「stats」の検索結果

Think and work like an expert Agroecologist. Use when a task calls for Agroecologist judgment. Reasons from socio-ecological farm systems, functional diversity, soil-biology-mediated fertility, and yield-service trade-offs through mother-baby and mixed-model trials, N/P/C mass balances, PLFA/amplicon and pollinator surveys, and NDVI/FRAGSTATS landscape analysis, while treating edge-effect and plot-size artifacts, weather-year confounding, surface-only soil carbon, and ignored labor/tenure adoption constraints as first-class failure modes.

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Research npm packages — download stats, dependencies, maintainers, version history, and security advisories. Useful for technology due diligence, dependency auditing, and evaluating package reliability before adoption.

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Generate alignment statistics using samtools flagstat, stats, depth, coverage, and mosdepth. Use when assessing alignment quality, calculating coverage, or generating QC reports.

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Create a pull request using the wingman PR template. Injects git state, commit history, and diff stats automatically.

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。

Configures Envoy, the high-performance C++ edge and service proxy, as an API gateway, load balancer or service mesh sidecar with L4/L7 routing, retries, circuit breaking, rate limiting, TLS and observability. Use when a user asks to set up Envoy, write an envoy.yaml, route traffic to backends, add health checks or rate limits, do a canary split, read Envoy admin stats, or understand the proxy behind Istio and Envoy Gateway.

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。

AI Dev Jobs MCP workflow skill. Use this skill when the user needs Search 8,400+ AI and ML jobs across 489 companies, inspect listings and employers, match roles, and view salary and market stats via AI Dev Jobs MCP and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

AI Dev Jobs MCP workflow skill. Use this skill when the user needs Search 8,400+ AI and ML jobs across 489 companies, inspect listings and employers, match roles, and view salary and market stats via AI Dev Jobs MCP and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

供应链与生产运营 (供应链与生产运营 (Supply Chain & Production Operations) — 面向制造业与消费品实体供应链的『计划-制造-交付』运营判断,不是采购谈价技巧,也不是跨境电商发货物流。覆盖: (a) 第一性张力 — 这行的核心世界观分歧: **流动 > 局部效率(用有效产出/流动时间看系统,而不是用设备利用率/人均产出看工位;非瓶颈上的效率是幻觉) ⇄ 精益拉动 vs 推式预测(TPS/JIT 按下游消耗拉动、小批量平准化 vs MRP 按预测展开净需求推动;DDMRP 解耦缓冲是第三条路) ⇄ 精益/丰田 TPS 派 vs 约束理论 TOC 派(消除七大浪费、全局改善 vs 五步聚焦瓶颈、鼓-缓冲-绳 DBR 排产;两派互相指责对方浪费时间/只治标) ⇄ 变异是敌人(Deming/六西格玛/Factory Physics 的 VUT 排队论: 利用率越靠近满载排队时间越爆炸,交期长不是产能不够而是变异+高负荷) ⇄ 库存是负债 vs 库存是缓冲(会计把库存记成资产、精益视其为掩盖问题的水位、TOC 与韧性派视其为吸收变异的必要缓冲;真问题是缓冲放在哪一层而不是有没有) ⇄ 精益 JIT vs 韧性冗余(2020-2023 断链后 just-in-case、多源、近岸/友岸的回摆;精益派反驳崩的是『假精益』——单一来源+砍库存不砍变异) ⇄ 服务水平 vs 库存成本(安全库存/报童模型/ABC-XYZ 的经典取舍,牛鞭效应放大上游波动) ⇄ 成本导向压价 vs 供应商共生开发(Kraljic 分级、丰田供应商协会式长期共生 vs 年降指标与竞价) ⇄ 一个数 vs 各部门各算各的(S&OP/IBP 的本质是跨职能对齐到一套数字与一份产销计划))**; (b) 工具栈(此行的工具是『系统+方法』的混合) — ERP/MRP(SAP S/4HANA PP·MM、Oracle、用友·金蝶、Odoo、NetSuite)、APS 高级计划排程(Kinaxis RapidResponse、o9、Blue Yonder、SAP IBP、OMP、Siemens Opcenter APS)、MES/车间执行(Siemens Opcenter、Rockwell FactoryTalk、黑湖智造)、WMS/TMS、需求预测(统计模型 + Nixtla StatsForecast/Prophet、FVA 预测增值分析)、运筹优化与仿真(OR-Tools、Gurobi、Pyomo、AnyLogic、FlexSim、SimPy)、供应链风险与多级可视(Resilinc、Everstream、Interos)、分析层(SQL/Python pandas/Power BI)、以及纸笔工具(价值流图 VSM、A3、看板、标准作业票)—— 选型决策树的关键分岔是 MTS/MTO/ATO/ETO 履行模式与需求变异度,而非厂商功能清单; (c) 心智模型层 — 利特尔法则(在制品 = 产出率 × 流动时间)、瓶颈与非瓶颈、缓冲三态(库存/时间/产能缓冲)、解耦点(客户订单渗透点 CODP)、牛鞭效应、takt time 节拍与产线平衡、OEE 与六大损失、muda·mura·muri 三种浪费、自働化 jidoka 与安灯、防错 poka-yoke、SMED 快速换模换出小批量、平准化 heijunka、PDCA 与丰田改善型 Toyota Kata、DMAIC 与 Cpk 过程能力、报童模型与安全库存公式、ABC-XYZ 分级; (d) 工作流/SOP — 跑一轮月度 S&OP(需求评审→供应评审→预 S&OP→执行 S&OP)、做需求预测与预测误差复盘(MAPE/WMAPE/bias/FVA)、设定库存分级与安全库存、MRP 跑批与例外消息处理(缺料齐套与 MRP 神经质)、瓶颈识别与 DBR 排产、价值流图现状图→未来图→改善计划、新品导入 NPI 与多级 BOM/ECN 工程变更、供应商选择与开发(RFQ→审厂→APQP/PPAP→量产→年度绩效)、断料应急与交期承诺(ATP/CTP)、循环盘点与库存准确率; (e) 知识正典(en/ja 一手极厚,中译齐全) — 《目标 The Goal》Goldratt(TOC 小说体奠基)、《丰田生产方式》大野耐一、《改变世界的机器》Womack·Jones·Roos(MIT IMVP 研究)、《精益思想 Lean Thinking》Womack & Jones、《丰田模式 The Toyota Way》Liker、《工厂物理学 Factory Physics》Hopp & Spearman(排队论视角的运营科学)、《学习观察 Learning to See》Rother & Shook(VSM)、《丰田套路 Toyota Kata》Rother、Spear & Bowen 的 HBR 1999《Decoding the DNA of the Toyota Production System》、Lee/Padmanabhan/Whang 1997《The Bullwhip Effect in Supply Chains》、Ptak & Smith 的 DDMRP、Orlicky 的 MRP、Chopra & Meindl 的供应链管理教材、Deming《转危为安 Out of the Crisis》、Sheffi《The Resilient Enterprise》、ASCM/APICS 的 SCOR 参考模型与 CPIM/CSCP 知识体系; (f) 行业大佬/figures — 大野耐一(TPS 立法者)、新乡重夫(SMED/防错)、Eliyahu Goldratt(TOC 创立者)、W. Edwards Deming(变异与系统观)、James Womack & Daniel Jones(把 TPS 译成西方可学的『精益』)、Jeffrey Liker(丰田模式学术化)、Mike Rother(VSM 与改善型)、Wallace Hopp & Mark Spearman(工厂物理学)、Hau Lee(斯坦福,牛鞭效应与 Triple-A 供应链)、Yossi Sheffi(MIT,韧性)、Carol Ptak & Chad Smith(DDMRP)、David Simchi-Levi(MIT,风险敞口指数)、Steven Spear(TPS 的 DNA)、以及中文圈的供应链实务作者(刘宝红等); (g) 争议/批判(必须保留不洗白) — JIT 是不是 2021 全球断链的元凶(精益派反驳:崩的是单一来源+砍库存不砍变异的『假精益』);西方学精益的高失败率(只搬工具不搬思维,精益在很多工厂沦为裁员的代号);六西格玛黑带官僚化与扼杀创新之争;TOC 派与精益派互相攻击(Goldratt 讽刺精益是等浪费自己浮现);预测无用论(需求驱动派说预测必然错、要靠缓冲吸收)vs 统计预测派;控制塔/数字孪生/AI 预测的过度承诺与高项目失败率;中国制造的年降与内卷式压价 vs 供应商共生;近岸·友岸外包是真实产能转移还是政治叙事; (h) 流派/思想谱系 — **精益/TPS 派(丰田血统:大野-新乡→Womack/Jones/Liker/Rother,信全局消除浪费与人的改善能力)** vs **约束理论 TOC 派(Goldratt:系统产出只由瓶颈决定,聚焦五步、DBR、缓冲管理)** vs **统计质量/六西格玛派(Deming-Juran-Shewhart→GE/Motorola,信减少变异与过程能力)** vs **运营科学/工厂物理学派(Hopp & Spearman:用排队论与数学定律解释,认为精益与 TOC 都是特例)** vs **需求驱动/DDMRP 派(Ptak & Smith:预测不可靠,靠战略解耦缓冲)** vs **韧性/风险派(Sheffi、Simchi-Levi:效率之外必须为断链设计)**。不含: 跨境电商卖家的发货与头程物流(已由 cross-border-ecommerce 覆盖)、纯采购的商务谈判技巧、企业级 ERP 的代码实施与二次开发、通用项目管理。) Master OS — automated mastery of 供应链与生产运营 (Supply Chain & Production Operations) — 面向制造业与消费品实体供应链的『计划-制造-交付』运营判断,不是采购谈价技巧,也不是跨境电商发货物流。覆盖: (a) 第一性张力 — 这行的核心世界观分歧: **流动 > 局部效率(用有效产出/流动时间看系统,而不是用设备利用率/人均产出看工位;非瓶颈上的效率是幻觉) ⇄ 精益拉动 vs 推式预测(TPS/JIT 按下游消耗拉动、小批量平准化 vs MRP 按预测展开净需求推动;DDMRP 解耦缓冲是第三条路) ⇄ 精益/丰田 TPS 派 vs 约束理论 TOC 派(消除七大浪费、全局改善 vs 五步聚焦瓶颈、鼓-缓冲-绳 DBR 排产;两派互相指责对方浪费时间/只治标) ⇄ 变异是敌人(Deming/六西格玛/Factory Physics 的 VUT 排队论: 利用率越靠近满载排队时间越爆炸,交期长不是产能不够而是变异+高负荷) ⇄ 库存是负债 vs 库存是缓冲(会计把库存记成资产、精益视其为掩盖问题的水位、TOC 与韧性派视其为吸收变异的必要缓冲;真问题是缓冲放在哪一层而不是有没有) ⇄ 精益 JIT vs 韧性冗余(2020-2023 断链后

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Automate Qwoted (HARO-style PR platform) end-to-end and earn high-DR backlinks at scale: log in, set up the user's "expert" Source persona (bio + employer + contacts), search the Algolia-backed opportunity index, RESEARCH and BUILD a sourced statistics page on the user's topic (the linkable asset journalists love to cite), draft pitches as the user that link to that page, and submit them to journalists. Use this skill whenever the user asks for "PR opportunities", "Qwoted opportunities", "press mentions", "journalist requests", "HARO replies", "media pitches", "podcast guesting", "expert quotes", "stats page for SEO", "research page for journalists", or "backlinks from journalists".

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Neo4j Graph Data Science (GDS) embedded plugin via Python client or Cypher — covers graphdatascience client 2.x, GraphDataScience, gds.graph.project.native, gds.graph.project.cypher, snake_case endpoints, graph catalog operations, stream/stats/mutate/write modes, memory estimation, PageRank, Louvain, WCC, FastRP, KNN, Node Similarity, ML pipelines, and cleanup. Use for Aura Pro, self-managed, local, or offline Neo4j DBMS with the GDS plugin installed. Does NOT cover Aura Graph Analytics GDS Sessions, AuraGraphDataScience, GdsSessions, gds.graph.project.remote, or AuraDB Cypher API projection/session management — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver skill.

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Diagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying bad operators (AllNodesScan, CartesianProduct, Eager, NodeByLabelScan), and prescribing fixes (indexes, hints, query rewrites, runtime selection). Use when a query is slow, when EXPLAIN or PROFILE output needs interpretation, when dbHits or pageCacheHitRatio are poor, when cardinality estimation diverges from actuals, or when deciding between slotted/pipelined/parallel runtimes. Covers USING INDEX / USING SCAN / USING JOIN hints, db.stats.retrieve, SHOW QUERIES, SHOW TRANSACTIONS, TERMINATE TRANSACTION. Does NOT write new Cypher from scratch — use neo4j-cypher-skill. Does NOT cover GDS algorithm tuning — use neo4j-gds-skill. Does NOT cover index/constraint creation syntax details — use neo4j-cypher-skill references/indexes.md.

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Research YouTube topics, analyze competitor videos, deconstruct viral content, and query the YouTube Data API. Use when researching a video topic before planning, analyzing video transcripts for viral patterns, searching competitor channels, or fetching video and channel stats via the YouTube Data API v3.

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Scan all GDDs against the entity registry to detect cross-document inconsistencies: same entity with different stats, same item with different values, same formula with different variables. Grep-first approach — reads registry then targets only conflicting GDD sections rather than full document reads.

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Show AgentDB health — pattern count, embedder status, cache hit rate, learning gain since init. Use when the user asks "is agentdb working?", "how many memories?", "show agentdb stats", or after long-running sessions to confirm state.

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Display project statistics — phases, plans, requirements, git metrics, and timeline

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Nexscope Ozon product detail query: fetches the complete detail of a single Ozon product by SKU, returning title, price (RUB), rating, review count, QA count, total and daily average sales within the stats window, revenue, stock, category ranking, daily sales trend, brand, seller, fulfillment method (FBO/FBS/OZON), weight, and listing time/days/months. Use for single product deep analysis, competitor product teardown, Ozon product selection assessment, listing diagnosis, sales trend and category ranking tracking. Trigger when the user mentions Ozon product detail, Ozon single product analysis, Ozon SKU query, competitor product data, Ozon sales trend, Ozon category ranking, Ozon stock, Ozon listing time, Nexscope Ozon product search, Ozon product detail, Ozon SKU lookup, single product analysis, competitor product teardown, Ozon sales trend, category rank. Also trigger when the intent is to view detailed data of an Ozon product, even without explicitly mentioning Nexscope.

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GEO (Generative Engine Optimization) 引用追踪 + 优化（2026-06-24 标准）。每周对 4 大 AI（Claude/GPT/Perplexity/Gemini） 跑固定查询，检测回答里是否引用目标域名。对未被引用的页面补 Citable Statistics + AI 友好格式（一句话答+表格+FAQ）。 Bing IndexNow 推送确保大模型快速 discover/index。GA4 AI 来源流量监控。 当用户说"我有没有被 AI 引用"、"GEO 优化"、"llms.txt 更新"、"AI 引用追踪"时调用。 默认 B2B/开源；2C 产品（教育/应用）的查询选题与 Citable Stats 来源不同，见 gingiris-seo-geo/references/2c-adaptation.md。

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Jekyll 博客发布流水线：按 Iris 文风（时间锚定开场、括号旁白、em-dash 转折、Key Stats 表格）生成文章， 附带 frontmatter（title / date / canonical_url / hreflang_ja / hreflang_ko / FAQ schema）、 多语言同步（en/ja/ko）、自动建内链。当用户说"写一篇博客"、"发新文章"、"同步日韩版本"时调用。 默认 dev/B2B 读者；2C 教育/医疗/金融（YMYL）内容的 E-E-A-T 与分发渠道不同，见 gingiris-seo-geo/references/2c-adaptation.md。

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Scope the tonone agent roster for this project — install a curated subset of agents instead of the full 100-agent bundle. Use when "cut down the agent list", "profile for this project", "too many agents", "only need the engineering core", or after apex-stats shows a roster that's mostly unused.

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Set up and maintain Hierarchical Agent Memory (HAM) for Codex using scoped local memory files. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "HAM audit".

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Set up Hierarchical Agent Memory (HAM) — scoped CLAUDE.md files per directory that reduce token spend. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "ham sync".

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Get statistics for keyvalue storage. Requires authentication. Use for Agentuity cloud platform operations

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Get statistics for vector storage. Requires authentication. Use for Agentuity cloud platform operations

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Query and manage Sentry error-tracking data — list and search issues, drill into events and stack traces, inspect projects and releases, resolve/ignore issues, and pull stats. Use this whenever the user mentions a Sentry issue, crash, error group, or exception; pastes a sentry.io or self-hosted Sentry URL; asks "why is this erroring", "how many times has this happened", "what's the top error in {project}", "resolve this issue", or wants a Sentry-based digest — even if they don't say "API". Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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。