「rce」の検索結果

NestJS decorator usage: built-in route/param/class decorators, custom decorators via `createParamDecorator` and `SetMetadata`, metadata reflection via `Reflector`. Apply when designing controllers, guards, interceptors, or custom decorators. Use this skill to: - Pick the right built-in decorator for routes, params, and DI. - Compose decorators (`@UseGuards(A, B) @UseInterceptors(C)`). - Build custom param decorators (e.g., `@CurrentUser()`). - Use metadata for role-based logic (Reflector + SetMetadata). - Avoid common decorator mistakes. Do NOT use this skill for: - General module/DI patterns (see nest-conventions). - GraphQL-specific decorators (see nest-advanced). - ORM entity decorators (see nest-data-layer).

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Convert SVG files to high-quality PNG images with precise control over dimensions, background color, and CSS rendering (including mix-blend-mode, filters, gradients). Use this skill when the user wants to: (1) convert SVG to PNG, (2) generate app icons or splash screens from SVG source, (3) batch-generate multiple PNG sizes from a single SVG, (4) render SVG with advanced CSS features like mix-blend-mode or filters to PNG. Triggers: "SVG to PNG", "SVG変換", "PNG変換", "アイコン生成", "スプラッシュ生成", "app icon from SVG", "favicon generation", "splash screen generation".

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Use when an exact symbol, path, entrypoint, or line range can bound a focused code question or patch proposal under a fixed source budget. Not for source changes or broad repository exploration.

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。

Integrate one exact-head Proto UI pull request through either the ordinary independent-review path or a separately explicit current-user connected-operation decision. Preserve source, DCO, live platform enforcement and all deferred criteria; never manufacture approval or acceptance.

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Fix silent failures when parsing Grafema semantic IDs that are in URI format instead of legacy arrow format. Use when: (1) code splits semantic IDs by "->" but gets the whole string back because IDs are grafema:// URIs, (2) file path extraction from semantic IDs returns empty or wrong values, (3) derived edges (DEPENDS_ON, etc.) produce 0 results despite source edges existing, (4) any code that processes semantic IDs after the analysis pipeline's to_uri_format() has run. The grafema:// URI format uses # fragments with percent-encoded characters instead of -> separators.

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。

Force `rust-embed` to re-embed assets from `$GRAFEMA_UI_DIST` (or any env-driven path) when cargo's incremental compilation skips the macro re-expansion. Use when: (1) you rebuilt the GUI bundle but the rust-server binary still serves the OLD bundle, (2) `cargo build --release` finishes in seconds (incremental hit) and `strings target/release/binary | grep <new-hash>` finds nothing, (3) any rust-embed pipeline where assets live outside the cargo source tree and are picked up via env var or symlink. Cargo only re-runs the proc macro when the rust file declaring `#[derive(RustEmbed)]` changes — env var changes alone don't trigger it.

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Use when the user needs to search or navigate code with Sourcegraph MCP tools. Provides disciplined search workflows for finding implementations, understanding systems, debugging issues, fixing bugs, and reviewing code.

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Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore - same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.

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Use when the user wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.

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。

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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 the NowStack Mobile web app to production on Vercel with the Convex backend shipped alongside it. Use when the user says "deploy my app", "deploy the web app", "ship to production", "go live", or needs CONVEX_DEPLOY_KEY / the Vercel build wired so the backend never drifts behind the frontend.

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Find realistic link prospects with SERP and backlink evidence, discover contact paths via web tools, and draft personalized outreach — with targets saved to .seo/backlink-targets.json for off-page-seo campaigns. Use this skill when the user has a linkable asset (study, tool, guide, template, product page) and wants backlinks, resource-page inclusions, or comparison mentions. Triggers: "link prospecting", "find backlink opportunities", "resource page outreach", "get backlinks for", "link building outreach".

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Create high-converting free resources that capture emails and build trust. Produces complete lead magnets (ebooks, checklists, templates, toolkits, quizzes) with landing page copy, thank-you page, and follow-up email sequence. Use when someone needs a list-building asset, wants to grow their email list, needs an opt-in incentive, a content upgrade, a gated download, or top-of-funnel content. Triggers on 'lead magnet', 'ebook', 'checklist', 'template', 'free resource', 'opt-in', 'grow my list', 'email capture', 'content upgrade', 'list building', 'gated content', or 'free download'. Even if someone just says 'I need something to capture emails' or 'how do I get more subscribers', this is the skill. Every lead magnet passes a 4-gate quality test before shipping.

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。

Rate limiting for serverless and edge apps with the @upstash/ratelimit TypeScript/JavaScript SDK backed by Upstash Redis. Use when adding a rate limiter or throttling to an API route, Next.js middleware, Vercel Edge, Cloudflare Workers, or any HTTP endpoint; returning 429 Too Many Requests; choosing between fixed window, sliding window, and token bucket algorithms; limiting per user, IP, API key, or tenant with prefixes and custom keys; protecting login, signup, form, or AI endpoints from abuse, bots, and brute force; using deny lists, ephemeral caching, analytics, timeouts, and multi-region rate limits; or estimating the Redis command cost of rate limiting. Also use when the user says rate limit, rate-limiting, throttle, quota, request limits, or traffic protection.

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。

Git-native issue tracking (beads), coding guidelines, knowledge injection, and spec-driven planning for AI agents. Drop-in replacement for bd/Beads with simpler architecture. Use for: tracking issues/beads with dependencies, creating bugs/features/tasks, planning specs, implementing features from specs, code reviews, committing code, creating PRs, loading coding guidelines (TypeScript, Python, TDD, golden testing, Convex, monorepo patterns), code cleanup, research briefs, architecture docs, agent handoffs, and checking out third-party library source code. Invoke when user mentions: tbd, beads, bd, shortcuts, issues, bugs, tasks, features, epics, todo, tracking, specs, planning, implementation, validation, guidelines, templates, commit, PR, pull request, code review, testing, TDD, test-driven, golden testing, snapshot testing, TypeScript, Python, Convex, monorepo, cleanup, dead code, refactor, handoff, research, architecture, labels, search, checkout library, source code review, or any workflow shortcut.

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。

Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.

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。

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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 legacy prompts or older framework flows reference `vercel-react-expert` and you need the modern runtime to resolve that capability cleanly.

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 the user wants an agent to create or update a project-local source/paper note from a PDF, URL, arXiv, DOI, manual source, or Zotero item.

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 you need to know where a piece of reporting actually came from — the user gives an article URL and asks "where does this come from?", "who is the original source?", "is this just a rewrite?", or another skill needs the provenance chain before grading or citing a report. Follows an article's links and named attributions back to the originating vendor, government, victim or actor document and returns the chain, the primary's stated access and confidence language, publication dates, and every hop it could not resolve. Deterministic — a script or the Liberty91 platform resolves the chain; the model does not reconstruct it from memory. Invoked by /quality-of-information-check, /threat-actor-profiling, /campaign-tracking and /intelligence-writing. Works on any URL with no API key.

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 as the default entry point for any CTI request that doesn't name a specific skill. Activates when a user asks to investigate an indicator, profile a threat actor, write an assessment, enrich IOCs, or build detection rules, and when a user asks where a report or article comes from, how reliable it is, to grade a source, check a report, or split a report into claims, or pastes a bare article URL. Routes to the right investigation, provenance, grading or analysis skill, then auto-applies rigor skills (source rating, TLP, confidence, likelihood) on the output.

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 report or article needs to be split into individual claims before it is graded, cited or used as evidence — the user asks "what does this report actually claim?", "split this into claims", "separate the facts from the judgements", or another skill needs a claim list. Turns one document into five to twelve atomic claims, each typed (observation, attribution, assessment, actor claim, victim disclosure) and anchored to the verbatim sentence it rests on, so every extraction can be checked against the source. Preserves the source's own hedging. Does not grade. Invoked by /quality-of-information-check; its output feeds /ach, /key-assumptions-check, /intelligence-writing, /writing-assessments and /threat-actor-profiling.

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 you need Liberty91 platform intelligence — what actually happened (deduplicated Threat Events with every source, Admiralty reliability/credibility and verification stage), whether an IOC is already known to your account, the canonical threat library (actors, malware, vulnerabilities, clusters, ATT&CK TTPs), alert matches, or which of your customer organizations an occurrence affects — or when you need to push intelligence back: ingesting your own report, generating an intelligence package, or uploading an organization document. Also supplies event provenance fields, when the platform returns them, and Threat Library alias resolution to /source-provenance and /quality-of-information-check. Two-way, first-party integration. Commonly invoked by /ip-investigation and friends, /ioc-enrichment-workflow, /threat-actor-profiling, /campaign-tracking and /vulnerability-intelligence. Reads $LIBERTY91_API_KEY.

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。