725 件 ・ 関連度順
概要と使いどころ
Use to generate a character image to spec — max-fidelity reproducible prompt from a Canon Spec, anchors-first, provider/governance-gated. Triggers 'generate this character', 'render to spec'.
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Validate Commodore MPS-802 and 1526 serial dot matrix printer behavior in EMULATORS, including SerialPrinterMPS802 classes and their MatrixPrinterEmulation and SerialPrinterPeripheralSimulation hierarchy, IEC/KERNAL-trap printer routing, device numbers, secondary-address functions, PETSCII/character sets, 8x8 glyphs, enhanced width, reverse and paging modes, formatter strings, programmable characters, line/page geometry, PostScript output, and fidelity boundaries. Use when Codex reviews or edits COMMODORE MPS802 printer code, generated printer output, PostScript printer templates, C64/VIC-20/C264 serial-printer behavior, or compares implementation against .codex/docs/MPS802.pdf, the MPS-802 user guide, and 1526/MPS-802/4023 service information.
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。
테스트 없는 기존 코드에 행위 보존 안전망(Characterization → Approval → Mutation)을 구축한 뒤 개선 스킬로 핸드오프. "레거시에 테스트 추가", "안전망 구축", "characterization test" 요청 시 사용. /tdd-legacy로 호출.
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。
Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.
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。
Knowledge base from the GAO Cost Estimating and Assessment Guide (GAO-20-195G). Use for program cost estimating: the four characteristics of a reliable estimate (comprehensive, well documented, accurate, credible) and the 18 best practices; the 12-step cost estimating process (purpose, plan, technical baseline, WBS, ground rules and assumptions, data, point estimate, sensitivity, risk/uncertainty, document, present, update); estimating methods (analogy, parametric, engineering build-up, learning curves); Monte Carlo risk/uncertainty analysis, confidence levels and contingency; auditing and validating an estimate against the characteristics; earned value management (EIA-748, BCWS/BCWP/ACWP, CPI/SPI/TCPI, EAC, PMB, IBR); and specialized techniques (software cost estimating, learning curves, Analysis of Alternatives, WBS templates, the Green Book internal-control framework). This is the cost-estimating companion to the GAO Schedule and Technology Readiness Assessment guides. Does not cover schedule estimating/risk in depth (see the GAO Schedule Assessment Guide), agency-specific cost models, or the full text of referenced standards (EIA-748, MIL-STD-881D, the Green Book) — these are named, not reproduced.
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。
Drafts the instructions field of a declarative agent that orchestrates a set of custom skills, under a character cap (default 8,000), from the skills' names and descriptions: role and scope, one routing rule per skill, workflow order, gates written as workflow holds, global rules, output format and what the agent never does. Returns the text in a fenced block with its character count labelled counted or estimated, plus a skill inventory, routing table, gate table, rules trace and open questions for the owner. Use when the user asks to "write the instructions for my agent", "draft a system prompt that ties these skills together", "tighten these agent instructions to fit the cap", "how should the agent route between these skills" or "revise my agent's orchestration text". Do not use to review a single SKILL.md, use skill-file-reviewer instead. Drafts for human review; never approves, authorises or signs off.
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Build an Anima LoRA from a single-character or style name, from Danbooru collection through curation, section-ordered WD14 captions, dataset checks, confirmed training, fixed-seed checkpoint validation, and a staged Civitai draft. Use for requests such as make a LoRA of X, collect images and train a character LoRA, train and upload to Civitai, or build a dataset from scratch and publish. Delegate training to lora-trainer and structural checks to dataset-doctor; never train without confirmation and never auto-publish.
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 a consented continuity system for an authorized person, fictional character, product, or world across video shots.
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。
Japanese brand design guidelines with traditional color palettes (伝統色), typography (ゴシック体/明朝体), ma (間) spacing, wabi-sabi (侘寂) aesthetics, and logo design for Japanese characters. Triggers on requests for 和風デザイン, ブランドデザイン, 伝統色, 日本語タイポグラフィ, ロゴデザイン, 配色ガイド.
Generate 3D models, textures, images, rig characters, and animate them using the Meshy AI API. Handles API key detection, setup, and all generation workflows via direct HTTP calls. Use when the user asks to create 3D models, convert text/images to 3D, texture models, rig or animate characters, or interact with the Meshy API.
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Generate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API. Handles API key detection, task creation, polling, downloading, and full 3D print pipeline with slicer integration. Use when the user asks to create 3D models, convert text/images to 3D, texture models, rig or animate characters, 3D print a model, or interact with the Meshy API.
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。
OpenAI API (developers.openai.com) の画像・動画生成リファレンス。 image generation（gpt-image, Images API `images.generate` / `images.edit`）、 image edit（mask, reference images）、image generation tool（Responses API 組み込み）、 vision（画像入力, image tokens, vision fine-tuning）、 video generation（Sora, `sora-2` / `sora-2-pro`, videos エンドポイント）、 extend / edits, characters。
Extract one EcoSIM PFT parameter record from a PFT-parameter NetCDF file into a standalone NetCDF. Use when Codex needs to isolate all parameters for a given EcoSIM six-character PFT code such as ndlf34, or a four-character pfts_short value plus Koppen climate code, while preserving all non-PFT-specific variables such as pfts_short, pfts_long, and Koppen metadata.
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This skill should be used when the user wants to "test a claim against data", "validate an empirical hypothesis", "refine a model/theory with evidence", "characterize a pattern and verify it", "演繹と帰納で検証", "仮説をデータで検証して更新", "実例と反例で確かめる", "弁証法ループ", "主張を現物で裏取り", "傾向分析を検証して精緻化", or mentions iteratively updating a hypothesis by deriving predictions and testing them against a real corpus. NOTE: Use this for VALIDATING/REFINING an empirical claim or model against real data through a derive→test→update loop. Use strong-inference for debugging an UNKNOWN cause, and devils-advocate for adversarially stress-testing a DESIGN proposal.
Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics overlays, real-time terminal rendering. Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII 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。
Requires OFOX_API_KEY — create one at https://app.ofox.ai. Generate a realistic-human, dialogue-driven short-drama clip — one shot, or a few hard-cut shots inside one job — from a script or scene description using the Ofox video API (Seedance 2.5). Runs a short creative brief when the input leaves beat, aspect ratio, emotional arc or camera register open (one held take, a travelling take, or a multi-shot cut list) ("Let the AI decide" is offered on the taste questions, never on a must-ask one, and never as the default), writes a structured prompt (header manifest, timestamped shots, quoted dialogue with delivery notes, consistency lock), shows a cost estimate, then calls ofox-video-core to submit, poll, download, and report the real cost. Use when a user asks to turn a script beat into video, e.g. "generate scene 3 of this script, two characters talking, 15 seconds", "make a vertical short-drama clip of these two arguing in a kitchen", "turn this dialogue into a 12-second video", or "give me a realistic short-drama shot of a couple breaking up at a train station". Do not use for silent product/brand shots (see seedance-ad-creative), for anime- or manga-styled scenes (see seedance-anime-drama), for one person addressing the viewer rather than another character — a script read from a supplied portrait is talking-head, an idea pulled out of an article is explainer — or for anything not involving people/dialogue.
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 in script-to-video production pipelines for Apple Silicon Macs. Specializes in hybrid local/cloud workflows, LoRA training for character consistency, motion graphics generation, and artist commissioning. Activate on 'AI video production', 'script to video', 'video generation pipeline', 'character consistency', 'LoRA training', 'cloud GPU', 'motion graphics', 'Wan I2V', 'InVideo alternative'. NOT for real-time video editing, video compositing (use DaVinci/Premiere), audio production, or 3D modeling (use Blender/Maya).
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。
写真や画像をNES、Game Boyなどの配色でレトロなドット絵に変換します。雨や雪、星のきらめきを重ねた短いMP4やGIFの作成にも対応するスキル。
音声ファイルの周波数、音量、テンポなどの特徴を画像にするスキル。複数の図を一枚に並べたり、指定区間だけを切り出して、音声の比較や合成処理の確認に使えます。
Caveman in Classical Chinese: 文言文 register, far fewer characters, technical terms verbatim. Invoke only with /megacave or /caveman wenyan. Stays on until "stop caveman" or "normal mode".
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Create, repair, validate, preview, and package Codex-compatible animated pet spritesheets from character art, screenshots, generated images, or visual references. Use when a user wants to hatch a Codex pet, create a custom animated pet, or build a built-in pet asset with an 8x9 atlas, transparent unused cells, row-by-row animation prompts, QA contact sheets, preview videos, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.
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。
Train custom AI models (LoRA) on fal.ai for personalized image generation tailored to a brand, character, or style.
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Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided 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。
Use when reviewing templates, rendered HTML, or shared components related to Declare UTF-8 character encoding. Validate the final browser-facing markup, not just the source framework abstraction.
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。