「mapping」の検索結果

Map customer journeys from awareness to advocacy. Visualize touchpoints, identify drop-off points, analyze channel interactions, and optimize the end-to-end experience. Use when the user asks about customer journey, funnel mapping, touchpoint analysis, or experience optimization.

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DEPRECATED — superseded by tree-sitting. This skill generated persistent _MAP.md files; tree-sitting does the same AST extraction at runtime (~700ms for 250 files) with no artifact to write, commit, or keep in sync. Use tree-sitting for "map this codebase", "explore repo", "understand structure", or any code navigation task. The final working version is archived at release mapping-codebases-v0.8.0.

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产品设计 / UX 设计 (产品设计 / UX 设计 (Product Design / UX Design) — 数字产品的用户体验设计：从用户研究 → 信息架构 → 交互设计 → 视觉/UI → 可用性测试 → 设计系统的认知操作系统，面向 UX designer / product designer / interaction designer / UX researcher / design systems engineer / design lead，以及想入行、转岗、服务这行(设计外包、设计工具厂商、研究平台)的视角。覆盖: (a) 第一性张力 — **用户中心/研究驱动 (user-centered, 'you are not the user', research before pixels, observe behavior not opinions) ⇄ 商业目标/约束/交付 (business goals, north-star metrics, ship it, designer 服务于产品成功不是个人作品集)**; **设计直觉/品味/craft (taste, aesthetics, 'attractive things work better' — Norman 情感化设计) ⇄ 数据/实验/A-B 测试 (data-driven, metrics, experimentation, 'opinions are not data')**; **用户说的 (stated preference, 访谈里说喜欢) ⇄ 用户做的 (revealed behavior, 行为观察 > 自陈, usability test 看人真实卡在哪)**; **一致性/设计系统/规范 (consistency, design system, Jakob's law 用户带着别处的习惯来, 复用组件) ⇄ 创新/打破模式 (innovation, 该不该破规范)**; **减法/简化 (simplicity, Krug 'Don't Make Me Think', less but better — Dieter Rams) ⇄ 功能丰富/利益相关者塞需求 (feature creep, stakeholder pressure)**; **UX(体验/流程/研究) ⇄ UI(视觉/像素/组件)** 的职责之争, **designer 该不该写代码 (design engineer / 'should designers code')**; (b) 核心工作流 / pipeline (最标准、最易蒸高质量+CLI 化) — Double Diamond (发现 discover → 定义 define → 开发 develop → 交付 deliver) / Design Thinking (共情 empathize → 定义 → 构思 ideate → 原型 prototype → 测试 test) / Design Sprint (Jake Knapp 5 天冲刺); 完整链: 用户研究 (访谈/问卷/可用性测试/卡片分类) → persona / journey map / JTBD → 信息架构 IA + 用户流 user flow → 线框 wireframe (低保真) → 交互/原型 prototype (高保真) → 视觉 UI / 设计系统落地 → 可用性测试 usability testing → handoff 给开发 → 度量迭代 (北极星 / HEART 框架); (c) 工具栈 — 设计/原型 (Figma 绝对主导 / Sketch 衰退 / Adobe XD 已停更 / Framer 高保真+建站 / Penpot 开源 / Principle / ProtoPie 微交互); 协作白板 (FigJam / Miro / Mural); 用户研究 (Maze / UserTesting / Lookback / Dovetail 研究库 / Optimal Workshop 卡片分类树测试 / Hotjar / Microsoft Clarity 热图); 设计系统/交付 (Storybook / 蓝湖(中) / Zeplin / design tokens / Tokens Studio); 度量 (Amplitude / Mixpanel / 北极星指标); AI 新兴 (Figma AI / v0 by Vercel / Galileo AI / Uizard / Cursor 给 design engineer); (d) 知识正典 — Don Norman《The Design of Everyday Things》(affordance/signifier/mapping 圣经)、Steve Krug《Don't Make Me Think》(可用性入门)、Alan Cooper《About Face》《The Inmates Are Running the Asylum》(交互设计/persona 起源)、Jakob Nielsen 10 Usability Heuristics + NN/g 文章库、Jake Knapp《Sprint》、Jeff Gothelf《Lean UX》、Teresa Torres《Continuous Discovery Habits》、Erika Hall《Just Enough Research》、Julie Zhuo《The Making of a Manager》、《Refactoring UI》(Adam Wathan/Steve Schoger)、《Universal Principles of Design》、Laws of UX (Jon Yablonski)、《100 Things Every Designer Needs to Know About People》(Susan Weinschenk)、IDEO/Stanford d.school 设计思维材料; (e) figures/流派 — 认知心理/人因派 (Don Norman 认知科学奠基、Susan Weinschenk 行为心理、Kathryn Whitenton); 可用性工程派 (Jakob Nielsen 启发式/折扣可用性、Steve Krug、Jared Spool UIE); 交互设计/目标导向派 (Alan Cooper goal-directed/persona、Kim Goodwin); 设计思维派 (IDEO Tim Brown、David Kelley d.school); 精益/持续探索派 (Jeff Gothelf Lean UX、Teresa Torres continuous discovery、Marty Cagan《Inspired》产品发现); 设计系统/系统化派 (Brad Frost Atomic Design、Nathan Curtis design tokens、Alla Kholmatova); 视觉/UI craft 派 (Adam Wathan & Steve Schoger Refactoring UI、Dieter Rams 'less but better' 工业设计遗产); 设计领导/职业派 (Julie Zhuo、Aarron Walter 情感化设计、John Maeda); 移动优先 (Luke Wroblewski 'Mobile First'); (f) 行业话术/黑话 — affordance 可供性 / signifier 意符 / mental model 心智模型 / IA 信息架构 / heuristic evaluation 启发式评估 / usability 可用性 / a11y accessibility 无障碍 / WCAG / persona 用户画像 / journey map 用户旅程 / JTBD jobs-to-be-done / north-star metric 北极星 / HEART 框架 / wireframe 线框 / mockup / prototype 原型 / fidelity 保真度 (lo-fi/hi-fi) / design system 设计系统 / design token 设计令牌 / atomic design 原子设计 / component library 组件库 / handoff 交付 / red routes 关键路径 / dark pattern 暗黑模式 / Fitts's law / Hick's law / Jakob's law / Miller 7±2 / progressive disclosure 渐进披露 / above the fold / hamburger menu / skeuomorphism vs flat / micro-interaction / empty state / edge case / happy path / design debt 设计债 / dogfooding / dot voting / affinity mapping 亲和图 / card sorting 卡片分类 / tree testing / A/B test / NPS / SUS (System Usability Scale); (

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GitHub 解封 — 账号封禁/限制/恢复的领域操作系统。覆盖：(a) GitHub 官方政策体系 (TOS / 可接受使用政策 / Trust & Safety 执行模式 / 公开申诉路径 / 受制裁地区解读)；(b) 封号原因诊断学 (spam / abuse / 制裁误标 / 2FA 丢失 / ToS 违规 / 账号劫持误判 / ban evasion 等子类型 + 症状→原因映射 + 自我分诊压测)；(c) 申诉实操 craft (写一封能让 T&S 快速判误伤的英文申诉信 + 升级阶梯 + 法务介入边界 + Don'ts)；(d) 制裁与合规硬边界 (OFAC SDN 名单 / 哪些国家或地区真不可达 / VPN 误判路径 / 中国大陆不是美国制裁地区的反复重申)；(e) 中国大陆开发者特别处境 (sanctioned region 误标 + SMS 验证不支持 +86/+852 + Gitee/GitLab/Codeberg/JihuLab 备用迁移 + 私有自托管 Gitea/Forgejo)；(f) 真实案例库 (~30+ 公开案例分类拆解：误伤 / 真违规 / 制裁误标三类各自赔率与申诉成功路径)。伦理锚绝不软化：不教 ban evasion / 不教撒谎式申诉 / 真违规承认改进 / 丢 2FA 无恢复因子则诚实告知 / 中国制裁误标靠申诉不靠换 VPN / 申诉 6 个月窗口必算清 / 约 3% (业内估) 真违规恢复 vs 约 79% (业内估) 误伤申诉成功率分层标。 (GitHub Account Reinstatement — the domain operating system for GitHub (and analogous platform) account suspension, restriction, appeal, and recovery. Covers: (a) GitHub official policy architecture (TOS / Acceptable Use Policies / Trust & Safety enforcement patterns / public appeals pathways / sanctioned-region interpretation); (b) suspension-cause diagnostics (spam / abuse / sanctions mis-flag / 2FA loss / ToS violation / account hijack false-positive / ban evasion taxonomy + symptom→cause mapping + self-triage pressure test); (c) appeal craft (writing an English appeal letter that gets T&S to fast-track a false-positive ruling + escalation ladder + legal-engagement boundary + Don'ts); (d) sanctions and compliance hard boundaries (OFAC SDN list / which countries/regions are truly unreachable / VPN false-positive paths / China is NOT a US-sanctioned region — repeated emphasis); (e) mainland China developer special circumstances (sanctioned-region mis-flag + SMS verification does not support +86/+852 + Gitee/GitLab/Codeberg/JihuLab fallback migration + self-hosted Gitea/Forgejo); (f) real-case library (~30+ public cases with categorized analysis: false-positive / genuine violation / sanctions mis-flag, each with win-rate and appeal success path). Ethical anchors absolutely not softened: do NOT teach ban evasion (creating new accounts turns a temp ban into a permanent one — the single biggest mistake); do NOT teach social engineering or lying in appeals (misuse + actually confirms violation); genuine violations: admit + improve beats denial; lost 2FA + no recovery factor → do not pretend recovery is possible; China sanctioned-region mis-flag → fix via appeal, not repeated VPN switching; appeal form 6-month window must be counted carefully; historical 约 3% (业内估) genuine-violation recovery vs 约 79% (业内估) false-positive appeal success rate stratification must be honestly labeled. Anti-samples (explicitly describe harm, never endorse): developer permabanned for ban evasion / open-source team org-banned for personal attacks in public issues / cases where repeated VPN switching escalated risk-control severity.) Master OS — automated mastery of GitHub Account Reinstatement — the domain operating system for GitHub (and analogous platform) account suspension, restriction, appeal, and recovery. Covers: (a) GitHub official policy architecture (TOS / Acceptable Use Policies / Trust & Safety enforcement patterns / public appeals pathways / sanctioned-region interpretation); (b) suspension-cause diagnostics (spam / abuse / sanctions mis-flag / 2FA loss / ToS violation / account hijack false-positive / ban evasion taxonomy + symptom→cause mapping + self-triage pressure test); (c) appeal craft (writing an English appeal letter that gets T&S to fast-track a false-positive ruling + escalation ladder + legal-engagement boundary + Don'ts); (d) sanctions and compliance hard boundaries (OFAC SDN list / which countries/regions are truly unreachable / VPN false-positive paths / China is NOT a US-sanctioned region — repeated emphasis); (e) mainland China developer special circumstances (sanctioned-region mis-flag + SMS verification does not support +86/+852 + Gitee/GitLab/Codeberg/JihuLab fallback migration + self-hosted Gitea/Forgejo); (f) real-case library (~30+ public cases with categorized analysis: fa

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UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use when conducting user research, creating personas, mapping user journeys, planning usability tests, or validating designs.

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Counts and compares blog posts across a target and competitors, audits content freshness via sitemap lastmod dates, and maps competitor organic keywords (US, non-branded, informational) against the target to surface ranking gaps. Use when asked to compare blog counts, content volume, publishing cadence, content freshness, keyword mapping, keyword gap analysis, or competitor keyword reports — e.g. "how many blog posts does X have vs Y", "how fresh is content on X", "where does [target] rank for [competitor] keywords", "keyword gap between X and Y". Works from company names alone. Always use for blog comparison, freshness audits, and keyword mapping tasks.

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Research, expand, and cluster keywords for content strategy. Use when planning content topics, building keyword lists, expanding seed keywords, clustering by topic and intent, or mapping keywords to funnel stages. Triggers on "keyword research," "keyword expansion," "keyword clustering," "content topics," "what to write about," "topic ideation," "content planning," "6 circles method," "seed keywords," "keyword difficulty," "search volume," "intent mapping," "pillar content strategy," or "content calendar planning." For SEO optimization and auditing, see seo-and-aeo-strategy.

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Build a learning progression showing prerequisite-to-mastery steps for a target skill or understanding. Use when sequencing content, designing diagnostics, or mapping prerequisite gaps.

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Tags harness-decomposed curriculum items (KUDs, LTs, criteria) with a school's developmental band metadata while preserving source voice and labels. Supply the band schema; the skill derives mapping rules from it.

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Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI startup competitive analyses including the Lovable case study (4.3M views, 229K impressions launch day). By @WeiYipei. Inside: 4-step research framework (website → social → traffic → ad spend) · 3-version Wayback evolution analysis · X/Twitter propagation-chain mapping · growth-flywheel 6-stage scoring · KOL identification · content-effectiveness ranking · ICP + freemium pricing teardown · self-check checklist. New in 1.2: time-machine competitor archaeology — Google date-range search back to pre-fame years, star-history launch-day reconstruction (AppFlowy ~40-channel zero-budget launch teardown), competitor funding news as a launch-timing signal, and the "open source alternative" positioning play. Sourced from first-hand founder interviews (AFFiNE co-founder, 2026-04, self-reported figures marked). 🇨🇳 你的竞品刚刚爆了。你完全不知道他们怎么做到的。该拆官网？盯社媒？画增长飞轮？这份 SOP 给你从 Wayback Machine 快照到 X/Twitter 传播链路还原到飞轮六阶段评分的完整方法论。基于 150+ AI 创业公司竞品分析实战，含 Lovable 完整案例。 🇯🇵 競合が突然バズった。どうやって成長したか全く分からない。このSOPは、Waybackスナップショットからツイッター伝播チェーン分析、フライホイール6段階スコアリングまでの完全な競合調査フレームワークを提供します。150以上のAIスタートアップ分析から構築。 🇰🇷 경쟁사가 갑자기 폭발적으로 성장했습니다. 어떻게 그렇게 빨리 성장했는지 모릅니다. 이 SOP는 Wayback 스냅샷부터 X/Twitter 전파 체인 분석, 플라이휠 6단계 스코어링까지 완전한 경쟁사 조사 프레임워크를 제공합니다. 150개 이상의 AI 스타트업 분석에서 구축. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "竞品调研" | "竞品分析" | "增长飞轮" | "传播链路" | "競合分析" | "경쟁사 분석" | "social media teardown" | "Twitter propagation" | "content strategy analysis"

Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI startup competitive analyses including the Lovable case study (4.3M views, 229K impressions launch day). By @WeiYipei. What's inside: • 4-step research framework (website teardown → social media → traffic sources → ad spend) • 3-version website evolution analysis (V1 / Beta / Launch via Wayback Machine) • X/Twitter propagation chain mapping (4-phase model + single-post teardown template) • Growth flywheel 6-stage scoring system (Activation → Referral → Acquisition → Retention → Revenue → Product) • KOL identification framework (Traffic King / Topic Starter / Mindset Shifter) • Content effectiveness ranking (validated: Visual Demo > User Story > Competitor Comparison) • ICP deep analysis template + Freemium pricing teardown • Complete self-check checklist 🇨🇳 你的竞品刚刚爆了。你完全不知道他们怎么做到的。该拆官网？盯社媒？画增长飞轮？这份 SOP 给你从 Wayback Machine 快照到 X/Twitter 传播链路还原到飞轮六阶段评分的完整方法论。基于 150+ AI 创业公司竞品分析实战，含 Lovable 完整案例。 🇯🇵 競合が突然バズった。どうやって成長したか全く分からない。このSOPは、Waybackスナップショットからツイッター伝播チェーン分析、フライホイール6段階スコアリングまでの完全な競合調査フレームワークを提供します。150以上のAIスタートアップ分析から構築。 🇰🇷 경쟁사가 갑자기 폭발적으로 성장했습니다. 어떻게 그렇게 빨리 성장했는지 모릅니다. 이 SOP는 Wayback 스냅샷부터 X/Twitter 전파 체인 분석, 플라이휠 6단계 스코어링까지 완전한 경쟁사 조사 프레임워크를 제공합니다. 150개 이상의 AI 스타트업 분석에서 구축. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "竞品调研" | "竞品分析" | "增长飞轮" | "传播链路" | "競合分析" | "경쟁사 분석" | "social media teardown" | "Twitter propagation" | "content strategy analysis"

Data governance strategy, quality validation rules, and data dictionary management for vehicle insurance platform. Use when defining data quality standards, implementing validation rules, managing field mappings, resolving data conflicts, or establishing data governance processes. Covers data cleaning standards, quality metrics, and mapping management.

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Dasel v3 query patterns for Hibernate .hbm.xml mapping files — entity-table binding, Java property-to-column extraction, one-to-many set/list/bag relationship tracing, many-to-one foreign key discovery, batch scanning across 60+ HBM files. Use when querying Hibernate ORM class mappings, extracting schema metadata from Java persistence layer, or auditing entity-column relationships in enterprise legacy codebases.

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Maps your company's GTM motion (PLG, SLG, or Hybrid) to concrete funnel stages with inputs, outputs, and qualification criteria per stage. Detects motion type from website + Exa research, then produces pre-close + post-close stage definitions, the FETE mapping (Fit / Engagement / Tier / Eligibility), and closed-lost re-entry paths. Triggers - "funnel strategy", "funnel stages", "pipeline stages", "sales process", "lead qualification", "GTM motion mapping", or any work defining how leads flow through the business. Upstream - recommended company-context, icp-research. Downstream - feeds lifecycle-marketing, outreach-emails, content-strategy. NOT for lead scoring logic (use /lead-scoring) or lifecycle email campaigns (use /lifecycle-marketing).

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Use when building or refining an Ideal Customer Profile (ICP), defining target personas, mapping buyer psychographics, or discovering where prospects gather online. Triggers: 'build my ICP', 'define target customer', 'who should I sell to', 'refine ICP', 'persona mapping'.

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Develops PE sector theses with industry mapping, secular trends, fragmentation opportunity, and target universe identification. Use when building sector strategies, mapping investment themes, or identifying subsector opportunities.

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Redact rendered visual artifacts — Mermaid diagrams, SVGs, and HTML dashboards — before public disclosure, using an ordered longest-first mapping table (specific label, then namespace label, then catch-all placeholder) that swaps sensitive identifiers for descriptive stand-ins while preserving structure, node ids, and teaching value. Covers whole-word identifier replacement, node-label-vs-node-id discrimination, re-rendering the derived image from the redacted source, and verifying the result through the redaction gate. Use when a diagram or dashboard built from internal findings must ship to a public mirror without leaking the names behind it.

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매핑 에디터. proposal_script.md와 generate_제안서.py 간의 매핑을 검증하고 수정합니다. 스크립트의 레이아웃 지시가 코드에 올바르게 반영되었는지 확인. 트리거: /mapping-editor, 매핑 검증, 스크립트-코드 매핑

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STIX 2.1 bundle creation reference. Object types, relationships, and JSON templates for structured threat intelligence sharing. Includes the per-claim mapping of evidence grades (access level and claim support) from /quality-of-information-check to STIX confidence, and the reverse mapping on import.

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Use when you need to answer "which attacker techniques do our controls actually stop, and how well?", "what controls should I have for this threat?", or "what telemetry should I collect to detect it?" — joining a customer's or your own security control baseline to ATT&CK techniques using a public, versioned evidence base of 9,545 control-to-technique mappings from six sources. Produces four ranked lists (addressed strongly, addressed weakly, real gaps, and techniques no control anywhere addresses). Use for control gap analysis, security programme prioritisation, board reporting on coverage, or the Resistance Strength side of a FAIR risk assessment. Invoke after /threat-actor-profiling or /lookup-liberty91 has produced a technique list.

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Company expense receipt workflow. OCR, rename, sort, summarize, and prepare receipt images/PDFs/videos for D365 expense mapping and attachment. Use when sorting company expense receipts, organizing travel expense files, OCR renaming, receipt sorting, expense sorting, レシート仕分け, 出張経費バッチ, D365経費仕分け, D365経費カテゴリマッピング, or D365経費精算. Not for personal or sole-proprietor tax receipts (確定申告).

White-box source code security review structured around OWASP ASVS 5.0 (427 verification requirements across 16 chapters). Reads and understands application source code to build a security-aware knowledge base that enriches all downstream skills. Covers: tech stack identification, route/endpoint mapping, authentication and authorization architecture, dangerous function patterns, source-to-sink data flow tracing, IaC review, dependency analysis, ASVS compliance mapping, and LLM integration security (prompt injection, tool abuse, output handling, RAG poisoning, MCP server patterns). When LLM/AI framework usage is detected, automatically reviews OWASP LLM Top 10 patterns from source code and chains into /ai-redteam with white-box context for live endpoint testing. Chains into /pentester, /threat-modeling, /web-exploit, /api-security, /cloud-security, /analyze-cve, /credential-audit, and /ai-redteam — providing white-box context that transforms black-box testing into targeted, informed assessment.

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Analyzes remote access trojan command-and-control by mapping the RAT command set, identifying the C2 transport and beacon parameters, and cataloging operator capabilities from static strings and config. Activates for requests to analyze RAT C2, map a trojan command handler, or identify remote-access capabilities and C2 transport.

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Annotate genetic variants (GWAS hits, eQTLs, rare variants) with ENCODE functional data to interpret non-coding variation. Use when the user has variants of interest and wants to understand their regulatory context, identify causal variants from GWAS loci, assess variant impact on regulatory elements, perform enrichment testing of variant sets in tissue-specific annotations, or link variants to target genes through enhancer-gene maps. Handles the full post-GWAS workflow from variant set → tissue mapping → functional annotation → fine-mapping awareness → enrichment → variant-to-gene → prioritization. Use this skill for ANY variant interpretation task involving ENCODE chromatin, accessibility, TF binding, or 3D genome data.

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