「collection」の検索結果

Automate Webflow CMS collections, site publishing, page management, asset uploads, and ecommerce orders via Rube MCP (Composio). Always search tools first for current schemas.

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Automate Shopify tasks via Rube MCP (Composio): products, orders, customers, inventory, collections. Always search tools first for current schemas.

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Create or redesign self-contained single-file HTML artifacts with a visual direction shaped by the user's brief, project, and subject. Use when HTML is the deliverable for a report, explainer, landing page, presentation, tool, mixed artifact, or broad request. This is the collection's only implicit router. Route clear wireframe, prototype, mockup, plan, or diagram requests to the matching direct-invocation specialist when available. Do not use for ordinary application implementation when a standalone HTML file is not the deliverable.

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Use when searching, extracting, ingesting, or querying a document collection with the NeMo Retriever 26.8.1 CLI, including local LanceDB indexes and deployed Retriever services. Use for PDFs, images, Office files, HTML, text, audio, and video; not for editing documents or web search.

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Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals. Not for applying optimization patterns once profiling has identified a bottleneck (→ See `samber/cc-skills-golang@golang-performance` skill).

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Create, inspect, validate, explain, and improve Ralph hat collections. Use this skill whenever the user asks to make or refine a `.ralph/hats/*.yml` workflow, debug hat routing, explain event topology, or tune a multi-hat Ralph run.

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Use when testing Ralph's hat collection presets, validating preset configurations, or auditing the preset library for bugs and UX issues.

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Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API. Triggers: "azure-monitor-ingestion", "LogsIngestionClient", "custom logs", "DCR", "data collection rule", "Log Analytics".

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Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.

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Design voice and conversational interfaces — dialog flows, error recovery, and persona. Use when the interface speaks and listens rather than being tapped. For graphical input collection, use `form-design`.

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Audit a Python project's installed dependencies for known CVEs by wrapping pip-audit (PyPA's official vulnerability auditor) and emitting findings in the canonical penetration-tester schema. Detects vulnerable direct AND transitive packages, normalizes pip-audit's severity output via OSV severity bands, falls back to pip list --outdated when pip-audit isn't installed, and supports requirements.txt, pyproject.toml (PEP 621), Pipfile.lock, and poetry.lock as input sources. Use when: pre-merge gate on a Python project, post-incident sweep after a PyPI compromise (e.g. ctx, request-toolbelt typosquats, ultralytics 8.3.42 compromise), SOC2 evidence collection, or inheriting an unfamiliar Python codebase. Threshold: any HIGH or CRITICAL CVE in the resolved dependency tree. MODERATE / LOW reported informationally. Trigger with: "audit python deps", "pip vulnerability scan", "check pypi packages for CVEs", "pip-audit run".

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Audit a Node.js project's installed npm dependency tree for known CVEs by wrapping the npm audit JSON output and emitting findings in the canonical penetration-tester schema. Detects direct AND transitive vulnerabilities, normalizes npm's severity scale (info/low/moderate/ high/critical) to the shared Severity enum, and parses both v1 and v2 audit output formats so the skill works against npm 6 and npm 7+ lockfiles. Use when: pre-merge gate on a Node project, post-incident sweep after a transitive package compromise (e.g. event-stream, ua-parser, node-ipc, color.js), SOC2 vendor-management evidence collection, or auditing an inherited or acquired Node codebase. Threshold: any HIGH or CRITICAL CVE in the resolved dependency tree. MODERATE / LOW reported informationally. Trigger with: "audit npm deps", "npm vulnerability scan", "check node packages for CVEs", "npm audit".

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Optimize Apple Notes automation performance for large note collections. Trigger: "apple notes performance".

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Assemble a minimal, reproducible Adobe diagnostic bundle with default-deny collection and verifiable redaction. Use for engineering or Adobe support escalation. Use when the task requires adobe redacted diagnostic bundle. Trigger with "Adobe debug bundle", "collect Adobe diagnostics", or "prepare Adobe support case".

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Diagnose and recover failing or stuck Hugging Face Space deployments for OpenEnv environments. Use when deploying envs from `envs/` to the Hub (`openenv` namespace with version suffixes), when Spaces are in `BUILDING`/`APP_STARTING`/`RUNTIME_ERROR`, or when release collections need to be reconciled after targeted redeploys.

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Build MCP servers using the Leanmcp SDK with decorator-based TypeScript. Use this skill when users ask for "leanmcp", "MCP with decorators", "MCP with authentication", "MCP with elicitation", "MCP with environment injection", or want a simpler, more elegant way to build MCP servers. Leanmcp provides automatic schema generation, dependency injection, authentication, and user input collection.

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Tencent IMA OpenAPI skill for notes and knowledge base management. Use when user mentions knowledge base, notes, memos, file uploads, web page collection, or knowledge search. Supports notes CRUD, knowledge base file upload, web link addition, and content search.

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Tencent Survey (wj.qq.com) online survey platform skill. Supports survey creation, question management, answer collection, and data export. Use when user mentions surveys, questionnaires, forms, polls, exams, or assessments.

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Machine learning toolkit for genomic interval (BED) data; use it when you need to tokenize BED collections and train embeddings for regions/cells/labels, build consensus peak universes, or run similarity search and downstream ML on chromatin accessibility datasets.

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Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

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Generates complete single-compound network-toxicology research designs from one exposure, one disease or toxic phenotype, and a validation direction. Use when a study centers on one compound–one disease link and needs target collection, overlap construction, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-check, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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。

Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

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Designs retrospective or prospective clinical cohort study protocols for biomedical and clinical research. Always use this skill when the user needs a cohort-based study plan rather than a general study idea, evidence summary, or mechanistic experiment design. Focus on cohort appropriateness, enrollment logic, baseline time-zero definition, follow-up structure, endpoint definition, variable collection, confounding control, and a coherent primary statistical analysis line. Do not invent data availability, follow-up completeness, outcome ascertainment quality, sample size adequacy, or causal interpretability.

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。