「data write」の検索結果

Browser QA of a PR against a running Twenty app, post-merge on main or pre-merge via the qa-scout label. Derives user-visible scenarios from the PR diff, executes them with the Playwright MCP browser, attests database effects over SQL, watches server and worker logs for swallowed errors, and writes a structured verdict plus a report. Invoked by ci-e2e-main.yaml after the deterministic e2e suite; also runnable locally against a dev stack.

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 audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," "indexing issues," "stuck on page 2," "striking distance keywords," "title tag rewrite," "low CTR," "local SEO," "Google Business Profile," or "map pack." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. 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。

Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.

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。

Converts PDF (.pdf) documents into Markdown so their contents can be accurately analyzed, summarized, searched, or extracted from. Use this skill whenever the user shares, references, or asks about a .pdf file — even if they don't say "convert" or "markdown" explicitly. This includes requests to "read", "summarize", "review", "extract data from", "compare", or "analyze" a PDF report, paper, invoice, form, contract, or scanned document. Always run the bundled conversion script to produce Markdown first; do not attempt to parse PDF content directly or write ad-hoc extraction code. Also use this skill for batch requests involving a whole folder of PDF documents. IMPORTANT: When the user references a folder or set of documents containing multiple file types (.pdf, .docx, .xlsx), invoke ALL three sibling skills — convert-pdf-to-md, convert-word-to-md, and convert-excel-to-md — so no file type is silently skipped.

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。

Converts Word (.docx) documents into Markdown so their contents can be accurately analyzed, summarized, searched, or extracted from. Use this skill whenever the user shares, references, or asks about a .docx file — even if they don't say "convert" or "markdown" explicitly. This includes requests to "read", "summarize", "review", "extract data from", "compare", or "analyze" a Word document, resume, report, contract, or proposal. Always run the bundled conversion script to produce Markdown first; do not attempt to parse .docx content directly or write ad-hoc conversion code. Also use this skill for batch requests involving a whole folder of Word documents. IMPORTANT: When the user references a folder or set of documents containing multiple file types (.pdf, .docx, .xlsx), invoke ALL three sibling skills — convert-pdf-to-md, convert-word-to-md, and convert-excel-to-md — so no file type is silently skipped.

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。

Work with OpenViking, the persistent context database behind this agent's memory. Use it whenever the user refers to earlier sessions or shared history ("like last time", "what did we decide"), asks to remember or forget something, shares files, URLs, or repos worth keeping, or when the task needs context this session does not have — even if nobody says the word "memory". Also use it when the user asks where memories are stored: per project, per folder, or shared between repositories. Covers choosing between context search, find, list search, and grep, reading viking:// URIs, and when (not) to write.

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。

Apply the SGLang kernels RFC when adding, moving, splitting, or reviewing kernel APIs, registry metadata, kernel tests, benchmarks, and model-specific implementations. Use with add-jit-kernel, add-sgl-kernel, and write-sglang-test for placement and migration checks.

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。

Deploy and configure Zeek (formerly Bro) to passively analyze network traffic, generate structured connection/DNS/HTTP/SSL/file logs, detect anomalous behavior, and write custom scripts for organization-specific threats. Use for passive monitoring at network choke points, feeding SIEM/threat hunting with protocol metadata, or retrospective log analysis during incident response; not a substitute for inline IDS/IPS or host 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。

Persistent SEO workflow for Claude Code — run a technical SEO audit, build a hub-and-spoke keyword strategy, write page-type-aware content briefs, and draft SEO-optimized articles, persisting every artifact to a .seoagent/ workspace so the work compounds across sessions. Use when auditing a site's technical SEO, planning a keyword/content strategy, writing content briefs, drafting SEO articles with structured data, or resuming SEO work started in a previous session.

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。

Chat with a companion that knows your notes folder (a journal, an Obsidian vault, any .md or .txt files) and every earlier chat, using cognee memory with local databases. Use when someone wants to ask questions about their own notes, or to talk with a companion that remembers past conversations. Runs locally; reads the notes folder, writes only to cognee's memory.

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。

Deep-research a topic end to end and produce a permanent, reusable knowledge asset: archive every primary source verbatim (gated by the user's privacy/retention posture), write one 1:1 summary per source, then synthesize a single self-contained compendium page. Depth is a dial (base synthesis → grounded primaries → books + counter-canon → saturation), each level an idempotent superset of the one below. Distinct from data-research (structured trackers) and perplexity-research (web deltas): this produces prose knowledge synthesis backed by an archived source corpus.

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。

Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool 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。

Mounts Cloud Storage buckets on Google Cloud as a POSIX file system using Cloud Storage FUSE (and the gcsfuse CLI tool). Use when interacting with the gcsfuse CLI or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre; use gke-storage).

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。

Cross-platform operations runbook for the Slack control-pane agent. Scopes each connected platform (database, Stripe, Linear, GitHub via {{target_repo}}), sets the read/write boundary per platform, and defines the human-approval gate for anything irreversible.

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 building Python 3.11+ applications requiring type safety, async programming, or robust error handling. Generates type-annotated Python code, configures mypy in strict mode, writes pytest test suites with fixtures and mocking, and validates code with black and ruff. Invoke for type hints, async/await patterns, dataclasses, dependency injection, logging configuration, and structured error handling.

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 designing new high-level system architecture, reviewing existing designs, or making architectural decisions. Invoke to create architecture diagrams, write Architecture Decision Records (ADRs), evaluate technology trade-offs, design component interactions, and plan for scalability. Use for system design, architecture review, microservices structuring, ADR authoring, scalability planning, and infrastructure pattern selection — distinct from code-level design patterns or database-only design tasks.

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。

Generate a Privacy Impact Assessment in house format for a new feature, product, or processing activity, using the structure learned from your seed PIA. Use when the user says "write a PIA", "privacy impact assessment for", "do we need a PIA for this", "privacy review this feature", or describes a new data processing activity.

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。

Draft or update the privileged investigation memo from the investigation log. Use when an investigation is far enough along to write the first memo cut, or when new data has been added and the existing draft needs updating.

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, convert, read and edit Office documents locally with GenOffice's command line. Build a new presentation (pptx) from a brief through a checked outline, per-page spec and render pipeline, a new spreadsheet (xlsx) from CSV or JSON data with formulas, a new Word document (docx) from Markdown or HTML, or a PDF; convert between pdf, docx, xlsx, pptx, md, html and csv; read the structure and text of an existing file (including the user's own docx / xlsx / pptx as source material) and apply structured edits to it. Use whenever the user asks for a slide deck, presentation, spreadsheet, workbook, report, Word document or any real Office file, a format conversion, a rewrite of part of an existing document, or wants the result opened in the GenOffice editor. Documents are processed locally; only search, image and media send the query or the referenced file to the provider configured in GenOffice.

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。

Writes, reviews, and debugs property-based tests — Hypothesis, fast-check, proptest, jqwik, rapid, and Echidna or Medusa for Solidity invariants. Use whenever tests should cover a whole input domain instead of a hand-picked list of examples: encode/decode and serialize/deserialize pairs, parsers, canonicalizers and normalizers, validators, numeric and Decimal types, comparators and sort order, data structures, and smart-contract state invariants. Also use when adding cases to an existing @given, fast-check, or proptest suite, when judging whether existing property tests assert anything real, and when a generator has shrunk a counterexample and you need to tell a wrong property from a genuine bug. Not for coverage-guided binary fuzzing (libFuzzer, AFL), mutation-testing campaigns, static analysis, benchmarking, or end-to-end UI 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。

Design, critique, read, write, render, and implement UML and UML-like software diagrams. Use when the user mentions UML, sequence diagrams, class diagrams, activity diagrams, state machines, use case diagrams, component diagrams, deployment diagrams, object diagrams, package diagrams, profile diagrams, timing diagrams, communication diagrams, interaction overview diagrams, composite structure diagrams, ERDs, database schema diagrams, C4, BPMN, swimlanes, flowcharts, network diagrams, application architecture diagrams, software architecture diagrams, diagram-as-code, model-as-code, XMI, UMLDI, PlantUML, Mermaid, Graphviz DOT, D2, Structurizr, DBML, diagrams.net/draw.io, Kroki, or interactive diagram editors and explorers.

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 a question, decision, plan, tradeoff, or claim needs a rigorous verdict and one perspective is not enough. Spawns a panel of 3 to 5 subagent jurors that form independent blind opinions, deliberate anonymously under an anti-anchoring and anti-sycophancy protocol, and return one committed verdict with confidence, preserved dissent, and a concrete next action. Domain-agnostic across engineering, architecture, data, product, hiring, strategy, vendor choice, build-vs-buy, and research design. Trigger phrases include "convene a jury", "have agents debate and decide", "get a panel to decide", "multi-agent decision", "stress-test this and decide", "monte um juri", "tribunal de agentes", "painel para decidir". Do NOT use to only critique without deciding (use the-fool for that), to build a plan or write the solution itself, or for simple factual lookups.

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。

Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool 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。

Review and optimize application data access that uses Entity Framework Core (EF Core). Use for EF Core performance and correctness reviews of services, repositories, imports, and background jobs: slow or memory-heavy queries, excessive round-trips, hidden SaveChanges, premature materialization, dynamic query construction, result-graph tracking, bulk writes, DbContext lifetime or overlapping operations, search semantics, paging, raw SQL, and model/index coverage. Use whether or not EF Core owns the schema. For EF Core, not Dapper or raw ADO.NET.

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。