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概要と使いどころ
Make an evidence-backed go or no-go decision for an Alchemy-backed production release. Use when reviewing a launch or material integration change. Trigger with "Alchemy production checklist", "Alchemy go-live review", or "is this Alchemy integration ready".
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Issue an evidence-backed production decision across Adobe auth, entitlements, API versions, data custody, async reliability, spend, observability, and rollback. Use before launch or a material integration change. Use when the task requires adobe production readiness decision. Trigger with "Adobe production checklist", "approve Adobe launch", or "Adobe go-live review".
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Runs the production schedule — sequencing and releasing work, managing work in process and changeovers, and holding the promised date when the floor and the order book disagree. Use this to build or fix a production schedule, decide what to run next, diagnose why lead times are growing while machines look busy, set up OEE or throughput measurement, or choose between make-to-stock and make-to-order for a product line.
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Render, preview, validate, and burn consistent production subtitles from QC-passed real-timestamp caption artifacts. Use for AI-video production, washed-video production, IndexTTS2 explainers, practical long-form videos, talking-head edits, Kimi-style dark rounded captions, warm-white translucent captions, subtitle styling, subtitle overlays, or fixing caption size, position, shadow, drift, and 4K scaling. Always pair with ra-audio-to-subtitles when final word timestamps do not already exist.
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Performs root-cause analysis of a bug — from a GitHub issue, an issue URL, or a free-text description — before any reproduction or fix is written. TRIGGER when: triaging or diagnosing a bug, investigating why something misbehaves, an issue number/URL handed over for analysis, needing the root cause before touching code. DO NOT TRIGGER when: a failing reproduction test already exists and you are ready to fix → opsmill-dev-fixing-bugs; writing that reproduction test → opsmill-dev-test-driving-bugs; capturing a new bug as a ticket → opsmill-dev-creating-issues.
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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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Orchestrate production-grade Blender work by probing the installed Blender version, retrieving relevant official Blender documentation, assigning visual design intent, classifying shape grammar and geometry/deformation/procedural/physics/shading needs, comparing direct Blender components with node systems, creating staged artifacts, and routing to specialist skills. Use for every non-trivial Blender creation, reconstruction, simulation, animation, environment, or scene-modification request, especially when the user does not know whether to use topology, Boolean, Array, curves, snapping, Displace, simulation, Geometry Nodes, or shading.
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Triage and manage production incidents. Trigger with "we have an incident", "production is down", "something is broken", "there's an outage", "SEV1", or when the user describes a production issue needing immediate response.
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Create, implement, or review html video production work with format, accessibility, performance, and visual-verification controls. Use when the requested deliverable depends on html video production.
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Plan, troubleshoot, and hand off provider-independent in-camera VFX and LED volume virtual-production work, with Unreal Engine/nDisplay/Live Link awareness. Use for ICVFX suitability, LED stage geometry, frustums, tracking, lens calibration, sync, render nodes, color, lighting/reflections, artifact mitigation, rehearsal, live-composite fallback, monitoring, records, QA, and handoff. Do not use for ordinary offline post compositing, generic storyboards, or full game production.
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Produce audio-first podcast episodes with generative tools — design the show and episode format (interview, narrative, news brief, two-host conversational), write scripts for the ear, decide between fully-synthetic and hybrid (recorded human + synthetic) production and meet the disclosure duty each triggers, cast and direct multi-voice TTS for consistency and chemistry, build episode structure (cold open, intro/outro, segments, ad slots), assemble and edit, clear music and SFX rights, hit podcast loudness and delivery standards, ship metadata/chapters/transcripts through RSS, and pass platform AI-content policy and QA before publishing. Use this skill whenever the deliverable is a podcast episode or a podcast show bible, or when an agent must make production, rights, disclosure, or delivery decisions for spoken-word audio. Not for music tracks, single-voice notification prompts, or video where picture leads (route audio-for-video to a video skill).
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Provider-independent production design direction for AI-generated media. Use when translating narrative, brand, advertising, product, social, film, or worldbuilding intent into sets, locations, props, color and material palettes, era research, graphic and environmental design, continuity bibles, image/video prompt planning, reference strategy, production constraints, iteration notes, handoff specs, and QA.
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Validate system resilience through controlled fault injection. Covers hypothesis-driven chaos experiments, failure injection types (network, service, infrastructure, dependency), LitmusChaos/Chaos Mesh/AWS FIS/Gremlin/toxiproxy tooling, automated abort gating, game day planning, and progressive chaos adoption. Use when: "chaos engineering," "fault injection," "resilience test," "game day," "failure recovery," "system reliability," "blast radius." Not for: safe rollout flags/canary/dark launch during a release — use testing-in-production; designing new tests from production telemetry — use observability-driven-testing. Related: testing-in-production, observability-driven-testing, performance-testing, release-readiness, test-environments.
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Turn a vague bug report into a VERIFIED minimal reproduction and then a failing regression test, agent-driven end to end. Covers extracting the implicit repro from a thin report (env, build, steps, data), the reproduce-minimize-isolate-capture loop, git bisect to find the introducing commit, building a deterministic minimal repro (fixed seeds, frozen time, stubbed network), writing the failing regression test BEFORE the fix (red) and confirming the fix flips it green, and writing repro evidence back into the ticket. Distinguishes flaky-not-reproducible from environment-specific. Use when: "reproduce this bug," "minimal reproduction," "repro steps," "find the commit that broke it," "git bisect," "make the repro deterministic," "write a failing test for this bug," "regression test for a defect," "can't reproduce this bug." Not for: Classifying/deduplicating/severity-routing existing failures without reproducing them — that is ai-bug-triage. Generating tests from specs rather than from a defect — that is ai-test-generation. Related: ai-bug-triage, ai-test-generation, test-reliability, systematic-debugging, qa-project-context.
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Production deployment patterns for MCP servers including Docker, cloud platforms, monitoring, and scalabilityUse when "mcp deployment, deploy mcp server, mcp docker, mcp production, mcp monitoring, mcp, deployment, docker, production, monitoring, scaling" mentioned.
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Production incident response - from detection through resolution to post-mortem. Effective communication, systematic investigation, and blameless learning from failuresUse when "incident, outage, production issue, site down, on-call, post-mortem, war room, severity, pages, alerts, rollback, incident, outage, on-call, post-mortem, production, reliability, SRE, communication" mentioned.
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The craft of producing creative assets that communicate brand, product, and message effectively. Creative communications bridges strategy and execution—translating brand positioning and marketing goals into visual, audio, and interactive experiences that resonate with audiences. This skill covers creative briefing, creative production workflows, asset creation, creative feedback, and cross-channel creative adaptation. Great creative communications amplifies strategy; it doesn't replace it. Use when "creative, creative brief, assets, video, motion, animation, creative production, photoshoot, visual content, creative review, ad creative, creative feedback, creative, production, video, motion, assets, briefs, feedback, visual-content" mentioned.
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The new frontier of audio: AI-generated music with Suno and Udio, AI sound effects with ElevenLabs, AI voice cloning, and AI audio enhancement. This skill covers the full spectrum of neural audio synthesis and manipulation. We're in an era where a single person can produce broadcast-quality audio content that would have required studios, musicians, and engineers. AI doesn't replace musical talent—it democratizes production capability. Original compositions in minutes. Custom sound design in seconds. Voice in any language instantly. The practitioners of this skill understand both the creative potential and the ethical considerations. They know when AI audio enhances creativity and when it needs human touch. They're audio directors who orchestrate AI tools like instruments in a symphony. Use when "AI music, generate music, Suno, Udio, AI audio, AI sound, generate soundtrack, background music, sound effects, audio generation, jingle, AI score, stem separation, ai-audio, music, suno, udio, sound-effects, soundtrack, generation, audio-branding, production" mentioned.
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Checks production-bound code for observability, error handling at system boundaries, input validation, graceful degradation, and resource management, scaled to the project's real stakes. Use before declaring a production feature done.
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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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Suede-owned marketing image production for generation prompts, hero and social graphics, product mockups, export sizing, compression, and preview assets. Use when the user needs a general-purpose marketing image or an image-production workflow. NOT FOR: paid-ad creative systems (use suede-ad-creative), video production (use suede-video), or app-store listing strategy (use suede-aso).
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Analyze production Agentforce agent behavior using session traces and Data Cloud, and manage Agent Health Monitoring (AHM) alerts. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries; creates, lists, updates, or deletes an AHM data alert on an agent metric (escalation rate, deflection, etc.); asks why an alert is not firing or wants to check whether alerts have fired (notification counts, or the per-alert Incidents view). DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.
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Error-handling, security, logging and performance bar for production code. Use when writing or changing production code, before the run ends.
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Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.
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