「construction」の検索結果

Build a five-level rubric with coherent logic for a learning target within a developmental band. Use for Manning methodology programmes where Competent = success. For general curriculum rubrics, use criterion-referenced-rubric-generator 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。

Expert Godot shader patterns for batch-safe hitflash, alpha-scissor foliage/dissolve, screenspace postFX, depth reconstruction, triplanar, and instance uniforms — not first-shader tutorials. Trigger on draw-call batching breaks, discard vs depth-prepass, foliage shadows, post-process quads, or world-position FX. Keywords: instance uniform, ALPHA_SCISSOR, hint_screen_texture, hint_depth_texture, global uniform, sampler2DArray, canvas_item, spatial, post-processing.

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 Ideogram 4 (Ideogram Ultra) txt2img and img2img workflows with the local open-weights model, dual conditional/unconditional models with DualModelGuider, Qwen3-VL text encoder, and structured JSON ("compositional deconstruction") prompts for strong text rendering and layout control

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。

3D chart visualization with Swift Charts using Chart3D, SurfacePlot, interactive pose control, and surface styling — plus a 2D Swift Charts construction reference (marks, axes, selection, SectorMark, scrollable charts). Use when creating data visualizations with Swift Charts.

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Analyze the photograph uploaded by the user, deconstruct its visual evidence, abstract and distill the essential relationships, then reconstruct them as a new nonliteral lower-panel composition guided by 19 bundled references. Use for CLEAN upper-photo/lower-abstraction editorial artworks with a number, date, and short phrase in the lower panel. This is abstraction and reconstruction, never style conversion or a miniature redraw. Preserve the exact user-uploaded photograph unchanged above.

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Use this skill AUTOMATICALLY before writing any Higgsfield prompt. Query the memory databases for relevant past failures and pre-apply known fixes before the user even hits generate. Triggers include: any request to write a Higgsfield prompt, any use of the higgsfield-prompt skill, any mention of generating a video or image on Higgsfield, any MCSLA prompt construction. This skill should run SILENTLY in the background — don't announce it, just apply what's known. If the databases are empty, skip silently and proceed with normal prompt generation.

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Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".

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。

Blameless post-mortem structure, incident timeline reconstruction, response evaluation, and organizational learning

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。

Enforce "No Exploit, No Report" policy with PoC construction standards, false-positive filtering, and evidence collection per vulnerability class across backend, frontend, and mobile. Use when validating security findings, constructing exploit proofs, filtering false positives, or writing pentest findings.

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。

OWASP LLM Top 10 (2025) audit checklist for AI applications, agent tools, RAG pipelines, and prompt construction. Use when performing any security review touching LLM client code, prompt templates, agent tools, or vector stores.

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Researches archives, contemporary accounts, and timeline reconstruction. Use when the album subject involves historical events that need primary source verification.

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 multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.

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Detects duplicate boilerplate, copy-paste tests, and structural maintainability issues across .NET test suites. Use when the user asks to reduce repetition, consolidate similar test methods, convert copy-paste tests to data-driven parameterized tests, suggest a better test structure, or identify refactoring opportunities. Identifies repeated construction, assertion patterns, copy-paste methods convertible to DataRow/Theory/TestCase, redundant setup/teardown, and shared infrastructure. Produces an analysis report with concrete before/after suggestions. Works with MSTest, xUnit, NUnit, and TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), reviewing test quality or anti-patterns (use test-anti-patterns), or deep mock auditing (use exp-mock-usage-analysis).

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 AI-enabled .NET applications with Semantic Kernel using services, plugins, prompts, and function-calling patterns that remain testable and maintainable. USE FOR: adding AI-driven prompts, plugins, or orchestration to a .NET app; reviewing kernel construction, service registration, or plugin usage; building function-calling. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。

UUID generation skill - Universally Unique Identifiers v4 and v7 for entity IDs. 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。

@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。

@delon/chart skill - G2Plot enterprise charting components with @delon/chart. 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。

@delon/acl skill - Access Control List for role-based permissions and UI element visibility. 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。

Turn an object or character reference image into a quality-gated, animation-ready procedural Three.js model built in code. Use for image-to-3D reconstruction, detail-accurate object rebuilds, stylized/likeness-maximized human characters, sculpt specs, and staged code generation.

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。

Solana transaction construction including instruction building, account resolution, compute budget, priority fees, and versioned transactions

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。

Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features

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。

Rust-backed Python library for fast genomic token arithmetic and BED processing. High-performance BED I/O, interval set ops (intersect, merge, complement, subtract), region tokenization against a universe, universe construction. Use for preprocessing large BED collections and ML token vocabularies.

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 strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier.

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 best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.

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。