「nct」の検索結果

Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.

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Detects and safely removes unused code (imports, functions, classes) across multiple languages. Use after refactoring, when removing features, or before production deployment. Includes safety checks and validation.

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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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。

@delon/util skill - Utility functions library for array, string, date, number manipulation. For ng-events construction site progress tracking system.

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。

Implement fullscreen functionality using screenfull library for cross-browser fullscreen support in ng-events project

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。

PROACTIVELY search auto-generated documentation when receiving ANY user instruction. Search for function signatures, API documentation, class definitions, and code comments BEFORE implementing anything. Your codebase documentation is valuable - use it first.

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 Angular 20 standalone components using modern patterns: Signals for state management, input()/output() functions (not decorators), @if/@for/@switch control flow (not *ngIf/*ngFor), inject() dependency injection (not constructor), and OnPush change detection. Use this skill when scaffolding new UI components that need reactive state, form handling, or integration with services following the three-layer architecture.

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Quick reference for Portfolio Buddy 2 project structure. Use when: adding new features, modifying existing components, understanding data flow, or onboarding to the codebase. Contains component hierarchy, hook patterns, and utility functions.

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Use when you need to run a binary, trace execution, or observe runtime behavior. Runtime analysis via QEMU emulation, GDB debugging, and Frida hooking - syscall tracing (strace), breakpoints, memory inspection, function interception. Keywords - "run binary", "execute", "debug", "trace syscalls", "set breakpoint", "qemu", "gdb", "frida", "strace", "watch memory"

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This skill should be used when troubleshooting Firebase emulator issues, rules violations, function errors, auth problems, or deployment failures. Triggers on "error", "not working", "debug", "troubleshoot", "failing", "broken", "permission denied", "emulator issue".

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This skill should be used when adding features to existing Firebase projects. Triggers on "add function", "create endpoint", "new tool", "add api", "new collection", "implement", "build feature". Guides TDD workflow with test-first development, security rules, and emulator verification.

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Create high-converting, visually distinctive landing pages. Use when building marketing pages, product launches, SaaS homepages, or any single-page conversion-focused website. Guides section-by-section composition with anti-AI-slop principles.

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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

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。

Fetches Zig language and standard library documentation via CLI. Activates when needing Zig API details, std lib function signatures, or language reference content that isn't covered in zig-best-practices.

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Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

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。

Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.

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 distinctive Photo Relic editorial artworks from user-provided photographs: preserve the real photo and pair it with a recognizable, artful abstract relic shaped like memory, modern printmaking, quiet Eastern restraint, and source-derived light. Use when the user asks to turn a photo into minimal art, photographic relic posters, abstract editorial photography, gallery-like photo posters, Douyin-ready art series covers, graphic photo abstraction, afterimage compositions, or similar visual treatments while keeping the original photograph truthful.

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。

Decision rubric for promoting a prose prompt into a typed DSPy Signature. This is an ENHANCE overlay on top of the [[dspy]] library skill: it does NOT teach DSPy syntax — it answers the coder-agent decision "when do I stop hand-writing a prompt string and declare it as a `dspy.Signature`, and how do I name/describe its fields so the optimizer and the calling code both get a clean contract." Activate when: a prompt string grows past ~50 lines; the LM output is consumed by code (parsed, branched on, stored) rather than read by a human; the same prompt is reused across >1 call site; or a teammate asks "should this be a Signature?". Do NOT activate for one-shot throwaway prompts, or for HOW-TO questions about DSPy modules /optimizers/compile — defer those to the [[dspy]] skill and the [[agentsop-dspy]] workflow skill. Search keywords: typed prompt, structured prompt, DSPy Signature, prompt as a function, prompt contract, when to formalize a prompt.

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。

Decision protocol for wrapping a REST / GraphQL / RPC API as a tool an LLM agent can call. The load-bearing premise: the *tool surface* is an LM-friendly subset of the *API surface* — one tool per user intent, not one per endpoint. Activates when a coder agent must expose an external HTTP API to a model (function calling, tool_use, MCP, LangChain `@tool`, CrewAI `BaseTool`). Encodes the *what to surface, how to name, how to shape, how to fail* — not any single framework's API. ~80% of agent tools in production are HTTP wrappers; this is the SOP for getting them right.

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。

Enhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations, rare jargon — that pure dense embedding silently misses. Encodes the single decision rule (**hybrid is traffic-driven, not theoretical: add sparse only when the query share that depends on exact tokens is non-trivial**), the wiring of QueryFusionRetriever-style fusion (RRF vs alpha-weighted), and per-query-type alpha tuning. Frame the work as recovering lexical identity that dense pooling destroys, not as "add keyword search for completeness". Cross-links [[llamaindex]].

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。

Symbol-level code context for LLM coder-agents: tree-sitter extracts symbols, PageRank ranks them over the cross-file reference graph, and the top class/function signatures are fed to the LLM as a token-budgeted read-only map (not RAG, no vector index, human-auditable). Use when an agent must locate the right files in a large/multi-file repo, when builds/refreshes/scopes a repo-map, or when "model edits the wrong file" needs fixing.

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。

Decomposed, multi-criteria metric design for LLM pipelines. The metric IS the model — change the metric and the optimizer changes behavior. Decompose by default; bool during compile, float during eval; calibrate against human; mitigate judge bias. Search keywords: LLM-as-judge, llm as judge, eval metric, evaluation score, scoring function, rubric, RAGAS, G-Eval, judge bias, verbosity bias, how to evaluate LLM output.

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Cross-engine decision rubric for self-hosting or recommending an LLM serving stack. Picks among vLLM, SGLang, TensorRT-LLM, TGI, llama.cpp, Ollama, and MLX as a function of (hardware × workload × constraint), not "which is fastest". Activates whenever a coder-agent must choose, defend, or migrate a serving runtime.

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Build and govern a 50-200 example domain-specific held-out benchmark sampled from real traffic. Distinct from public benchmarks (MMLU/HumanEval/GSM8K via lm-evaluation-harness) which measure GENERAL capability. Only a held-out domain set predicts whether THIS system works on YOUR data. Collect real examples, label, hold out (never train/prompt on it), size 50-200, version it, refresh on drift.

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。