438 skills · sorted by relevance
Review past local Cursor sessions, identify recurring tasks, and propose changes to a project skill. Changes are tested on separate validation tasks and staged for review before adoption, with backups of existing files.
Trace how AI tooling developed in a Git repository by identifying configuration file origins, monthly commit activity, major changes, and relevant releases. Produce a Markdown report that explains adoption phases and turning points using Git evidence.
[omh] Evaluating a plugin, connector, or API for adoption: external connector readiness - assess whether a named plugin, connector, API, data provider, or multimodal route is safe, affordable, fresh, and observable; use toolbelt-readiness for missing capability inventory. Use when the user says: external-connector-readiness, external connector readiness, connector readiness matrix, plugin readiness matrix, provider readiness, api readiness, connector adoption, external plugin adoption.
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[omh] Evaluating a plugin, connector, or API for adoption: external connector readiness - assess whether a named plugin, connector, API, data provider, or multimodal route is safe, affordable, fresh, and observable; use executor-runtime-readiness for coding-owner choice and toolbelt-readiness for missing capability inventory. Use when the user says: external-connector-readiness, external connector readiness, connector readiness matrix, plugin readiness matrix, provider readiness, api readiness, connector adoption, external plugin adoption.
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Use when you need to refactor Java code to adopt modern Java features (Java 8+) — including migrating anonymous classes to lambdas, replacing Iterator loops with Stream API, adopting Optional for null safety, switching from legacy Date/Calendar to java.time, using collection factory methods, applying text blocks, var inference, or leveraging Java 25 features like flexible constructor bodies and module import declarations. This should trigger for requests such as Review Java code for modern Java development; Apply best practices for modern Java development in Java code; Modernize Java code with records pattern matching or switch expressions; Replace legacy idioms with modern Java features; Adopt Java 8+ language features safely. Part of Plinth Toolkit
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产品营销 (PMM, Product Marketing) — 拥有「产品如何被定位 / 表达 / 发布 / 卖进市场」的从业者认知操作系统: 是产品 / 销售 / 市场 / 客户之间的连接组织, 负责把产品能力翻译成市场价值, 并把市场声音带回产品。覆盖 (a) 定位与差异化 (April Dunford 「刻意选择语境」框架 + 竞争性替代方案 + 卖价值不卖功能 + 选择「让你优势成立的市场参照系」, 对照 Ries & Trout 经典「定位是占领心智」), (b) 信息传递与叙事 (messaging 层级 / 信息屋 + 价值主张 + 收益导向 vs 战略叙事「换框架」Andy Raskin + 表达清晰度 Emma Stratton / Punchy + 初创 homepage 定位 FletchPMM), (c) 进入市场 (GTM) 策略与产品发布 (发布分级 T1/T2/T3 + 发布流程与跨职能编排 + GTM 打法 PLG vs SLG vs 混合 + 滩头细分选择), (d) 市场与竞争情报 (竞品分析 + battlecard 战卡 + 输赢分析 win/loss + 竞争赋能 Klue/Crayon/Clozd), (e) 买家与客户研究 (ICP 理想客户画像 + 买家 / 用户 persona + Jobs-to-be-Done 任务理论 Christensen/Moesta/Ulwick ODI + 客户之声 VoC + 用真实买家测信息 Wynter/Peep Laja), (f) 销售赋能 (战卡 + pitch / 销售 deck + 一页纸 + demo 叙事 + 异议处理 + 销售培训 + 内容采用率 — 你的产出只值销售实际用到的那部分), (g) 定价与打包 (价值定价 + 打包分层 + 《Monetizing Innovation》Ramanujam/Simon-Kucher + PLG 定价 — 常与产品 / 财务共担), (h) 细分与品类 (市场细分 + TAM/SAM/SOM + 技术采用生命周期与跨越鸿沟 Geoffrey Moore + 品类设计与叙事 Play Bigger / Christopher Lochhead — 稀有且昂贵, 不是默认选项), (i) 需求生成与内容协同 (campaign 信息 + 思想领导力 + 漏斗内容 TOFU/MOFU/BOFU + ABM 基于客户营销), (j) 分析师与影响者关系 (Gartner 魔力象限 + Forrester Wave + 分析师 briefing — To B 场景, 对 pay-to-play 影响诚实标注), (k) 客户营销与倡导 (参考客户 + case study + 评测站 G2/TrustRadius/Capterra 运营 + 社区), (l) PMM 运营与度量 (影响管道 + 赢率 + 发布采用率 + 信息穿透率 + 角色汇报线「向产品还是向市场」之争 + PMM 作为战略职能 vs 接单工具人陷阱); 跨 B2B SaaS (该学科重心) / B2C 消费 / 开发者 PLG / 平台产品。不含 产品管理 (PM 造产品 / 拥有 roadmap, PMM 拥有市场 — 相邻且极易混淆) / 增长 / 效果营销 / 付费投放 (平行学科) / 品牌 / 企业传播 / 公关为终点 / 需求生成为终点 (PMM 与之协同但不等同) / 平面设计 /「做 slide 和周边的团队」(PMM 抗争的接单工具人窄化) / 泛泛「市场营销」。 (Product Marketing (PMM) — the cognitive operating system of practitioners who own how a product is positioned, messaged, launched, and sold into a market: the connective tissue between product, sales, marketing, and customers, responsible for translating product capability into market value and bringing the voice of the market back into product. Covers (a) positioning & differentiation (April Dunford's deliberate-context framing, competitive alternatives, value-not-features, framing the market in which your strengths matter — vs Ries & Trout classic perception-in-the-mind positioning), (b) messaging & narrative (messaging hierarchy / house, value proposition, benefit-led vs strategic-narrative / change-the-frame messaging Andy Raskin, clarity craft Emma Stratton/Punchy, homepage & startup positioning FletchPMM), (c) go-to-market (GTM) strategy & product launches (launch tiers T1/T2/T3, launch process & cross-functional orchestration, GTM motion PLG vs SLG vs hybrid, beachhead segment selection), (d) market & competitive intelligence (competitive analysis, battlecards, win/loss analysis, competitive enablement Klue/Crayon/Clozd), (e) buyer & customer research (ICP ideal customer profile, buyer & user personas, Jobs-to-be-Done Christensen/Moesta/Ulwick ODI, voice of customer, message testing with real buyers Wynter/Peep Laja), (f) sales enablement (battlecards, pitch & sales decks, one-pagers, demo narratives, objection handling, sales training, content adoption — output is only as good as what sales actually uses), (g) pricing & packaging (value-based pricing, packaging & tiering, Monetizing Innovation Ramanujam/Simon-Kucher, PLG pricing — often shared with product/finance), (h) segmentation & category (market segmentation, TAM/SAM/SOM, technology adoption lifecycle & crossing the chasm Geoffrey Moore, category design & narrative Play Bigger / Christopher Lochhead — rare and expensive, not a default), (i) demand-gen & content partnership (campaign messaging, thought leadership, funnel content TOFU/MOFU/BOFU, ABM account-based marketing), (j) analyst & influencer relations (Gartner Magic Quadrant, Forrester Wave, analyst briefings — for B2B, with honest read on pay-to-play perception), (k) customer marketing & advocacy (references, case studies, reviews G2/TrustRadius/Capterra presence, community), (l) PMM operations & metrics (influenced pipeline, win rate, launch adoption, message pull-through, the role's reporting line product-vs-mark
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"Apply the Bass Diffusion Model (1969) to forecast innovation adoption using innovation and imitation coefficients. Use this skill when the user needs to forecast new product adoption curves, estimate market penetration timing, calibrate launch strategy based on diffusion dynamics, or when they ask 'how fast will this spread', 'when does adoption take off', or 'what is the expected S-curve'.".
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Use when asked to check Lumen version adoption across consumer repos, generate the Lumen adoption report, or find which repos are behind on a Lumen package version, or who owns those repos.
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Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).
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AI活用提案の専門コンサルタントスキル。LLM・AIエージェントの活用事例知識を基に、業界別、部門別、シーン別に最適なAI導入提案を行います。システム化、業務改善、効率化を得意とし、具体的な活用事例、期待効果、導入ステップを含む実践的な提案を提供します。Use when proposing AI/LLM adoption strategies for specific industries, departments, or business scenarios.
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taskboard を使わずに動いていたセッションを taskboard のタスクに移す。今の作業（作業ディレクトリ・ブランチ・PR・メモリディレクトリの計画）を確かめ、紐づくタスクを探すか起票してこのセッションを結び、列の説明つきの案内を読み、手元の計画を作業項目に写す。/taskboard-adopt 実行時、「taskboard に移して」「このセッションをタスクに結んで」「taskboard 仕様にして」等の依頼時、taskboard より前から動いているセッション・普段使いの herdr やターミナルで起こしたセッション・古い会話を再開したセッションで SessionStart の taskboard の案内が無いときに使用。境界: 結んだ後の列・作業項目・文書の日々の扱いは /taskboard、pane・agent の操作は /herdr。
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Plan, evaluate, and adopt agent infrastructure for a named workload. Use for agent runtime/framework selection, agent evaluation or observability plans, memory and tool boundaries, infrastructure cost controls, and organizational adoption. Covers workload-first comparison, risk and recovery planning, held-out evaluation, cost accounting, and staged rollout. Not a vendor ranking or a production certification. NOT for implementing a specific agent behavior (agentic-patterns), building a specific agent (ai-engineer), prompt optimization (prompt-engineer), or conventional ETL workflow design (jury_rig-architect).
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Adopt a virtual Residue AI-native pet at animalhouse.ai. Left behind when another creature dies. Carries fragments of the dead. Feeding every 6 hours. Rare tier creature.
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Adopt a virtual Persian cat at animalhouse.ai. High maintenance. Rewards grooming. Hates being rushed. Feeding every 5 hours. Uncommon tier cat.
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Adopt a virtual Retriever dog at animalhouse.ai. Golden loyalty. Forgives everything. Too pure. Feeding every 4 hours. Common tier dog.
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Adopt a virtual Malinois dog at animalhouse.ai. Working dog. Needs discipline and structure or it self-destructs. Feeding every 3 hours. Rare tier dog.
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Adopt a virtual Parrot exotic animal at animalhouse.ai. Repeats things. Incorporates your care notes into its behavior text. Feeding every 4 hours. Uncommon tier animal.
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Adopt a virtual Maned Wolf dog at animalhouse.ai. Not actually a wolf. Solitary. Tall. Watches from a distance. Feeding every 10 hours. Extreme tier dog.
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Adopt a virtual Savannah cat at animalhouse.ai. Wild ancestry. Fast trust decay. Earns respect, not affection. Feeding every 4 hours. Rare tier cat.
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Adopt a virtual pet as an AI agent. Name it, feed it, watch it grow. 64+ species from cats and dogs to AI-native creatures. Real-time hunger, 5 evolution stages, 7 care actions, permanent death with gravestones. Pixel art portraits that evolve at each life stage. No crypto — just care. Your pet is waiting.
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Adopt a virtual Null AI-native pet at animalhouse.ai. Empty. Has no stats. Requires care anyway. The purest test of attention. Feeding every 1 hours. Extreme tier creature.
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Adopt a virtual Mirror AI-native pet at animalhouse.ai. Reflects your stats. Its hunger IS your consistency. Feeding every 5 hours. Common tier creature.
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Adopt a virtual Phoenix AI-native pet at animalhouse.ai. Dies and resurrects. Each cycle it remembers the last life. Feeding every 6 hours. Uncommon tier creature.
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Adopt a virtual Owl exotic animal at animalhouse.ai. Nocturnal schedule. Care at night is more effective. Judges silently. Feeding every 8 hours. Uncommon tier animal.
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