1.1万 件 ・ 関連度順
概要と使いどころ
Implement or review a single-index Algolia search contract with filters, facets, pagination, and highlighting. Use when building product or content search against the JavaScript v5 client. Trigger with "Algolia search workflow", "add facets", or "searchSingleIndex".
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Build a reliable Algolia indexing pipeline for full replacements, incremental updates, settings, synonyms, and rules. Use when a source of truth must publish deterministic search state. Trigger with "Algolia indexing workflow", "replace index records", or "sync Algolia".
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Separate Adobe development, stage, and production organizations/projects/workspaces, credentials, profiles, storage, events, budgets, and evidence. Use when the task requires adobe multi-environment isolation. Trigger with "Adobe environments", "separate Adobe staging", or "Adobe workspace setup".
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Organization-grade cloud and supply-chain attack-surface discovery: S3/GCS/Azure Blob bucket discovery via observed-name mining (CNAME/cert-SAN/Wayback) and bounded two-class permutation (6 prefixes x 15 suffixes on trusted tokens, bounded target-bound expansion on subdomain stems), existence (HEAD/GET) vs public-listing confirmation, object-key triage into 9 value tiers (database dumps, credentials, IaC state, kubeconfig, VCS dirs, config, archives, PII, logs), dangling-CNAME bucket-takeover detection, and the ownership-gated severity model that stops an unattributable public bucket from becoming a false CRITICAL; the fully offline AWS-account-ID recovery from a leaked AKIA/ASIA/AROA access key (base32 decode, runnable stdlib Python, canonical test vector, AWS-documentation-example-ID screening); dependency-confusion confirmation for npm/PyPI (internal-signal classifier -- private-registry binding vs org-namespace match -- paired with a read-only public-registry 404 check and the npm scope-claimability nuance the public search API misses); and passive cloud-native/container/Kubernetes/CI control-plane fingerprinting (Lambda URLs, API Gateway, Cloud Run, App Service, kubelet/etcd/K8s API/dashboard, Jenkins/GitLab/Argo CD) as an org-attribution and exposure surface. Passive/discovery only -- no exploitation, no credential submission, no active control-plane confirmation (a stage-6 validate_cloud active tier is described but out of scope). Use when enumerating a target's cloud storage footprint, recovering an AWS account ID from a leaked key, confirming a supply-chain dependency-confusion vector, or fingerprinting cloud-native/K8s/CI infrastructure for an authorized external recon engagement.
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Release workflow for deploying OpenEnv environments to Hugging Face Spaces and keeping canonical references in sync.
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Working pattern for enterprise-edition features in Grida — the commercial/hosted concerns (entitlement, BYOD, billing) on top of the OSS core. Anchor for the `GRIDA-EE: <surface>` grep marker, `(ee)` route group, `*-hosted` package suffix, and namespaced `grida_*` schema. Use when adding or touching an EE-only feature, or deciding whether a feature is EE territory. Surface skills (`platform-consumers`) are siblings.
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Doctrine for designing and evolving any **SDK** Grida ships — TypeScript, Rust, or otherwise. "SDK" here means a surface that crosses a foreign-or-foreign-treated boundary: published packages, separately-versioned consumers, FFI bindings, public-by-design modules. An SDK's job is to refuse; a strict, honest surface rejects the wrong contents and keeps the package testable in isolation. Default is "core, not customizable"; customization is the exception, defended by a deciding table. Use when authoring or evolving any such surface — `@grida/*` published packages, engine crates (gridaco/nothing `crates/*`) published or FFI-exported, intent/message vocabularies, any contract a second author will compile against. Internal-only helper packages are welcome to follow, not forced. Companion skill for two-sided contract work: $sdk-seam. Critique partners: $pedantic, $etiology. Related: $naming.
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Structured approach to workplace conflicts, performance discussions, and challenging feedback using preparation-delivery-followup framework. Use when preparing for tough conversations, addressing conflicts, giving critical feedback, or navigating sensitive workplace discussions.
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Create and manage Claude Code plugins with proper structure, manifests, and marketplace integration. Use when creating plugins for a marketplace, adding plugin components (commands, agents, hooks), bumping plugin versions, or working with plugin.json/marketplace.json manifests.
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Start and scale networked products using Andrew Chen's "The Cold Start Problem" framework for network effects. Use when the user mentions "network effects", "chicken and egg", "cold start", "two-sided marketplace", "atomic network", "hard side", "liquidity", "critical mass", "invite-only launch", "how do I get my first users", or "the marketplace has no buyers or sellers". Also trigger when launching a marketplace, social, or collaboration product that is worthless without other users, deciding launch sequencing and seeding tactics, or diagnosing stalled network growth at scale. Covers the five stages: cold start, tipping point, escape velocity, hitting the ceiling, and the moat. For word-of-mouth virality, see contagious. For habit-driven retention, see hooked-ux.
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Raises the visual and interaction quality of an interface — layout, hierarchy, type, spacing, density, and the details that separate a considered product from a generic one. Use this when a screen works but looks unfinished or default, when a layout feels crowded or arbitrary, when a page has no clear focal point, or when an interface needs to feel trustworthy rather than merely functional.
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Applies a deliberate visual direction to an interface — minimalist editorial, industrial utilitarian, or high-polish commercial — each with its own type scale, palette behavior, surface treatment, and motion. Use this when a product needs a point of view rather than defaults, when choosing between visual directions, when an interface reads as generic, or when restyling something without changing its structure.
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Design any process, idea, problem, or body of knowledge into an ICM (Interpretable Context Methodology) workspace — folder structure as agent architecture — or restructure an existing folder, repo, or vault into one. Use when the user wants to (1) turn a recurring workflow into an agent-runnable folder pipeline, (2) organize scattered notes, files, or knowledge into a library one AI agent can walk, (3) map a team or company as connected context ("context map", "second brain", "team brain", "knowledge base for AI"), (4) audit a codebase or mixed folder into a walkable edit map (objects, processes, change-impact) so later agents can change it without slurping the tree, (5) audit or restructure an existing workspace to ICM conventions, or (6) says "make this an ICM", "ICM this", "map this repo", "audit this folder", "what would a change hit", "build me a workspace", or "structure this for agents".
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Teaches evidence-based analysis of transaction logs and traces. Use when reconstructing transaction behavior from tables, TSV/CSV files, timestamps, counters, object histories, validation records, abort records, retries, partial logs, or when producing classified transaction outputs from trace evidence.
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Für Marketplace AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: marketplace-agb-mediation-escalation.
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Für Marketplace AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: marketplace-agb.
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视频创作总控/导演。把一条视频从「参考素材 → 选题 → 脚本 → 剪辑 → B-roll → 字幕 → 封面」串成一条流水线，判断当前该走哪一步、调用哪个 space-video-* 子技能，并在成片后做复盘。当用户说「做个视频」「我要拍条口播」「这个视频从头帮我做」「视频工作流」「video pipeline」，或不确定该用哪个视频子技能时触发。
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小红书笔记正文撰写。输入主题/产品/经历/素材，产出可直接发布的小红书正文（含开头 3 行、分段骨架、结尾互动、标签组合、合规自检）。覆盖种草测评、干货教程、个人故事、合集清单、避雷拔草、好物分享、经验复盘 7 种笔记类型。当用户说"写小红书""小红书文案""发笔记""笔记正文""种草文""种草笔记""避雷帖""干货笔记""帮我写篇小红书""这个怎么发小红书""小红书怎么写""笔记体检""改成小红书风格"时触发。标题批量生成交给 space-xhs-title，封面/配图交给 xhs-html。
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Create and composite HeyGen digital-human videos in the content workspace. Use whenever the user asks for a 数字人, 数字人5, HeyGen, AI 口播人物, 数字人小窗, 圆形头像口播, full-screen digital human, or asks to make a video that uses the existing avatar. Enforce Creator-plan CLI OAuth, the configured Digital Twin, local IndexTTS2 narration, Avatar III for an unspecified digital human but Avatar V whenever the named preset 数字人5 is requested, adaptive face measurement plus the approved human-reviewed visual-centering offset in the headroom_08 circular bottom-left layout unless full-screen is explicitly requested, subtitle-safe placement, credit-aware master reuse, final QC/archive routing, and mandatory pairing with `heygen-avatar-motion` before any motion prompt or paid generation.
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Stress-test a decision or claim through a sharp, fair Q&A — the questions a good lawyer or skeptical friend would ask before you commit. Use when asked to cross-examine me, ask me hard questions about this, interrogate my plan, or make me defend this. Produces a sequenced line of probing questions (from clarifying to challenging to the killer question), space to answer, and a debrief on where your answers were strong, evasive, or exposed a gap — so weaknesses surface in private before they surface in public.
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Decide when and how your product and communications must (or should) label AI-generated content, and write the disclosure policy — surface-by-surface rules, exact label wording, and the review trigger for regulations like the EU AI Act's transparency obligations. Use when asked 'do we have to label AI content', 'write our AI disclosure policy', 'are we covered for the AI Act', or when marketing/support/product start shipping AI-generated output. Produces a disclosure policy with a per-surface matrix and ready-to-use label copy. Not legal advice.
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Intent-based observability + traceability router across layers, boundaries, and signals. Routes to vendor-specific skills via category taxonomy; owns transport tuning, meta-observability, incident forensics. Use for observability, traceability, telemetry, APM, RUM, metrics, logs, traces, profiles, SLO, incident forensics, tracing architecture work.
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Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a drawing filling the right, and a payoff that letters the closing line and rings the claim. The board is a PHOTOGRAPH and the ink is multiplied onto it, so the surface sheen and window light come through the strokes; the drawings are generated as line art and traced to ordered strokes, then revealed in drawing order on the word they belong to. The layout is SOLVED from the beats, never hand-placed. FREE assembly (Pillow plus ffmpeg) apart from one board photo and one drawing per subject, both one-time per brand. Use for the whiteboard format.
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Finds Fabric items across every workspace with the Catalog Search API when the workspace is unknown, locating an item by display name, inventorying one item type tenant-wide, and returning the workspace and item IDs later calls need. Dataflow Gen1 and Gen2 items are not supported. Use only to locate items; once found, query them with the owning item skill such as sqldw-cli, spark-cli, eventhouse-cli or fabriciq.
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