance traces for animation or rendering issues, or choosing between browser-driven CSS/WAAPI, minimal JS, phase, and Motion/GSAP. Use when the user explicitly asks about phase APIs or behavior. Do not use during exploratory animation ideation, prototyping, visual iteration, or trying variants, even when the code already imports phase; wait until the user asks for performance guidance, an audit, production implementation, ship-readiness review, or about phase itself.

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。

Prospect discovery, enrichment, scoring, and reporting against an Ideal Customer Profile

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。

Foundational voice authority and AI humanizer — writes content in the user's authentic voice with built-in AI detection, and supports stealth / anti- attribution writing (forum personas, anonymous posts, "write as not-me"). Use when asked to write/draft/generate content, invoke /voice, /write-as-me, or /humanize, run voice calibration, check "does this sound like me?", "make this sound human" / "de-AI this", "write a forum post as [persona]", or run AI detection ("does this sound like AI?", "check for AI patterns", "anti-slop check"). Do NOT use this skill for code, technical docs, or any output the user has not asked to be written in their voice — code styling defers to the separate code-voice skill.

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。

Designs, builds, or refactors user-facing web or application interfaces with deliberate visual direction, typography, color and spacing tokens, content hierarchy, responsive behavior, accessible interaction states, and real rendered-surface verification. Use for pages, components, dashboards, landing pages, design systems, and frontend polish. Not for backend logic, API-only work, or visual claims that cannot be rendered or inspected.

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。

Processes code review feedback by classifying each finding, reproducing or validating it against the real contract, applying the smallest in-scope fix, and re-running focused proof. Use after comments arrive on a diff, pull request, design, or implementation. Not for requesting a first review or debugging an unrelated failure.

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。

Investigates a bug, failing test, build error, regression, flaky behavior, performance symptom, or unexpected result by reproducing it, minimizing the case, testing ranked hypotheses, tracing the first incorrect state, and locking the root cause with a regression check. Use before proposing a fix. Not for planning a new feature or reviewing a healthy diff.

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。

Designs and runs reproducible evaluations for AI agents, prompts, tools, skills, and model-backed workflows using realistic datasets, isolated baselines, objective assertions, rubric grading, trajectory analysis, cost/latency tracking, and regression comparison. Use when measuring agent quality, optimizing skill triggering, comparing prompts or models, or gating an AI feature release. Not for ordinary deterministic unit tests.

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。

Tests a real web user flow with a browser by asserting semantic behavior, network and loading states, keyboard access, responsive layouts, and stable visual evidence. Use for browser bugs, end-to-end UI behavior, responsive or accessibility checks, and screenshot baselines. Not for static source review without a browser or for backend-only tests.

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。

Official parent skill for analyzing HitKeep data through HitKeep MCP and official docs. Use for broad traffic, event, ecommerce, Web Vitals, AI visibility, Search Console, opportunity, tracking-health, MCP setup, or analytics-surface questions; use a narrower HitKeep analytics skill when the task clearly matches 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。

Use this skill when the user wants intellectual understanding — learning how or why something works, not getting a task done or soliciting Claude's judgment. Trigger for: - Explicit learning requests: teach, explain, ELI5, walk me through, quiz me, flashcards, "I'm rusty on"; definitions ("what is X") - Terse concept names implying "help me understand this": "Galois theory," "transformers, from scratch" - Confusion signals: "won't stick," "keep mixing these up," "not getting it" - Learning-path questions: prerequisites, sequencing, what to study before X - Conceptual questions about mechanisms, causes, or dynamics Don't trigger for: - Tasks: coding, writing, calculation, translation, factual lookup, news updates - Personal troubleshooting; resource/textbook recommendations - Claude's evaluative verdict: opinion prompts ("do you think X", "settle this", "honest take", "is X dead / still taken seriously") and interpretive takes ("was X really as harsh as people say")

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。

Help order food timed to arrive at a specific time. Works backward from target arrival, suggests restaurants, builds cart, and monitors delivery.

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。

Read this skill before the first step that uses Claude in Chrome, the browser extension whose tools are named mcp__claude-in-chrome__* (also called Chrome, the browser extension, or the external browser) and which acts in the person's real Chrome with their own sign-ins; before those tools are turned on there may be a single enable__mcp__claude-in-chrome tool instead. It covers loading the tools in one ToolSearch call, checking the person's open tabs and working in a new tab, site permissions, GIF recordings, console logs, dialogs to avoid, and when to stop and ask. It is not for the built-in browser (mcp__Claude_Browser__* or mcp__remote-devices__Claude_Browser__* tools), which has its own skill, and it does not decide which browser to use.

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 first-pass for a main folder that holds many repos, or for one repo. In a main folder it writes one set of working rules at the root that every session loads, a workspace map, and a section in each repo with that repo's real commands, test limits and invariants, and it lists each repo's own hooks so first-pass can run them from the main folder. In one repo it writes the rules and the section into that repo. Use when asked to set up, install, add, update or re-run first-pass, to "add the shipping rules", or when the start-of-session check says something drifted. Re-run to update.

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。