「search skill」の検索結果

Design search — query input, zero results, refinement, and result presentation. Use when users retrieve rather than browse. For browse structure, use `navigation-patterns`.

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Build a repository that makes findings findable, reusable, and cumulative across teams. Use when the same research keeps getting redone. For synthesising one study, use `affinity-diagram`.

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。

当用户明确要求“迁移 LaTeX 模板”“把旧项目接入 ChineseResearchLaTeX”“把旧标书/论文/毕业论文/简历套进当前模板”“把 Word/PDF/Markdown/零散 tex 整理进现有项目”，或直接提到 `transfer-old-latex-to-new` 时使用。旧别名 `migrating-latex-templates` 可兼容理解。该 skill 只负责把正文内容迁移到当前仓库现有模板的内容层；绝不能修改 `packages/` 内公共包源码、也绝不能修改 `projects/` 内模板样式或入口骨架，只能写入目标项目允许承载正文的内容文件。

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。

当用户明确要求"更新项目指南""同步指南""沉淀洞见到指南"，或要求使用旧名 guide-updater skill 时使用。将对话中新产生的可复用写作洞见实时沉淀到项目指南文件，保持术语口径一致、结构稳定、可检验与可复现。调用时必须指定指南文件路径。

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This skill should be used when code search is needed (whether explicitly requested or as part of completing a task), when indexing the codebase after changes, or when the user asks about ccc, cocoindex-code, or the codebase index. Trigger phrases include 'search the codebase', 'find code related to', 'update the index', 'ccc', 'cocoindex-code'.

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。

Operates Amazon Kinesis Data Streams (KDS). Covers streaming tables and Amazon S3 delivery - serverless, fully managed delivery from a KDS stream to Apache Iceberg tables on S3 Tables or to general-purpose S3 buckets. Includes channel setup, IAM, schemas, output key templates, CloudWatch metrics and alarms, DLQ handling, quotas, and troubleshooting. For all other KDS topics and questions, search AWS documentation and blogs instead. Triggers: Kinesis Data Streams, KDS, streaming tables, stream to S3, stream to Iceberg, stream to S3 Tables, KDS delivery, KDS channel, CreateChannel, data channel, data freshness, dead-letter queue, KDS lakehouse, serverless Kinesis delivery, Firehose alternative for KDS, zero-ops Kinesis to S3. DO NOT USE for Kinesis Data Firehose, Kinesis Video Streams, or Managed Service for Apache Flink — use dedicated skills or search documentation instead.

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。

Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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 fail-closed production readiness review for an Algolia-backed search release. Use when promoting new indices, settings, credentials, UI behavior, or event tracking. Trigger with "Algolia production checklist", "search go-live", or "Algolia launch review".

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。

Plan and verify deployment of an Algolia-backed application with separated browser and server credentials. Use when releasing search code, index configuration, or event instrumentation. Trigger with "deploy Algolia", "Algolia production rollout", or "search release checklist".

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。

Plan and execute a reversible migration from another search system or legacy index design to Algolia. Use when translating schemas, queries, relevance behavior, or production traffic. Trigger with "migrate to Algolia", "Algolia cutover", or "search engine migration".

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 a minimal Algolia JavaScript v5 indexing and search proof using a disposable index. Use when verifying credentials, learning the client boundary, or proving first connectivity. Trigger with "Algolia hello world", "first Algolia search", or "test Algolia setup".

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Design or review an Algolia search architecture across source data, indexing, query, UI, events, and operations. Use when establishing system boundaries or evaluating an existing integration. Trigger with "Algolia architecture", "design search platform", or "review Algolia integration".

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。

Build and review a bounded CI gate for Algolia index configuration and disposable-index smoke tests. Use when search changes need pull-request evidence without mutating production. Trigger with "Algolia CI", "test Algolia in GitHub Actions", or "search deployment gate".

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。

Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).

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Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this 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。

Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

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。

Universal AI-powered web scraper for any platform. Scrape data from Instagram, Facebook, TikTok, YouTube, LinkedIn, X/Twitter, Google Maps, Google Search, Google Trends, Reddit, Airbnb, Yelp, and 15+ more platforms. Use for lead generation, brand monitoring, competitor analysis, influencer discovery, trend research, content analytics, audience analysis, review analysis, SEO intelligence, recruitment, or any data extraction task.

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将deep调研结果汇总为markdown报告，覆盖所有字段，跳过不确定值。

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。

读取调研outline，为每个item启动独立agent进行深度调研。禁用task output。

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向现有调研outline补充字段定义。

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。

向现有调研outline补充items（调研对象）。

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对目标话题进行初步调研，生成调研outline。用于学术调研、benchmark调研、技术选型等场景。

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。

Discuss a domain with the user, research it from real sources, then generate a trusted skill bundle for it - a step-by-step workflow with a flowchart, a domain adapter, a trap fixture, and a smoke eval. Use when the user says "/fable-domain SECTOR", "make a skill for DOMAIN", "add a domain to the fable method", or "give a lesser model Fable's workflow for DOMAIN". The bundle is the deliverable; a workflow without its flowchart, sources, and trap is not done.

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。

When the user wants to localize their App Store listing for international markets. Also use when the user mentions "localization", "translate my app", "international markets", "expand to new countries", "localize metadata", or "which countries should I target". For keyword research in specific markets, see keyword-research. For metadata writing, see metadata-optimization.

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。