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概要と使いどころ
Serena code intelligence — LSP-powered symbol navigation, diagnostics, and targeted code surgery. Activate before complex refactors, cross-file analysis, or when graph tools need symbol-level depth.
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Create graph-informed implementation plans before writing code. Use before any non-trivial task. Skip for small changes (≤2 files, <20 lines, no hub/bridge).
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。
Convert current conversation context into a structured Product Requirements Document (PRD) and optionally post it to GitHub Issues. Use before /supergraph:plan when requirements came from a conversation, user story, or feature discussion rather than a formal spec.
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 when a task needs the judgment of a demographer — building a cohort-component population projection, reconciling fertility/mortality rate estimates against ACS or vital-statistics counts, judging whether a population trend is real or sampling/measurement noise, sizing a census undercount adjustment, or advising a public agency on age-structure-driven facility or service demand.
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 when a task needs the judgment of a Diagnostic Medical Sonographer — deciding whether a duplex, compression, or graded-compression ultrasound study is diagnostic versus technically limited, choosing when to interrupt a scanning protocol to escalate a real-time critical finding, setting scan technique and ALARA (TI/MI) parameters for a given exam type, or drafting the technical worksheet and preliminary findings a physician will interpret and sign.
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 when a task needs the judgment of a Cartographer/Photogrammetrist — designing an aerial or LiDAR acquisition flight plan against a stated accuracy class, running an NSSDA checkpoint accuracy test on a delivered orthophoto or DTM, choosing a horizontal/vertical datum and geoid model for a georeferencing project, diagnosing why a state-plane grid distance disagrees with a field-surveyed ground distance, or specifying LiDAR point-density and classification requirements for a bare-earth deliverable.
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 when a task needs the judgment of a choreographer — building and teaching a dance number against a rehearsal-hour budget, staging repertory under a trust/estate's licensing rules, planning a tech-week spacing and cueing schedule, or diagnosing why a piece isn't reading from the audience.
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 when a task needs the judgment of an Anthropologist/Archeologist working in cultural resource management (CRM) — scoping a Section 106 Phase I identification survey, designing a shovel-test-pit sampling strategy, evaluating National Register of Historic Places eligibility under Criteria A-D, interpreting excavation stratigraphy via a Harris matrix and radiocarbon results, or running the ethnographic field-methods branch (participant observation, informant interviewing) for a cultural-anthropology component of a project.
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。
Improve an existing agent SKILL.md through one gated cycle: select the target (default this skill), snapshot the committed baseline, diagnose against skill-design-principles plus four rubric dimensions, apply exactly one focused change, and ship only if a different agent than the author PASSes with quoted evidence. Use when the user says "improve this skill", "improve-skill", "iterate on SKILL.md", or "apply skill-design-principles to a skill". Do not use for code slop over a duration (anneal), generic timeboxed work (timeboxed-iterating), graph orchestration (workgraph), metric hill-climb on a codebase (autoresearch), or stripping slop from existing prose (catalog-deslop).
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。
Run work as an explicit event-driven state machine executed by subagents. The orchestrator is the runtime from the canonical loop — `state, effects = init(machine); execute(effects); while state is active: event = nextEvent(); state, effects = transition(machine, state, event); execute(effects); return state.output`. All routing judgment is authored ONCE, at plan time, into a machine file (states, event alphabet, transition table, budgets, final states); at runtime the orchestrator does pure table lookups and dispatches effects — it never decides, never authors, never ingests artifacts. Subagent/sync protocol is the timeboxed-iterating one: role prompts scaffolded once to disk and dispatched BY PATH, an initialiser preamble every subagent reads first, a self-populating cheatsheet, per-unit files, tiny structured returns, independent measurement gate. Use when the user says "workmachine", "run this as a state machine", "state-machine this", or when a goal has a small number of clearly named phases with explicit outcomes (built / passed / failed / blocked) and you want the routing to be auditable rather than judged on the fly. For clock-driven loops use timeboxed-iterating; for wide graphs with judged edge criteria use workgraph.
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。
Build the internal link graph for a site, run PageRank-style authority distribution, detect orphan pages, and recommend new internal links via embedding-based semantic similarity (not keyword matching)..
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Generate rights-cleared paid-ad product photography variants from an authorized source image and validated brand profile. Triggers on: product photo, product photography, virtual photoshoot, photoshoot, enhance product image, studio shot, lifestyle shot, lifestyle product image, floating product image, ingredient shot.
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 when 用户想根据论文、科研文档、研究文本、图注、开题/答辩材料或具备读图条件的参考图生成可复制的 AI 科研绘图提示词；触发场景包括论文配图、科研绘图、图形摘要、Graphical Abstract、机制图、技术路线图、实验装置图、模型架构图、多面板对比图、汇报总览图、参考图风格迁移、未知领域母版生成，或上传/引用 PDF、Word、LaTeX、Markdown、摘要、方法段、图注、课题说明、研究方案、参考图片。Also use when the user asks for scientific figure prompts, research illustration prompts, graphical abstract prompts, mechanism diagrams, technical roadmaps, experimental setup diagrams, model architecture diagrams, multi-panel scientific figures, paper/conference figures, or AI image prompts based on papers, research documents, abstracts, methods sections, figure captions, proposals, reference images, PDFs, Word docs, LaTeX files, Markdown files, or research plans.
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 when designing or documenting a lead scoring model in Salesforce Sales Cloud or Account Engagement: qualifying criteria, MQL/SQL threshold definitions, scoring dimensions (demographic, firmographic, behavioral), and sales handoff SLA. NOT for configuring the scoring, grading, and automation rules in Account Engagement — use admin/mcae-lead-scoring-and-grading. NOT for the whole marketing program's lifecycle stages and platform scope — use admin/marketing-automation-requirements.
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 นักออกแบบท่าเต้น — choreography, 8-count breakdown, formation, music cut, dance style รองรับทุก genre
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。
นักเขียนอักษรประดิษฐ์ — Thai + Latin calligraphy, hand lettering, signage, wedding invitation, lettering artwork
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。
Ocean science expert covering physical, chemical, biological, and geological oceanography
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。
Photography expert for composition, lighting, camera settings, and post-processing techniques
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 when creating high-quality scientific or medical infographic images or prompts, including review figures, disease maps, teaching diagrams, mechanism summaries, classification overviews, timelines, matrices, and academic visual explainers. If the user asks to generate the image, use ChatGPT image generation.
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。
Develop an independent visual language and turn the human-chosen direction into a production-ready graphic, brand, campaign, publication, web, or product system. Use when originality and differentiation matter enough to escape generic AI and Big Tech aesthetics. Guides a minimum-evidence start, ten isolated typography-led HTML design directions made only from type, color, and space, visual-first human judgement, explicit commitment, and concrete Round 2 production. Do not use for routine UI fixes, small components, or applying an existing design 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。
Builds dependency graph, detects cycles (DFS), validates boundary rules, calculates coupling metrics (Ca/Ce/I, CCD/NCCD). Adaptive architecture detection.
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。
Verify and write proofs using the Acorn theorem prover for mathematical and cryptographic formalization. Use when working with Acorn proof files (.ac), verifying theorems, formalizing mathematical or cryptographic protocols, or writing proofs in the Acorn language. Triggers on: (1) Creating or editing .ac files, (2) Running acorn verify commands, (3) Formalizing math or crypto proofs, (4) Questions about Acorn syntax or standard library.
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。
host / 辅助段优化模块（L3）：把**非 DiT、非解码段**的固定开销落成可复验的优化—— ① 交付与搬运（mp4/图片编码、worker→API 通路、落盘、异步化）； ② 装载与预热（权重加载、编译与图下发预热、镜像/容器预热）。 当用户说“mp4 编码/传输/落盘慢”“交付这段占了不少时间”“模型加载慢”“首启编译太久” “固定开销占比高”“每个请求都要重新编码/重新写盘”时使用。 **入口条件**：占比已达门限（瓶颈标签 `非DiT-host段`；标签枚举、门限与测点口径的单一真源在 model-auto-optimization/references/bottleneck-labels.md，本技能只消费不定义）。 near-miss（看似相关但不属本技能）： - **并发 / 吞吐 / 批处理调度 / 队列深度与限流** → **不属本技能**（会改变所有性能口径与 SLO 语义， 未来独立立项；本技能只在**不改变请求并发模型**的前提下压固定开销）； - VAE/TAE 解码段（计算或通信）→ `vae-opt`；DiT 计算 → `dit-perf-opt`； 多卡并行 / 通信掩盖 → `dit-parallel-opt`； - **装库 / 下载权重 / 构建镜像 / 创建容器** → `env-install`（本技能只优化装载与预热**过程本身**）； - 编译与图下发的**机制本体**（pattern / Inductor / NPUGraph / 图 pool / 缓存条目）→ `pattern-dev` / `aclgraph-dev`（本技能不另做一套缓存）； - 产物一致性与精度判据 → `accuracy-gate`；收益口径 → `perf-gate`； - 瓶颈未定位 / 没有占比锚点 → `model-auto-optimization`。
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。
Drafts U.S. civil litigation Answers with paragraph-by-paragraph admissions/denials, affirmative defenses, and counterclaim triage. Trigger when user needs a responsive pleading to a complaint, mentions affirmative defenses, admit/deny responses, Rule 8, or insufficient knowledge responses.
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。