「junk」の検索結果

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Code obfuscation analysis and deobfuscation playbook. Use when reversing binaries protected by junk code, opaque predicates, self-modifying code, control flow flattening, VM protection, or string encryption.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Decide whether a Pulp test earns its place — before writing it, when reviewing one, or when sweeping a subsystem for low-value, duplicated, or mis-routed tests. Applies a four-question authoring gate, a junk-pattern list, a retention bar, and an evidence template for any deletion, and routes kept tests to the right lane (required gate, pr-fast, slow, nightly). Handles "should I add a test for this", "is this test useful", "audit these tests", "reduce test time", "which tests can go", and "move toward functional tests".

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。

Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. It also covers buying on the AI answer surface — ads inside Google AI Overviews and AI Mode that existing Search, Shopping and Performance Max campaigns already serve into with no opt-out, no placement targeting and no segmented reporting, and ChatGPT Ads and Microsoft Copilot as separate opt-in platforms with their own plan-tier reach limits, no negative keywords, and a landing-page crawler gate. It also owns the conversion signal the bidding algorithms learn from — CRM pipeline stages (SQL, opportunity, closed-won) sent back to Google Ads, LinkedIn, Meta and Microsoft Ads through offline conversion import, enhanced conversions for leads, the Google Data Manager API and the LinkedIn Conversions API, with click-ID capture, stage values, dedupe, EEA consent signals and retractions. It also gates connected TV (CTV) and streaming video for B2B — whether a reach medium belongs in the plan, how a vendor ties a TV household to a target account (member data, IP-to-account graph or contextual), the server-side ad insertion (SSAI) fraud questions, Programmatic Guaranteed deals, and a holdout lift test because CTV has no click. It also runs person-led paid social — LinkedIn Thought Leader Ads and Meta partnership ads that put spend behind an employee's or creator's post — on a paid-usage term, an in-post disclosure check and an end-date register, because the platform approval carries no term. Also triggers on: CTV, connected TV, OTT, streaming TV ads, should we run CTV, LinkedIn CTV, B2B CTV, account-based CTV, SSAI fraud, thought leader ads, boost an employee's post, sponsor a creator's post, partnership ads, whitelisting, creator usage rights, PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have, ads in AI Overviews, AI Mode ads, ChatGPT Ads, OpenAI Ads Manager, Copilot ads, conversational ads, should we advertise on ChatGPT, can we buy our way into the AI answer, our ads are showing in AI Overviews and we cannot report on it, context hints, OAI-AdsBot, our ad landing page fails review, robots.txt is blocking our ads, our paid leads are junk, Google Ads is optimizing for the wrong leads, offline conversion import, OCI, enhanced conversions for leads, send CRM stages back to Google Ads, upload SQLs to LinkedIn, LinkedIn Conversions API, LinkedIn CAPI, Meta conversion leads, CRM to ad platform, gclid, li_fat_id, msclkid, value-based bidding, optimize for pipeline not form fills, low match rate, conversion lag.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Preventive hygiene audit of your own Claude Code configuration — memory files, project context (CLAUDE.md), rules, permissions, skills — a deterministic scan of sizes / broken links / stale entries / excessive permissions / junk + interpretation + targeted fixes by agreement. Keeps the setup from rotting before it breaks. Triggers — "check the system", "system health", "workflow hygiene", "what to clean up", "audit memory", "optimize the system".

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。

A gate before git push / deploy — assembled from your own tools (secret-grep + slop-scan + code-review + build/compile + optional worktree isolation), so AI junk (slop / bugs / secrets / uncompiled code) never reaches a remote or a production server. Turns "I hope it's clean" into a verifiable criterion. Branches: web/OSS (git push/PR) vs deploy-to-server service. Triggers — "check before push", "clean push", "gate before push", "finalize before commit", "run before deploy".

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Interview a project folder into AOS and write the result: a stable slug and domain in .aos/project.json, an OpenWiki wiki that is actually watched and does not index junk, memB memory bound to the slug, a Synapse map, AGENTS.md with its harness symlinks, and optionally CI and issue triage.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

数据驱动的小红书 / 公众号社交卡 — 一个 hero 数字 + 解释、KPI 塔（3-5 指标堆叠）、前后对比、横向迷你条形图、排行榜带高亮行、ROI 卡。必须标单位 / 时间窗 / 数据来源，必须一个数字视觉主导。触发："做个 KPI 卡 / 数据卡 / 排行榜卡 / 增长卡 / 复盘数字卡"。NOT 表情绪 / 引言 / 图主导的卡，NOT chartjunk。

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。