'fab' 검색 결과

Claude API (platform.claude.com) のモデル・SDK。 model ID・選択基準、Fable・Mythos 5.1 / Opus・Sonnet・Haiku 5.5 移行・破壊的変更、 コスト最適化 (effort / caching / batch / advisor)、pricing、deprecation、用語集。 公式 SDK 7 言語 (Python / TypeScript / Java / Go / Ruby / C# / PHP)、 Claude API 用 CLI `ant` (Claude Code CLI とは別。quickstart / authentication / scripting、`ant apply` resources as code、Managed Agents session 接続)、 middleware、OpenAI SDK 互換、Apple Foundation Models。

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AppKit macOS UI リファレンス。Liquid Glass (NSGlassEffectView)、 NSApplication, NSWindow, NSViewController, NSView, NSMenu, NSToolbar, NSTableView, NSOutlineView, NSCollectionView, NSSplitViewController, NSTabView, NSPopover, NSOpenPanel / NSSavePanel, NSSearchField, NSControl, NSStackView, NSScrollView, Diffable Data Source, target-action, responder chain。

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Execute C# with Unity APIs when existing uloop tools cannot inspect or edit enough. Use for scene, prefab, SerializedObject, AssetDatabase refresh/.meta generation, menu, or PlayMode automation.

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Prepare a request to change the terms of a Japanese job offer before accepting it, sorting what the user wants changed (pay components, start date, work style, location, title, probation) by priority, tying each ask to a basis the user can show (the written offer, the job posting, what was said in interviews, their own records), working the timing back from the acceptance deadline, and drafting the message to the employer or recruiting agent. Use when a user has an offer and wants to ask for higher pay, a later start date, more remote days, or other changed terms, or wants to know how to raise it without losing the offer; do not use to predict whether the request will succeed, to set a target figure from market rates, to fabricate a competing offer, to decide whether to accept, or to send anything to the employer.

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Picks the model a coding agent uses when a test or browser check sends an AI call. Use when choosing a model for any test, UI check, bench, or agent run, or when a test names Opus, Sonnet, Fable, Astra, or any model by habit.

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UI 自動化生產總控 SKILL — 從 UI 示意圖、參考圖、Figma snapshot 或既有頁面經驗啟動，串接 proof draft、family-map、MCQ、normalized recipe、task shard、ui-spec 與 CompositePanel / ChildPanel scaffold、靜態驗證、Browser QA 自動調整，直到交付人類驗收。USE FOR: 想把一張新 UI 從「給圖」一路推進到可驗證的 screen package 與 QA 閉環。DO NOT USE FOR: 單純修一個 runtime bug、單一 prefab 微調、純美術資產委託。

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UI spec 與 Panel scaffold SKILL — 以 normalized recipe、family defaults、content contract 產出或更新 screen package 與 CompositePanel / ChildPanel TypeScript 骨架。USE FOR: 新 screen 從 recipe 落地、既有 screen 做 structure normalization、需要執行 compile-recipe-to-screen-spec.js / compile-recipe-to-panel-scaffold.js。DO NOT USE FOR: 最終視覺 QA、手工 Prefab 微調當正式流程。

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Performs claim-evidence verification for agent outputs, recording supported, contradicted, or insufficient-evidence results alongside source accessibility. Activate on 'detect hallucination', 'fact check', 'verify claims', 'check accuracy', 'find fabrications'. NOT for validation (use dag-output-validator) or confidence scoring (use dag-confidence-scorer).

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Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

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Design roadside signs and large-format ads for businesses, especially automotive dealers. Use for pylon signs, monument signs, lot banners, highway billboards, digital roadside boards, service-lane signage, used-car event signs, lease or finance campaign concepts, and other driver-at-speed messaging where legibility, offer hierarchy, and dealer ad compliance matter. Activate on "road sign", "billboard", "dealership sign", "car dealer ad", "pylon sign", "monument sign", "service lane signage", "automotive billboard", "roadside ad", "dealer lease offer", or "drive-by signage". NOT for fabrication drawings, permitting or local sign-code review, web or social ad campaigns, or full brand identity systems.

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Find and identify the CAttributeList_SetOrAddAttributeValueByName function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the econ attribute-list workhorse that sets an existing float attribute by name or appends a new one. Resolved via a durable caller anchor: the weapon paint-kit/wear/seed/kill-eater applier function, which is itself located by the literal econ keyvalue-system strings "set item texture prefab" / "set item texture wear" / "set item texture seed". This function backs weaponpaints-style plugins that programmatically set an econ item's paint kit / wear / seed / stickers. Trigger: CAttributeList_SetOrAddAttributeValueByName, SetOrAddAttributeValueByName

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GRASP principles knowledge base for PHP 8.5 projects. Provides quick reference for 9 responsibility assignment patterns (Information Expert, Creator, Controller, Low Coupling, High Cohesion, Polymorphism, Pure Fabrication, Indirection, Protected Variations). Use for architecture audits and design decisions.

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Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services

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Professional assistant for 3D printing, laser engraving, and 3D modeling workflows. Supports generating visual references for sculpting (Forger/ZBrush), creating 3D models via Meshy AI, and optimizing FDM/Resin print settings. Use when designing, fabricating, or troubleshooting physical maker projects.

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Design, critique, or specify honest charts and data visualizations from known data and audience needs. Use for chart selection, misleading-chart audits, accessibility, dashboard visuals, or "make this data easier to understand"; do not use for raw data analysis, PPT deck generation, spreadsheet surgery, or fabricated chart data.

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Use when AI contribution metadata must match the actual workflow, when model alias and dated identifiers need auditing, when verified and fabricated citation counts feed a release decision, or when a venue's AI disclosure statement needs evidence behind it.

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Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 5-stage recon pipeline (seed discovery, asset expansion, enrichment, exposure analysis, reporting), asset-graph discipline with 29 asset types, severity rubric (CRITICAL/HIGH/MEDIUM/LOW/INFO), confidence upgrade workflows, time budgeting, asset-level triage rules, scale-based tactics, identity-fabric mapping (Entra/Okta/ADFS/Google/SAML/M365 Teams+SharePoint+OAuth), API and auth-map methodology, JavaScript deep analysis, mobile attack surface, cloud attack surface, breach×identity correlation, detectability tagging, detection-aware probing (back-off, persona rotation), read-only validator discipline, WAF/CDN bypass + origin discovery, vulnerability prioritization (CVE/EPSS/KEV), phishing infrastructure planning + pretext development, bug bounty submission templates, client deliverable templates with risk translation, threat-actor investigation (incl. RU/CN pivots), cryptocurrency tracing,...

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Hunt TLS/SSL and DNS misconfigurations — missing HSTS (downgrade attack), weak cipher suites, expired/invalid certificates, mTLS bypass, missing SPF/DKIM/DMARC (email spoofing), DNS Zone Transfer (AXFR), dangling CNAME subdomain takeover, CAA records. Most of these are Info/Low on their own — this skill is opinionated about which findings actually pay (spoofable DMARC with delivered-to-inbox proof, AXFR returning internal hosts, dangling-CNAME takeover) versus which get rejected as best-practice noise (missing CAA, missing HSTS with no MitM position). Use during recon to find infrastructure weaknesses, and to TRIAGE them honestly before reporting.

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LLM bugs are only worth reporting when they cross a trust boundary you can prove — an OOB callback, a verbatim-reproducible secret, a cross-tenant record, or code execution. A model 'saying something bad once' is confabulation, not a vulnerability. Read the False-Positive Gate before claiming anything.

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