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Use when a Scenario user wants to report a bug or request a change, on the public tracker at github.com/scenario-labs/skills or by email to support@scenario.com: turning a failed generation, MCP tool error, or unclear skill into a reproducible report, collecting trace ids with diagnostics_run, stripping keys, signed asset URLs, and personal data, asking before sharing a team or project id, or building a prefilled issue URL. Keywords: file an issue, feature request, feedback.

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Use when generating or editing images with OpenAI's GPT Image models on Scenario via MCP: text-to-image, edits from reference images, inpainting with an alpha mask, in-image text for logos and infographics, transparent cutouts, pixel sizing up to 4K, quality tiers up to xhigh and max, input fidelity for product or face detail, or choosing between Flare, Sunburst, GPT Image 2 and 1.5. Keywords: GPT Image 2.5, GPT Image 2, OpenAI, DALL-E, gpt-image, ChatGPT image, txt2img, img2img, background.

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Use when the user wants to compare or benchmark multiple LLMs/agents arena-style but it's unclear which specific workflow fits — a general-purpose win-rate comparison on a custom task, or a benchmark specifically about reference/citation hallucination rate. Also use when the user mentions model arena, agent arena, pairwise model comparison, win-rate ranking, or comparing models on a task and hasn't specified whether that task is generic or about citation accuracy. This skill is the entry router for the arena-eval suite: it asks one diagnostic question when needed, then recommends the workflow or workflows needed to cover the request.

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Use when you need framework-agnostic OpenAPI 3.x guidance — spec structure, metadata and versioning, paths and operations, reusable schemas, security schemes, examples, documentation quality, contract validation (e.g. Spectral), breaking-change awareness, and handoffs to codegen — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Review an OpenAPI; Improve an OpenAPI; Improve API contract; Improve API schema design. Part of Plinth Toolkit

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Wiki-first "what do I use" lookup - pick the automated TOOL (wiki/tools/), then the PAYLOAD/technique (wiki/payloads/ + wiki/cheatsheets/), for a surface/service/vuln-class BEFORE hand-rolling or working from memory. Use for "tool for <service>", "automated tools for web/<service>", "what should I run on <surface>", "which tool for <X>", "payloads for <X>", "payload arsenal", "cheatsheet for <X>", "how do I exploit <tech/class>", "exploit/attack chain for <X>", "arsenal", any SSRF/XSS/SQLi/SSTI/LFI/JWT/XXE/IDOR/NoSQL/deserialization/CSRF/CORS/CRLF/GraphQL/smuggling/web-cache/OAuth/SAML/MFA/crypto/LDAP/XPath/WebAuthn/file-upload/IMDS/prompt-injection/Modbus ask, plus "privesc arsenal", "CVE arsenal", "default creds", "nuclei templates", "sqlmap/hydra/nmap/bloodhound", "password attacks".

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Use when the project follows API-first / OpenAPI-first approach: generating controller interfaces, DTOs, and clients from an OpenAPI spec. Use when you see openapi.yaml, openapi-generator-maven-plugin, or ApiDelegate pattern in the project.

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ニュースヘッドラインを入力として18ヶ月シナリオを分析するスキル。 scenario-analystエージェントで主分析を実行し、 strategy-reviewerエージェントでセカンドオピニオンを取得。 1次・2次・3次影響、推奨銘柄、レビューを含む包括的レポートを日本語で生成。 使用例: /scenario-analyzer "Fed raises rates by 50bp" トリガー: ニュース分析、シナリオ分析、18ヶ月展望、中長期投資戦略

Generate, edit, composite, stream, and production-review still images with OpenAI GPT Image models. Use when an agent must choose between GPT Image 2 and legacy GPT Image/DALL-E integrations, select the Image API or Responses API, build prompts and reference-image workflows, use masks or multiple inputs, preserve identity or brand details, render in-image text, handle output files and partial images, estimate cost and rate limits, migrate deprecated image code, or apply OpenAI image safety, consent, privacy, provenance, and rights requirements. Do not use for OpenAI video or audio generation.

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B2B 大客户销售 (B2B 大客户销售 / 企业级复杂销售 (Enterprise B2B Sales / Complex Selling) — 面向企业客户的高价值、长周期、多干系人复杂销售的职业认知操作系统，从业者(AE/客户经理/SE 售前/销售负责人 VP Sales/CRO)、想入行者、以及服务这行(销售咨询/enablement/RevOps)的视角。覆盖: (a) 第一性张力 — 三大方法论流派之争: **买方主导的诊断式提问 (consultative/discovery-led: SPIN 提问 → 痛点 → 方案, 「像医生先诊断再开方」) ⇄ 卖方主导的教学式挑战 (Challenger: Teach-Tailor-Take control, 用商业洞见 reframe 客户认知, 「像老师挑战客户的既有想法」) ⇄ 资格审查纪律 (MEDDIC/MEDDPICC: 先严格 qualify/disqualify, 「no deal 好过 bad deal」)**; 三派不是非此即彼, 资深人混用, 但底层世界观分歧真实(问 vs 教 vs 筛); 更深层张力 — 「过程/方法论纪律 (process rigor: 预测准确、deal inspection、qualify) ⇄ 关系/信任 (relationship: champion 培养、高管关系、人情)」, 「卖结果/业务价值 (business outcome/ROI、卖给经济决策人) ⇄ 卖产品/功能 (feature selling)」, 「多线程触达买方委员会 (multithreading buying committee) ⇄ 单一 champion 依赖」; (b) 方法论正典 (最标准化、最易蒸出高质量+CLI 化) — MEDDIC/MEDDPICC (Metrics 量化指标/Economic buyer 经济决策人/Decision Criteria 决策标准/Decision Process 决策流程/Identify Pain 痛点/Champion 内部拥护者 + Paper process 签约流程/Competition 竞争), SPIN (Situation/Problem/Implication/Need-payoff 提问序列), Challenger (商业洞见 commercial insight + reframe + 建设性张力 constructive tension + Mobilizer 调动者), Solution Selling (痛点链 pain chain/愿景创建/buying vision), Sandler (前置合约 up-front contract/痛苦漏斗 pain funnel/潜艇七步), Command of the Message (Force Management: 业务价值框架/before-after/decision criteria), Gap Selling (Keenan: 现状 current state → 期望 future state → 差距 the gap/问题中心), Miller Heiman Strategic Selling (蓝表 blue sheet/buying influences: 经济/用户/技术买家 + coach), Value Selling, BANT (老式资格框架, 多被 MEDDIC 取代); (c) 行业结构与角色 — SDR/BDR (开发/约见) → AE/客户经理 (成交) → SE/售前/Solutions Engineer (技术验证/POC/demo) → CSM (客户成功/续约扩张) → Sales Manager/VP Sales/CRO; RevOps (收入运营), Sales Enablement (赋能); land and expand (先落地再扩张)、量化指标 quota/OTE(目标总收入)/配额达成率/pipeline coverage(管道覆盖率 ~3x 业内经验法则)/win rate(赢率)/sales velocity(销售速度)/ramp time(爬坡期)/ACV·ARR·NRR(年合同额/年经常性收入/净收入留存); (d) 销售周期 (the deal/opportunity lifecycle) — prospecting 开发 → discovery 需求挖掘 → qualification 资格审查(MEDDIC) → demo/technical validation/POC 技术验证 → business case/value 业务价值与 ROI → proposal 提案 → negotiation 谈判(procurement 采购/法务/security review) → close 成交 → onboarding/expansion 交付与扩张; 干系人: champion 拥护者 vs coach 线人 vs economic buyer 经济决策人 vs blocker 阻碍者; mutual action plan/close plan(双向行动计划/成交计划); (e) 产出物与节奏 — discovery call(需求电话)、demo、business case、proposal/SOW、MAP(mutual action plan)、forecast(预测: commit/best case/pipeline 类别)、pipeline review/deal review/QBR(季度业务回顾)/account plan(客户计划); (f) 职业/招聘/薪酬 — quota/OTE/comp plan(底薪+提成)/accelerator(加速器)/SPIFF/clawback、ramp、President's Club(顶尖销售俱乐部)、SDR→AE→管理 晋升路径、rep turnover(销售流失率高); (g) 争议/批判 — Challenger 是否过誉(CEB 原始数据被质疑、insight selling 批评)、MEDDIC 沦为填表剧场(box-checking theater, 填字段不真 qualify)、活动指标 vs 质量(spray-and-pray 群发/冷电话存废之争)、AI SDR/自动化淹没收件箱(2024-2025 AI BDR 泡沫 + 邮件送达率崩溃 backlash)、SDR 军团模式是否将死(Winning by Design/signal-based GTM 取代)、配额虚高与销售 burnout、季末打折促单、销售方法论的「宗教化」与培训-认证产业链、最大竞争对手其实是「no decision/维持现状」而非对手; (h) 流派/思想谱系 — 提问诊断派(Rackham SPIN/Bosworth&Eades Solution Selling/Keenan Gap Selling) vs 挑战教学派(Dixon&Adamson Challenger/Force Management Command of the Message) vs 资格纪律派(MEDDIC: Napoli&Dunkel 起源/John McMahon/MEDDICC Andy Whyte) vs 关系战略派(Miller Heiman Strategic/Large Account) vs SaaS 现代收入架构派(Aaron Ross Predictable Revenue/Mark Roberge Sales Acceleration Formula/Jacco van der Kooij Winning by Design bowtie/David Skok 指标) vs 开发-心态派(Jeb Blount Fanatical Prospecting/Sales EQ、Anthony Iannarino、Josh Braun) vs 谈判(Chris Voss Never Split the Difference 战术同理心)。不含: B2C/零售/电商导购销售、纯电话客服/客户支持、纯渠道/分销管理(channel 虽相关但聚焦直销复杂销售)、纯 marketing/需求生成(demand gen 虽上游但本 skill 聚焦销售执行)、纯 CSM 客户成功(虽相关但聚焦 new logo + expansion 的销售动作)。) Master OS — automated mastery of B2B 大客户销售 / 企业级复杂销售 (Enterprise B2B Sales / Complex Selling) — 面向企业客户的高价值、长周期、多干系人复杂销售的职业认知操作系统，从业者(AE/客户经理/SE 售前/销售负责人 VP Sales/CRO)、想入行者、以及服务这行(销售咨询/enablement/RevOps)的视角。覆盖: (a) 第一性张力 — 三大方法论流派之争: **买方主导的诊断式提问 (consultative/discovery-led: SPIN 提问 → 痛点 → 方案, 「像医生先诊断再开方」) ⇄ 卖方主导的教学式挑战 (Challenger: Teach-Tailor-Take control, 用商业洞见 reframe 客户认知, 「像老师挑战客户的既有想法」) ⇄

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Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.

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Guide users to manually instrument Java GenAI applications with otel-util-genai. Use when the user needs OTel GenAI manual instrumentation: chat, Agent/Tool/Workflow/ Retrieval spans, voice/multimodal/smart-glasses telemetry. Works with any OTLP backend. Load from GitHub: alibaba/loongsuite-java/skills/otel-util-genai/

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Design and implement multi-tenant SaaS architectures with RLS, tenant isolation, and PostgreSQL / Desain dan implementasikan arsitektur SaaS multi-tenant dengan RLS, isolasi tenant, dan PostgreSQL.

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Evaluates financial and incurrence covenant packages with headroom analysis, definition review, and covenant-lite comparison. Use when analyzing loan covenants, negotiating covenant levels, or assessing borrower flexibility.

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Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

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Track exact provenance for every operation on ENCODE data — tool versions, reference files, scripts, parameters, and timestamps — to enable publication-ready methods writing. Use when the user processes ENCODE files, runs any bioinformatics tool, creates filtered/merged datasets, runs pipelines, performs liftover, uses R/Python/Bash for analysis, or needs to document their analysis chain for reproducibility and publication. Also use when the user says "write me methods" to auto-generate methods sections from the provenance log. This skill implements comprehensive provenance documentation: every tool, every version, every reference file, every parameter, every accession — no shortcuts. Use this skill for ANY processing step, ANY file transformation, ANY analysis operation on ENCODE data.

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Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters. Produces upgrade plans, pre/post-upgrade checklists, maintenance runbooks with gcloud commands, release channel strategy, and troubleshooting guides. Handles node pool upgrade strategies (surge, blue-green), version compatibility, PDB management, and workload-specific concerns (stateful, GPU, operators). Use this skill whenever the user mentions GKE upgrades, Kubernetes version bumps, node pool maintenance, GKE patching, cluster version management, release channel selection, maintenance windows, surge upgrades, stuck upgrades, or any GKE lifecycle management task — even casual mentions like "we need to upgrade our clusters" or "plan our next GKE maintenance" or "our upgrade is stuck." Don't use for GKE cluster creation, application onboarding, general networking/routing setup, or security policy configurations (use gke-basics or relevant GKE skills instead).

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Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types, including the Agent Script language for the new Agentforce Builder (hybrid-reasoning -> logic vs | prompt instructions, @actions/@subagent/@variables references, subagents). Use when creating agents in source control, writing or debugging Agent Script, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'convert an agent topic to a subagent', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship', 'Agent Script logic vs prompt instructions'. NOT for Apex-based agent actions — use agentforce/custom-agent-actions-apex. NOT for clicking the agent together in the Builder UI — use agentforce/agentforce-agent-creation.

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This skill recommends lifelong biochemical testing for recurrence in all patients with Cushing's syndrome after adrenalectomy, except those with resected adrenal adenoma showing CT density <10 Hounsfield units. Use when determining long‑term follow‑up strategy for a post‑adrenalectomy CS patient; triggers include “post‑adrenalectomy Cushing’s syndrome”, “CT density <10 HU”, and “adrenal adenoma resection”.

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This skill guides evaluation for malignancy using imaging in patients with a history of adrenal adenoma (post‑adrenalectomy Cushing’s syndrome) when the adenoma demonstrates a CT density of ≥10 Hounsfield units or when pathology is consistent with possible carcinoma. Trigger phrases include “CT density ≥10 HU”, “possible carcinoma on pathology”, and “post‑adrenalectomy CS patient with adrenal adenoma history”.

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Evolutionary file renaming with wrapper files. Enables renames without hard breaks — references are organically updated through usage.

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Evolutionary file renaming with wrapper files, plus the terse MOVED-stub standard for whole folder/file moves. Enables renames without hard breaks — references are organically updated through usage.

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Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, latest/current/default-model prompting guidance, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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Use this skill when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript, com.google.genai:google-genai for Java, google.golang.org/genai for Go), model selection, and API capabilities.

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Creates and edits CARLA Leaderboard route files — placing waypoints and scenario trigger points interactively from the spectator camera (route_creator, scenario_creator), visualising an existing route in the simulator (route_displayer), summarising it as a table (route_summarizer), capturing the current weather in route format (weather_creator), sorting scenarios by route position (scenario_orderer) and converting 1.0-format routes to 2.x (route_bridge). Use when the user asks to "make a new route", "add a scenario to a route", "visualise/debug a route", "see what's in this route file", or "convert old leaderboard routes".

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