Knowledge base from the FAA Human Factors Design Standard (HF-STD-001B, 2016) — the FAA's consolidated human-factors / human-systems-integration design criteria for systems it manages, operates, or maintains. Use for requirement-level design rules on: general human-factors design principles; automation and function allocation; designing equipment for maintenance; displays, controls, and visual indicators; alarms, audio, and voice communications; the computer-human interface (information presentation, coding, interaction styles, windows, dialogue); keyboards, input devices, and workstation/workplace ergonomics; system security, personnel safety, environment, anthropometry, and user documentation; plus intended-use and tailoring guidance. It is a quantified, source-attributed synthesis (heavily citing MIL-STD-1472G, MIL-HDBK-759C, DOD-HFDG-ATCCS, NUREG-0700, NASA-STD-3000, ANSI, OSHA, ISO 9241). Complements nasa-hsi as a parallel HSI tradition from the civil-aviation domain. Scope limits: it is design criteria, NOT an HSI lifecycle/process model (use nasa-hsi, nasa-npr-7123, or dau-se-guidebook for process); it does NOT reproduce the third-party standards it cites; it is FAA-acquisition-oriented (ATC, towers, TRACONs, airway facilities), not a general consumer-UX guide; and it is thin on modern touchscreen/mobile, web, and AI-driven interface patterns and on quantitative human-reliability analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
jgsystemsconsulting/jgs-se-knowledge-packs☆ 82026년 10월 15일 업데이트
Set up and run knowledge distillation (on-policy, off-policy, or multi-teacher) from a teacher model to a student model using the Tinker API. Use when the user wants to distill knowledge, compress models, or train a student from a teacher.
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uiuc-kang-lab/rlvr_generalization_bounds☆ 52026년 5월 13일 업데이트
Set up and run the full RLHF pipeline (SFT, reward model training, RL from reward model) using the Tinker API. Use when the user wants to do RLHF, train a reward model, or run the full preference-based RL pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
uiuc-kang-lab/rlvr_generalization_bounds☆ 52026년 5월 13일 업데이트
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
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huang-sh/DeepScience☆ 42026년 7월 15일 업데이트
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Lord1Egypt/awesome-skill-forge☆ 22026년 6월 10일 업데이트
Represent dependency trees, taxonomies, and other hierarchies inside a Poincare ball, a geometric model for hierarchical distances. Use tree depth for radius and text embeddings for direction, then measure distances to examine the encoded structure.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Analyze subtrees in module dependencies
- Examine structural patterns in class hierarchies
- Organize concepts by taxonomic relationships
ruvnet/ruflo☆ 7.8만2026년 10월 20일 업데이트
Choose a model training method and configuration from paired response preferences or individual positive and negative ratings. Covers DPO, ORPO, KTO, and SimPO, including pair construction, repeated training rounds, and troubleshooting learning settings.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Choose a training method for paired response preferences
- Select a method for individual positive and negative ratings
- Review learning settings when DPO training loses general capability
wshobson/agents☆ 4.1만2026년 10월 20일 업데이트
Keep coding tasks small by checking whether new code is needed, reusing existing helpers, and preferring standard libraries or built-in platform features. Apply the same approach to feature development, bug fixes, refactoring, and reviews.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Adding a feature with minimal code
- Reviewing unnecessary dependencies
- Simplifying an overcomplicated implementation
kortix-ai/suna☆ 2만2026년 10월 20일 업데이트
Review Python, JavaScript/TypeScript, and Go code against security guidance for the languages and frameworks in use. Identify important weaknesses, suggest improvements, and produce prioritized reports with code references, or apply secure defaults when writing new code.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Review existing code for security weaknesses
- Create a security report organized by severity
- Write new code with secure defaults
tech-leads-club/agent-skills☆ 7.1천2026년 10월 17일 업데이트
Analyze git history to map contributors to security-sensitive files and identify ownership gaps. Estimate bus factor, a measure of dependence on few people, group files that change together, and export CSV or JSON for further analysis.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Finding sensitive code with stale, concentrated ownership
- Checking reliance on one person for authentication code
- Comparing CODEOWNERS with contribution history
tech-leads-club/agent-skills☆ 7.1천2026년 10월 17일 업데이트
Configure login state and access rules in Blazor Web Apps. Accounts for server and browser rendering, protects pages by role or policy, and helps diagnose missing authentication state or errors on Identity pages.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Restrict pages by login status, role, or policy
- Show interface elements according to user permissions
- Keep login state available after WebAssembly loads
dotnet/skills☆ 5.7천2026년 10월 20일 업데이트
Set up Ignite UI Lite controls or GridLite in Blazor applications. Match package references, service registration, namespaces, theme stylesheets, and interactive rendering settings to the app’s structure, including projects with separate server and client code.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Adding Ignite UI Lite controls to a Blazor app
- Setting up an app that uses only GridLite
- Reviewing setup in separate server and client projects
dotnet/skills☆ 5.7천2026년 10월 20일 업데이트
Look up current research information using Perplexity's Sonar Pro Search or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent studies, technical documentation, and general research information with citations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
foryourhealth111-pixel/Vibe-Skills☆ 3.7천2026년 10월 20일 업데이트
Use when a model — a full theory paper's argument or the structural/conceptual scaffolding behind an empirical AER: Insights short-format manuscript — needs to be made minimal, so it carries the single insight in a few pages. Keeps only what the insight requires; it does not develop a general framework.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
brycewang-stanford/Awesome-Journal-Skills☆ 1.3천2026년 9월 27일 업데이트
Long-running iterative cycle — one entry, seven modes (debug|improve|planex|review|security|defensive|ui). Shared archaeology/audit → fix → verify → generalize → discover → persist skeleton with mode-specific dimensions. User-invoked campaign entry; single-step fixes route via /maestro-next
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
catlog22/maestro-flow☆ 5662026년 10월 15일 업데이트
Add, configure, or review Ignite UI for Blazor Lite component support in Blazor applications. USE FOR: installing IgniteUI.Blazor.Lite or IgniteUI.Blazor.GridLite, registering AddIgniteUIBlazor() in Blazor Server, WASM, Hybrid, or split Blazor Web App projects, adding @using IgniteUI.Blazor.Controls, wiring the theme stylesheet, picking the right host page, locating the GridLite stylesheet path, explaining single-project vs split Server/Client Web App setup differences, and checking where an interactive render mode is needed for Ignite UI components to work. DO NOT USE FOR: general Blazor component authoring without Ignite UI, choosing app architecture or render mode from scratch (see create-blazor-project), JavaScript interop (see use-js-interop), authentication (see configure-auth), prerendering (see support-prerendering), or layout/component design questions that need no Ignite UI setup.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
managedcode/dotnet-skills☆ 4902026년 10월 20일 업데이트
Review the symbol-notation section of a mathematical-modeling paper against the full paper. Check table coverage, symbol meanings, units, overloading, case and subscript consistency, first-use definitions, formula usage, and visible table formatting. Use for symbol-table diagnosis, not general paper review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
BZDmathclub/bzd-math-modeling-skills☆ 4242026년 9월 19일 업데이트
DL cell/nucleus segmentation for fluorescence and brightfield microscopy. Pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm segment cells without retraining. Outputs label masks for morphology and tracking. Use scikit-image watershed for rule-based; Cellpose when DL generalization across staining is needed.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
jaechang-hits/SciAgent-Skills☆ 3812026년 10월 16일 업데이트
Builds estimates, quotes, and bids for trades and field-service businesses (HVAC, plumbing, electrical, roofing, landscaping, painting, cleaning, remodeling, general contracting, IT/AV installs). Turns a scope into line items with waste factors, burdened labor, materials, equipment, subs, permits, mobilization, contingency, overhead, profit, and tax, then writes a client-ready xlsx (live formulas) and PDF plus an internal cost sheet, with good/better/best options, add-ons, a payment schedule, exclusions, and assumptions. Use whenever someone asks for a quote, estimate, bid, proposal price, or job price, asks "how much should I charge for this job", wants to check a price or margin on a job, or is confused about markup vs margin. Computes deterministically with a script instead of mental math, because models routinely treat a 30% markup as a 30% margin and drop labor burden, waste, and mobilization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
OneWave-AI/claude-skills☆ 3582026년 10월 20일 업데이트
Trigger this skill when building applications with Gemma or for general knowledge inquiries related to Gemma models (e.g. prompt structure, capabilities). Covers model selection, development workflows, and deployment best practices.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
skillmds/skillmd☆ 2492026년 10월 14일 업데이트
Generate 3D assets with Tencent's Hunyuan3D family — open-weight self-hosted models (Hunyuan3D-2.0/2.1 and HunyuanWorld / HY-World for scenes) and the hosted, closed API tiers (2.5, PolyGen, 3.0/3.1 via Tencent Cloud and third-party hosts). Use when a task involves image-to-3D or text-to-3D mesh generation, the shape-then-texture (DiT + Paint / PBR) workflow, choosing between self-hosting and a hosted API, checking the Community License's Territory (EU/UK/South Korea) and 1M-MAU commercial gate, sizing GPU/VRAM for local inference, ComfyUI integration, or reviewing and post-processing (retopology, UVs, decimation) the meshes these models produce. Not for image, video, or general LLM tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
calesthio/generative-media-skills☆ 2082026년 7월 14일 업데이트
Create, edit, analyze, and verify professional Excel workbooks for general business and operational use. Use when the deliverable is an .xlsx file; use a finance-specific modeling workflow for valuation or regulated financial models.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
sandbaseai/sandbase-skills☆ 2022026년 9월 26일 업데이트
Test payment and checkout flows end to end against PSP sandboxes — Stripe first, with the general pattern for Adyen/Braintree/PayPal. Covers Stripe test-mode card numbers and their decline codes, the 3DS/SCA challenge flow and its nested-iframe handling in Playwright, test clocks for subscription/billing-cycle simulation, webhook testing (stripe listen/trigger, signature verification, idempotency), failed/retried payments and refunds, and never using real cards. Use when: "test Stripe checkout," "payment test," "3DS test," "test webhook signature," "test subscription renewal," "test clock," "refund test," "decline card test," "checkout E2E." Not for: General API contract testing of non-payment endpoints — api-testing. PCI-DSS/regulatory compliance audit — compliance-testing. Related: api-testing, playwright-automation, compliance-testing, test-data-management, qa-project-context.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
petrkindlmann/qa-skills☆ 1962026년 6월 11일 업데이트
管理咨询 (管理咨询 (Management Consulting) — 战略与管理咨询的职业认知操作系统,从业者/咨询顾问/想入行者/采购咨询服务的客户视角。覆盖: (a) 第一性张力 — 假设驱动 (hypothesis-driven / answer-first / Day-1 answer) 的「先有答案再验证」⇄ 数据驱动 (data-led / bottom-up) 的「先穷尽事实再归纳」,这行的核心张力是「先射箭再画靶 vs 先画靶再射箭」,资深人偏 top-down 假设树减少 boil-the-ocean,但要时刻防 confirmation bias; 以及更深层的张力 — 「卖洞见 (insight / so-what) vs 卖工时 (analysis / 体力)」,「客户影响 (client impact / 落地) vs 智识严谨 (intellectual rigor / 漂亮 deck)」; (b) 方法论正典 (最标准化、最易蒸出高质量) — MECE (相互独立完全穷尽)、issue tree / logic tree (问题树/逻辑树)、hypothesis-driven problem solving (假设驱动)、Pyramid Principle (Minto 金字塔原理: SCQA 情境-冲突-疑问-回答 + 结论先行 + 自上而下 + 归纳/演绎)、80/20 (帕累托/抓大放小)、so-what (所以呢/洞见提炼)、storyline (故事线/ghost deck 鬼影稿)、driver tree / profit tree、2x2 矩阵、value chain、frameworks (Porter 五力/3C/4P/7S/BCG 矩阵/价值链) 既是脚手架也是陷阱 (套框架 vs 真洞见); (c) 行业结构 — MBB (McKinsey/BCG/Bain 战略三巨头) vs Big Four 咨询臂 (Deloitte/PwC Strategy&/EY-Parthenon/KPMG) vs 精品战略所 (Oliver Wyman/Kearney/Roland Berger/L.E.K./Arthur D. Little) vs 专业/职能 boutique (运营/数字化/PE 尽调); up-or-out (非升即走)、leverage model (杠杆模型: 1 个合伙人带多个 EM/咨询师/分析师的金字塔)、partner/principal/EM(项目经理)/associate(咨询师)/analyst(分析师) 职级; (d) 项目交付模型 (the case/study/engagement) — 提案/SOW (scope of work)、problem definition (问题定义/真问题 vs 表象问题)、workplan (工作计划)、hypothesis tree (假设树)、analysis (分析/建模)、synthesis (综合)、recommendation (建议)、implementation (落地); 客户管理 (steering committee 指导委员会、stakeholder 利益相关方、对齐); (e) 产出物 — the deck (PPT 幻灯片是咨询的母语)、executive summary、action title (行动标题/每页一句话结论)、ghost deck (先搭骨架后填数据)、Excel 财务模型、framework 图; (f) 职业/招聘 — case interview 生态 (案例面试: Victor Cheng LOMS/Case in Point/market sizing 市场估算/估算题/fit 行为面)、网申-笔试-案例面、up-or-out、exit options (退出路径: PE/VC/corp dev 企业发展/战略岗/创业/industry); (g) 争议/批判 — 咨询的价值之辩 (「借你的表告诉你时间」「frameworks 是剧场表演 vs 真洞见」Matthew Stewart《The Management Myth》)、McKinsey 的丑闻 (Purdue 阿片类/独裁政府/ICE/利益冲突, Bogdanich & Forsythe《When McKinsey Comes to Town》)、咨询 vs 自建战略部、generalist 通才 vs specialist 专才之争、AI/GenAI 对初级分析师工作的冲击; (h) 流派 — 战略经典 (Porter 定位学派/五力 vs 资源基础观 RBV vs 蓝海战略 Kim&Mauborgne vs 颠覆式创新 Christensen vs Mintzberg 应急战略批判 planning school); 三巨头气质分野 (McKinsey 事实驱动/CEO 信任/通才, BCG 概念/思想领导力/经验曲线/增长-份额矩阵, Bain 结果导向/NPS 净推荐值/PE 关系/与客户深度绑定)。不含: 投行 M&A/财务顾问 (估值/承销超出范畴)、纯 IT 实施/系统集成 (SI 人力外包)、纯审计/税务、纯人力 RPO/猎头、内部战略部 only (虽相关但本 skill 聚焦专业服务咨询的外部视角)。) Master OS — automated mastery of 管理咨询 (Management Consulting) — 战略与管理咨询的职业认知操作系统,从业者/咨询顾问/想入行者/采购咨询服务的客户视角。覆盖: (a) 第一性张力 — 假设驱动 (hypothesis-driven / answer-first / Day-1 answer) 的「先有答案再验证」⇄ 数据驱动 (data-led / bottom-up) 的「先穷尽事实再归纳」,这行的核心张力是「先射箭再画靶 vs 先画靶再射箭」,资深人偏 top-down 假设树减少 boil-the-ocean,但要时刻防 confirmation bias; 以及更深层的张力 — 「卖洞见 (insight / so-what) vs 卖工时 (analysis / 体力)」,「客户影响 (client impact / 落地) vs 智识严谨 (intellectual rigor / 漂亮 deck)」; (b) 方法论正典 (最标准化、最易蒸出高质量) — MECE (相互独立完全穷尽)、issue tree / logic tree (问题树/逻辑树)、hypothesis-driven problem solving (假设驱动)、Pyramid Principle (Minto 金字塔原理: SCQA 情境-冲突-疑问-回答 + 结论先行 + 自上而下 + 归纳/演绎)、80/20 (帕累托/抓大放小)、so-what (所以呢/洞见提炼)、storyline (故事线/ghost deck 鬼影稿)、driver tree / profit tree、2x2 矩阵、value chain、frameworks (Porter 五力/3C/4P/7S/BCG 矩阵/价值链) 既是脚手架也是陷阱 (套框架 vs 真洞见); (c) 行业结构 — MBB (McKinsey/BCG/Bain 战略三巨头) vs Big Four 咨询臂 (Deloitte/PwC Strategy&/EY-Parthenon/KPMG) vs 精品战略所 (Oliver Wyman/Kearney/Roland Berger/L.E.K./Arthur D. Little) vs 专业/职能 boutique (运营/数字化/PE 尽调); up-or-out (非升即走)、leverage model (杠杆模型: 1 个合伙人带多个 EM/咨询师/分析师的金字塔)、partner/principal/EM(项目经理)/associate(咨询师)/analyst(分析师) 职级; (d) 项目交付模型 (the case/study/engagement) — 提案/SOW (scope of work)、problem definition (问题定义/真问题 vs 表象问题)、workplan (工作计划)、hypothesis tree (假设树)、analysis (分析/建模)、synthesis (综合)、recommendation (建议)、implementation (落地); 客户管理 (steering committee 指导委员会、stakeholder 利益相关方、对齐); (e) 产出物 — the deck (PPT 幻灯片是咨询的母语)、executive summary、action title (行动标题/每页一句话结论)、ghost deck (先搭骨架后填数据)、Excel 财务模型、framework 图; (f) 职业/招聘 — case interview 生态 (案例面试: Victor Cheng LOMS/Case in Point/market sizing 市场估算/估算题/fit 行为面)、网申-笔试-案例面、up-or-out、exit options (退出路径: PE/VC/corp dev 企业发展/战略岗/创业/industry); (g) 争议/批判 — 咨询的
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
swaylq/master-skill☆ 1632026년 9월 6일 업데이트