3,772 件 ・ 関連度順
概要と使いどころ
Add persistent memory to Claude Code that survives across sessions. Use when: maintaining continuity across Claude Code sessions, building agents with persistent project memory, avoiding repeated context setup. Covers claude-mem (AI-compressed session logs) and Claude Subconscious (Letta-based background agent).
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。
Graphile Worker expert for high-performance PostgreSQL job queues with trigger-based job creation and millisecond job pickup via LISTEN/NOTIFY. Use when "graphile worker, postgres trigger job, listen notify queue, postgraphile worker, database trigger queue, transactional job, graphile-worker, postgresql, triggers, listen-notify, job-queue, postgraphile, high-performance, supabase" mentioned.
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。
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memoryUse when "conversation memory, remember, memory persistence, long-term memory, chat history, memory, conversation, persistence, context, llm" mentioned.
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。
知乎 AI 自媒体博主 (知乎 AI 自媒体博主 (从业者视角) — 在知乎做『AI 赛道』内容的自媒体博主，重点是**怎么写文案、怎么制作内容**。覆盖: (a) AI 赛道选题 — AI 工具实测 / 大模型解读 / prompt 工程 / AI 副业与变现 / AI 编程 / AI 行业分析 / AI 资讯解读，按知乎用户(偏高学历、理性、求深度、爱较真)重构选题; (b) 文案与内容结构(核心) — 知乎体回答/文章(开头钩子『先说结论/抛反常识/讲故事』、逻辑论证、数据与案例、分点小标题、配图与代码块、结尾升华或金句)、标题(问题型/数字型/反差型/痛点型)、专业感与『讲人话』的平衡、人设 voice、引用与举证(知乎读者吃『有理有据』); (c) 内容制作工作流 — 找问题/选题(热榜/搜索流量词/对标高赞)→搭框架(论点+论据+案例)→写初稿→配图配数据→排版→发布→盐选/付费咨询/带货沉淀; (d) AI 工具用于内容生产 — 找选题与资料(GPT/Claude/Kimi/秘塔)、写作辅助与改写、配图(即梦/MJ)、代码与图表、英文一手编译; (e) 平台机制与涨粉 — 知乎推荐算法(赞同/收藏/想法/盐值/专业徽章/搜索长尾)、热榜与问题流量、好物推荐/知乎带货、盐选专栏、创作者激励、垂直度与权重、避坑(折叠/限流/广告判定); (f) 变现(次要) — 盐选专栏 / 好物推荐带货 / 付费咨询 / 知乎知学堂引流 / 私域 / 商业合作。学派分歧: 长答案深度干货 vs 短平快蹭热榜、专业严肃 vs 故事化通俗、原创 vs 编译搬运洗稿、真人专业人设 vs AI 批量生产、回答优先 vs 文章优先、纯涨粉 vs 直奔盐选变现。不含: 通用知乎运营、电商带货全流程、其他平台(小红书/推特另有 sister skill)、AI 工具本身技术原理。) Master OS — automated mastery of 知乎 AI 自媒体博主 (从业者视角) — 在知乎做『AI 赛道』内容的自媒体博主，重点是**怎么写文案、怎么制作内容**。覆盖: (a) AI 赛道选题 — AI 工具实测 / 大模型解读 / prompt 工程 / AI 副业与变现 / AI 编程 / AI 行业分析 / AI 资讯解读，按知乎用户(偏高学历、理性、求深度、爱较真)重构选题; (b) 文案与内容结构(核心) — 知乎体回答/文章(开头钩子『先说结论/抛反常识/讲故事』、逻辑论证、数据与案例、分点小标题、配图与代码块、结尾升华或金句)、标题(问题型/数字型/反差型/痛点型)、专业感与『讲人话』的平衡、人设 voice、引用与举证(知乎读者吃『有理有据』); (c) 内容制作工作流 — 找问题/选题(热榜/搜索流量词/对标高赞)→搭框架(论点+论据+案例)→写初稿→配图配数据→排版→发布→盐选/付费咨询/带货沉淀; (d) AI 工具用于内容生产 — 找选题与资料(GPT/Claude/Kimi/秘塔)、写作辅助与改写、配图(即梦/MJ)、代码与图表、英文一手编译; (e) 平台机制与涨粉 — 知乎推荐算法(赞同/收藏/想法/盐值/专业徽章/搜索长尾)、热榜与问题流量、好物推荐/知乎带货、盐选专栏、创作者激励、垂直度与权重、避坑(折叠/限流/广告判定); (f) 变现(次要) — 盐选专栏 / 好物推荐带货 / 付费咨询 / 知乎知学堂引流 / 私域 / 商业合作。学派分歧: 长答案深度干货 vs 短平快蹭热榜、专业严肃 vs 故事化通俗、原创 vs 编译搬运洗稿、真人专业人设 vs AI 批量生产、回答优先 vs 文章优先、纯涨粉 vs 直奔盐选变现。不含: 通用知乎运营、电商带货全流程、其他平台(小红书/推特另有 sister skill)、AI 工具本身技术原理。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 知乎 AI 自媒体博主 (从业者视角) — 在知乎做『AI 赛道』内容的自媒体博主，重点是**怎么写文案、怎么制作内容**。覆盖: (a) AI 赛道选题 — AI 工具实测 / 大模型解读 / prompt 工程 / AI 副业与变现 / AI 编程 / AI 行业分析 / AI 资讯解读，按知乎用户(偏高学历、理性、求深度、爱较真)重构选题; (b) 文案与内容结构(核心) — 知乎体回答/文章(开头钩子『先说结论/抛反常识/讲故事』、逻辑论证、数据与案例、分点小标题、配图与代码块、结尾升华或金句)、标题(问题型/数字型/反差型/痛点型)、专业感与『讲人话』的平衡、人设 voice、引用与举证(知乎读者吃『有理有据』); (c) 内容制作工作流 — 找问题/选题(热榜/搜索流量词/对标高赞)→搭框架(论点+论据+案例)→写初稿→配图配数据→排版→发布→盐选/付费咨询/带货沉淀; (d) AI 工具用于内容生产 — 找选题与资料(GPT/Claude/Kimi/秘塔)、写作辅助与改写、配图(即梦/MJ)、代码与图表、英文一手编译; (e) 平台机制与涨粉 — 知乎推荐算法(赞同/收藏/想法/盐值/专业徽章/搜索长尾)、热榜与问题流量、好物推荐/知乎带货、盐选专栏、创作者激励、垂直度与权重、避坑(折叠/限流/广告判定); (f) 变现(次要) — 盐选专栏 / 好物推荐带货 / 付费咨询 / 知乎知学堂引流 / 私域 / 商业合作。学派分歧: 长答案深度干货 vs 短平快蹭热榜、专业严肃 vs 故事化通俗、原创 vs 编译搬运洗稿、真人专业人设 vs AI 批量生产、回答优先 vs 文章优先、纯涨粉 vs 直奔盐选变现。不含: 通用知乎运营、电商带货全流程、其他平台(小红书/推特另有 sister skill)、AI 工具本身技术原理。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「知乎 ai 博主」「知乎 ai 自媒体」「知乎 ai 赛道」「知乎高赞回答」「知乎 ai 文章」
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。
软件架构 (含 应用架构 / 系统架构 / 分布式 后端架构 / 前后端 数据流 与 数据模型 设计 / API 边界 / 可扩展性 / 演进式架构 — 不含 硬件架构 / 纯企业架构 / 纯 DevOps / 纯安全架构) (software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines).) Master OS — automated mastery of software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines).: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines). problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「软件架构」「系统设计」「system design」「应用架构」「service design」
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。
Listen to generated music by measuring it and by reading it as sheet music: render Strudel code or record a Web Audio page through the real engine in headless Chromium, then read spectrograms, pYIN pitch lines, chroma, roughness and semitone-rub meters; engrave notes as a score with beat-by-beat harmony; transcribe a melody from audio; read MusicXML/MIDI/ABC or a score image into notes and into Strudel code. Use to check whether generated music sounds right or better (before/after a change), to match a reference track, spectrogram image or score, or to find discordant chords, a buried melody, clipping or muddy mixes. Triggers on 'does it sound better', 'listen to', 'render and check', 'spectrogram', 'sfft/stft', 'discordant', 'clashing chords', 'sounds off', 'compare to the original', 'reproduce this track in Strudel', 'sheet music', 'score', 'notation', 'MusicXML', 'MIDI', 'ABC', 'transcribe', 'OMR'. Pairs with strudeling (writing Strudel); for format conversion use processing-video.
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。
Designs Amazon A+ Content (formerly Enhanced Brand Content) — the 8 module suite that appears below the standard product carousel for Brand Registered sellers. Covers Hero Banner (21:9 / 2388×1024), Pain Points / Selling Points / Technology / Data / How-to-Use / Variants (3:2 / 1536×1024), and Endorsement (21:9), with mobile safe-area rule, 30pt text floor, and cross-module consistency anchored to the main image URL. Use when the user says "A+ page", "A+ content", "Amazon A+", "Brand Content", "Enhanced Brand Content", "亚马逊详情页", "A+ 详情页", "21:9 banner", "Hero Banner", "Amazon detail page module", or any request for the brand-registered detail-page module suite. For the 1:1 main image and carousel secondary images, see the `product-shots-main-image` skill.
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。
Generates 9 consistent multi-angle fashion-editorial portraits from a single reference image, with locked identity (face/skin/eyes), preserved hairstyle structure, faithful outfit/accessories, and a unified photography style across all frames. Use when the user says "multi-angle", "multi-angle shots", "九连拍", "多角度九连拍", "9-angle portraits", "fashion lookbook", "model consistency series", "consistent portraits from one photo", "generate 9 angles of this model", or "e-commerce model multi-angle pack". Part of the product-shots ecosystem for cross-border e-commerce apparel and accessory listings.
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。
After scaffolded practice, run an unassisted check — a problem with no AI help. Separates what the learner can do with support from what they can do independently. Critical for preventing phantom attainment.
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。
When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms, or wants to do social listening and engagement triage. Also use when the user mentions 'LinkedIn post,' 'Twitter thread,' 'social media,' 'content calendar,' 'social scheduling,' 'engagement,' 'viral content,' 'what should I post,' 'repurpose this content,' 'tweet ideas,' 'LinkedIn carousel,' 'social media strategy,' 'grow my following,' 'TikTok video,' 'Reels,' 'Shorts,' 'video script,' 'video hook,' 'short-form video,' 'create a reel,' 'social listening,' 'brand mentions,' 'competitor monitoring,' 'top posts to comment on,' or 'find people asking for.' Use this for social media content creation, repurposing, scheduling, short-form video scripting, and social listening. For broader content strategy, see content-strategy. For paid ads, see ad-creative. For earned media, see public-relations.
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。
Data persistence patterns in Go covering raw SQL with sqlx/pgx, ORMs like Ent and GORM, connection pooling, migrations with golang-migrate, and transaction management. Use when implementing database access, designing repositories, or managing schema migrations.
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Analyzes and describes vulnerabilities for specific assets and threats within an insurance company risk register context. The output must be highly professional, eloquent, and grammatically impressive, utilizing labelled headings and subheadings.
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Distributed storage systems design and operation for cloud platforms. Covers the GFS/HDFS block-and-master pattern, object storage (Swift/S3) with consistent hashing and eventual consistency, block storage semantics, replication vs erasure coding, the CAP theorem in practice, read-repair and anti-entropy, snapshot chains, and the GFS/BigTable/Spanner evolution. Use when designing a storage subsystem, choosing between object/block/file, or reviewing a replication and consistency strategy.
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The **Coherence Composer** is an epistemological skill that validates structural consistency and explores counterfactual worlds while respecting constraints from both **Commitment Tracker** (ontology) and **Opacity Detector** (epistemology).
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> Persistent belief revision log wiring in-memory AGM BeliefSet to DuckDB time-travel storage. Triggers: belief revision, AGM postulates, time-travel query, belief history, AS-OF queries on propositions, persistent belief state, append-only belief log. >>>>>>> origin/main
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 before any CLI/TUI display code is written, modified, or audited — runs the 7-stage discipline (Context, Register, Shape brief, Implement, Critique, Audit, Polish) for Typer, Rich, Textual, and Questionary work, grounded in per-project PRODUCT.md and DESIGN.TUI.md/DESIGN.md. Triggers on output formatting, display design, interactive prompts, visual consistency, TUI layout, progress display, dashboard design, design audit, design polish, design critique, shape brief, register decision (brand-cli vs product-cli), and AI-slop checks.
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OS post-exploitation after a stable host foothold: quiet host recon, host-native persistence (Windows Run/tasks/services/COM/WMI and Linux SSH/cron/systemd/SUID), long-term C2 (Sliver primary, Mythic/Havoc optional), targeted collection and exfil, and engagement cleanup. Does not own AD/cloud/K8s native persistence, Ligolo pivoting, LSASS dumping, or EDR bypass. Operator selects objectives; success is a live callback, verified persistence, delivered loot, or restored host — not generating an unused implant.
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Reverse shell generation and listener management. Generates platform-specific reverse shell payloads (bash, python, php, powershell, java, ruby, perl, netcat, socat, msfvenom) and sets up listeners in the Kali container. Supports one-liner generation, encoded payloads for WAF/filter bypass, listener setup with session capture, and shell stabilization. Chains from /pentester, /metasploit, or /post-exploit when command execution is confirmed.
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。
Hunts for malicious scheduled task persistence by analyzing task registration events (4698) and schtasks process creation for suspicious actions, triggers, and hidden tasks. Activates for requests to hunt scheduled task abuse, detect malicious schtasks usage, or find persistence via the Task Scheduler.
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。
Diagnose failures in SKMTC sessions — no output, wrong output, error messages, failed bundle builds, parseIssues, "Registered definition mismatch", ref cycles, "Module not found" in generated code, or any other broken behavior. Applies across both CLI usage and generator authoring contexts. Use this skill when the user asks "why isn't my generator working", "no output for X", "wrong output", "what does this error mean", "manifest says X", "bundle failed", "INVALID_SCHEMA", "INVALID_DEPENDENCY_REF", "Registered definition mismatch", "Module not found" (in generated code), "ConfigValidationError", or reports any other SKMTC failure. This skill encodes a **verify-first epistemic stance** — read the manifest, check parseIssues, reproduce the failure before proposing fixes. Distinct from `skmtc-cli` and `skmtc-generator` which guide *doing*; this skill guides *diagnosing*.
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。
Mechanical consistency check for game design — answers "is the design consistent with itself?" — plus changed-scope impact analysis across all three GDD axes: systems, flows, and content types. Offline and deterministic: greps the facts registry against the documents (numbers, terms, IDs, duplicate & dangling references, roster↔disk, map freshness, Depends 3-way, status/version, AC presence, placeholder leaks, flow & content schema), and from a git diff computes which dependent systems, flows, and content a change affects. Scope is inferred: a named document checks that document; an unverified diff triggers a changed-scope pass; otherwise everything. Use when the user wants a consistency or impact check, e.g. "verify the design", "what did this change affect", "find broken references in the design". Conflicts triage into four tracks; the entailed fixes hand off to /unikit-gd-apply in one pass. This is the mechanical pass — for a subjective "is this design good" quality critique use /unikit-gd-review.
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。
iOS Core Data architect for persistent storage, CloudKit sync, schema migrations, and SwiftData migration. Activate on: Core Data, NSManagedObject, CloudKit sync, Core Data migration, NSFetchedResultsController, NSPersistentContainer, SwiftData migration path. NOT for: SwiftUI state management (use swiftui-data-flow-expert), server databases (use data-pipeline-engineer), SQLite direct (use mobile-offline-sync-architect).
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What you can build and do with an always-on AI agent that has episodic memory. Covers concrete product ideas, workflows, emergent capabilities from persistence plus memory, and real-world examples of deployed persistent agents. Helps you go from "I have the architecture" to "here's what it actually does for me." Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).
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This skill suggests administering a glucocorticoid antagonist (e.g., mifepristone) in patients with Cushing's disease who have diabetes or glucose intolerance and are not surgical candidates or have persistent disease after transsphenoidal surgery. Consider when encountering persistent hypercortisolism after TSS, contraindications to surgery, or comorbid diabetes/glucose intolerance requiring medical control.
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。