「red」の検索結果

Runs a multi-step procedure as a Python DAG, so ordering, branching and retries are enforced by the runner rather than described in prose a model can generate past. Use for "run these steps in order and retry the flaky one until the check passes", "build a pipeline that fetches, validates, then skips the upload when nothing changed", "make sure these steps cannot be skipped", "resume from where it broke instead of redoing the expensive early stages", "run these independent calls at once and merge the results", or any procedure of 3+ steps with branches, input contracts, or side effects that must not block the critical path. Primitives are depends_on, when=, validate=, retry_until=, detached= and journal_path=. Not for a single sequential call, for steps needing reasoning between them that no predicate captures, or for async and distributed work. To audit whether one verification check can actually go red, use gating. To fan work out across many subagents, use a dynamic workflow.

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Securely manages API credentials for multiple providers (Anthropic Claude, Google Gemini, GitHub). Use when skills need to access stored API keys for external service invocations.

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Detect, validate, and generate Schema.org structured data. JSON-LD format preferred. Use when user says "schema", "structured data", "rich results", "JSON-LD", or "markup".

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When the user wants to add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data," "JSON-LD," "rich snippets," "schema.org," "FAQ schema," "product schema," "review schema," "breadcrumb schema," "Google rich results," "knowledge panel," "star ratings in search," or "add structured data." Use this whenever someone wants their pages to show enhanced results in Google. For broader SEO issues, see seo-audit. For AI search optimization, see ai-seo.

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Generates a fully structured SEO content brief for a target keyword and optionally pushes it to a Notion database. Use this skill whenever the user says 'create a content brief', 'brief this keyword', 'run a content brief for', 'generate a brief', 'write a brief for [keyword]', 'content brief on [topic]', or any variation where someone needs a keyword researched and turned into a structured writing assignment with H1, H2 outline, FAQ, internal links, word count target, and writer notes. Also triggers when the user provides a keyword and asks for an SEO brief, editorial brief, or writing spec. Outputs a mandatory structured format that the text parser maps directly to Notion properties. Supports single-keyword and batch (CSV) modes.

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Repurpose blog posts for social media, email, YouTube, Reddit, and LinkedIn. Generates Twitter/X threads, LinkedIn articles, YouTube scripts, Reddit discussion posts, email newsletter excerpts. Adapts tone for each platform. Use when user says "repurpose", "blog repurpose", "share blog", "social media", "twitter thread", "linkedin post", "youtube script", "reddit post".

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CRE Lender/Credit analysis suite - 8 specialist skills for U.S. CRE loan screening and sizing, sponsor and appraisal review, credit memo writing, annual risk rating, covenant and watchlist monitoring, problem loans, and portfolio stress testing.

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Use when the user wants to save knowledge as a file so others don't have to rediscover it — "turn this into a doc", "write this up", "document how X works", "we figured this out and want to capture it", "nobody should have to figure this out again". Covers any request to create or update durable written artifacts: onboarding guides, runbooks, ADRs, API docs, architecture notes, postmortems, changelogs, setup guides. The trigger: user wants knowledge captured in a file for future reference, not just a conversation. Do NOT use when still making decisions (→ give-plan), just asking for explanation without a file (→ ask), or writing code (→ cook).

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Provisions and manages Neo4j Aura instances via CLI (aura-cli v1.7+) or REST API. Use when creating, pausing, resuming, resizing, or deleting AuraDB Free/Professional/Business Critical/VDC instances; downloading credentials; scripting CI/CD pipelines; polling async status; or using the Terraform neo4j-labs/neo4jaura provider. Covers auth setup (client credentials OAuth2), credential lifecycle (download once — never recoverable), instance type selection, region codes, and Python provisioning scripts. Does NOT handle Cypher queries — use neo4j-cypher-skill. Does NOT cover Graph Data Science algorithms — use neo4j-gds-skill or neo4j-aura-graph-analytics-skill. Does NOT cover neo4j-admin/cypher-shell — use neo4j-cli-tools-skill.

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Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.

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Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.

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Designs Amazon A+ Content (formerly Enhanced Brand Content) — the 8 module suite that appears below the standard product carousel for Brand Registered sellers. Covers Hero Banner (21:9 / 2388×1024), Pain Points / Selling Points / Technology / Data / How-to-Use / Variants (3:2 / 1536×1024), and Endorsement (21:9), with mobile safe-area rule, 30pt text floor, and cross-module consistency anchored to the main image URL. Use when the user says "A+ page", "A+ content", "Amazon A+", "Brand Content", "Enhanced Brand Content", "亚马逊详情页", "A+ 详情页", "21:9 banner", "Hero Banner", "Amazon detail page module", or any request for the brand-registered detail-page module suite. For the 1:1 main image and carousel secondary images, see the `product-shots-main-image` skill.

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Simplifies recently changed or explicitly scoped code while preserving observable behavior, error semantics, side effects, ordering, and public contracts. Use for readability cleanup, dead-code removal, reducing nesting, eliminating redundant wrappers, or making an implementation easier to maintain. Not for architecture redesign, new behavior, speculative cleanup, or a refactor without an adequate safety net.

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Track Serenity / @aleabitoreddit on X and turn the feed into (1) a beginner-friendly Chinese iMessage digest with first-principles + Buffett-style judgement, (2) cumulative 60-day ticker mention analytics CSVs for a website, (3) a Xiaohongshu writing brief, and (4) a durable private research map. Trigger on requests like "follow aleabitoreddit / AleaBito / Serenity", "daily Chinese updates from that X account", "60-day mention analytics", "ticker mention count", "写小红书 aleabito", "aleabito 研究地图 / research map", or any request for Chinese commentary derived from @aleabitoreddit posts.

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Web search, online retrieval, real-time information query, search engine search, Reddit and other community platform discussions, external site posts and trending topics. Trigger when users need to search for the latest information online, look up recent news or trends, query real-time data, research topics via search engines, browse Reddit or community discussions, discover external site posts and trending topics, obtain any online content, web search, online lookup, real-time information, news search, Reddit, trending topics, search engine. Even if the user does not explicitly say "search", trigger this skill whenever their intent involves obtaining real-time web information or verifying current facts.

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Query patent legal status information from the Nexscope (Nexscope) database. Triggered when users mention patent legal status, patent validity check, patent status query, patent event history, simple legal status, legal events such as transfer, license, pledge, opposition, litigation, re-examination, patent legal status, patent validity, patent events, transfer/license/pledge, Nexscope, or patent status lookup. Also triggered when users ask whether a patent is active, inactive, pending, expired, granted, withdrawn, or revoked, or want to look up legal status by patent ID or publication number.

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。

Query patent simple bibliographic (catalog) data from the Nexscope patent database. Triggered when users mention patent bibliographic information queries, patent basic info retrieval, patent catalog data, patent publication details, searching by patent number for inventors, patent applicant information, patent abstract retrieval, patent classification codes (IPC/CPC), patent citation queries, or any request to search for structured metadata by patent ID or publication number, patent brief bibliography, patent basic info, patent number lookup, patent abstract, Nexscope, or patent metadata. Even if the user does not explicitly mention "Nexscope" or "bibliographic info," this skill should be triggered whenever their need involves querying core bibliographic fields of specific patents.

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Retrieve Amazon product details by ASIN, including price, title, main image, listing date, material, weight, variant monthly sales, and up to 12 months of monthly sales history. Triggered when users query Amazon product details, ASIN lookup, product pricing, sales rank history, monthly sales trends, product dimensions, FBA fees, product specifications, batch ASIN query, Nexscope product details, ASIN detail lookup, monthly sales data, pricing info, product specifications, FBA fees, batch ASIN query. Even if users do not explicitly mention "Nexscope", this skill should be triggered whenever the task involves obtaining structured product data for one or more Amazon ASINs.

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。

Modify a lesson plan using SIOP sheltered instruction principles to support language learners across all four skills. Use when planning lessons for classes that include EAL students.

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When the user wants to add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data," "JSON-LD," "rich snippets," "schema.org," "FAQ schema," "product schema," "review schema," "breadcrumb schema," "Google rich results," "knowledge panel," "star ratings in search," or "add structured data." Use this whenever someone wants their pages to show enhanced results in Google. For broader SEO issues, see seo-audit. For AI search optimization, see ai-seo.

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。

Systematic backlink building for indie founders. Covers 5 channel types ranked by GEO + SEO ROI: Wikipedia (entities), authoritative media (PR), industry reviews (G2/Capterra/Product Hunt), Reddit/Quora discussions, and HARO/Featured.com expert quotes. Designed for 0→1 sites where domain authority is the bottleneck. Aligns with 2026 Google/Bing GEO guidance: backlinks are the strongest brand-authority signal AI search engines use to decide citations. Defaults to dev/B2B link sources; for 2C products (education, consumer apps) authority links differ — see gingiris-seo-geo/references/2c-adaptation.md.

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Filter rows of a Datagrok DataFrame via the datagrok_exec tool through the Filters panel — by range, equals/contains/in-set, multi-value, boolean, free-text row expressions, or substructure (SMILES / SMARTS / molblock). Also covers clearing, inverting, the show-only-filtered vs destructive-drop split, and the filter event lifecycle (onRowsFiltering / onFilterChanged / onRowsFiltered). Use whenever the user says "filter", "show only", "hide rows where", "narrow to subset", "find rows that", "contains", "substructure search", "categorical filter", "range filter", "invert", "clear the filter", "clear filters", "drop rows", or asks for the filtered subset as a new table. Does NOT cover selection (separate skill) or generic DataFrame cloning (datagrok-df-and-columns).

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First-order logic with quantifiers, predicates, functions, and identity. Covers syntax, interpretation over a domain, the universal and existential quantifiers, quantifier scope and binding, translation from natural language with multi-variable predicates, natural deduction for quantifiers, undecidability at first order, and the soundness/completeness theorems. Use when propositional logic is insufficient -- whenever "for all" or "there exists" matters to the argument.

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Use when you have captured session evidence — session-retro / session- observatory-live traces in .planning/patterns/, tool logs, correction records — and want to induce a reusable skill from it. Segments the traces into candidate skill units (an LLM judgment, not a deterministic parse) and decomposes each candidate into a four-part structured spec: workflow structure, execution semantics, and runtime attachments (verification, safety, rollback, state). It emits a spec object, NOT a finished SKILL.md, and hands that spec to skill-forge. It sits between skill-integration (upstream frequency detector) and skill-forge (downstream author). Backed by Agent-Trace-to-Skill Induction (arxiv 2606.06893v1). Triggers on inducing a skill from captured traces, turning a repeated pattern into a skill spec, and preparing evidence for skill-forge.

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。