研究与知识
研究、检索、摘要和知识工作
Skills 列表
knowledge-ops
Knowledge base management, ingestion, sync, and retrieval across multiple storage layers (local files, MCP memory, vector stores, Git repos). Use when the user wants to save, organize, sync, deduplicate, or search across their knowledge systems.
affaan-mmarket-research
Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.
affaan-mcontinuous-learning
[DEPRECATED - use continuous-learning-v2] Legacy v1 stop-hook skill extractor. v2 is a strict superset with instinct-based, project-scoped, hook-reliable learning. Do not invoke v1; route continuous learning, session learning, and pattern extraction requests to continuous-learning-v2.
affaan-msearch-first
Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.
affaan-mdeep-research
Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.
affaan-mrecsys-pipeline-architect
Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced For You algorithm. Use this skill whenever the user is building any system that picks "the top K items for a (user, context)" — social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.
affaan-magent-architecture-audit
针对 Agent 及 LLM 应用的全栈诊断工具。全面审计 12 层 Agent 架构栈，精准排查外壳封装退化（wrapper regression）、记忆污染、工具约束失效、隐式修复死循环以及渲染/传输篡改等硬伤问题。按严重程度输出诊断报告，并提供代码优先（code-first）的修复方案。是开发者构建 Agent 应用、自主循环（autonomous loops）或各类 LLM 驱动功能的必备 Skill。
affaan-m
writing-skills
当需要创建新技能、编辑现有技能或在部署前验证技能是否正常工作时使用
obraknowledge-ops
知识库管理、摄取、同步和检索，涵盖多个存储层（本地文件、MCP 记忆、向量存储、Git 仓库）。当用户想要保存、整理、同步、去重或搜索其知识系统时使用。
affaan-mresearch-ops
基于证据的当前状态研究工作流，适用于ECC。当用户需要基于当前公开证据和提供的本地上下文获取最新事实、进行比较、丰富信息或获得推荐时使用。
affaan-mgoogle-workspace-ops
在 Google Drive、Docs、Sheets 和 Slides 中统一操作，将计划、追踪器、演示文稿和共享文档整合为一个工作流界面。当用户需要查找、总结、编辑、迁移或清理 Google Workspace 资产，而无需直接调用底层工具时使用。
affaan-mautonomous-agent-harness
将 Claude Code 转变为一个完全自主的代理系统，具备持久记忆、定时操作、计算机使用和任务队列功能。通过利用 Claude Code 原生的 crons、dispatch、MCP 工具和记忆，替代独立的代理框架（如 Hermes、AutoGPT）。当用户需要持续自主运行、定时任务或自我指导的代理循环时使用。
affaan-mlogistics-exception-management
处理货运异常、运输延误、损坏、丢失和承运商争议的编码专业知识。由拥有15年以上运营经验的物流专业人士提供。包括升级协议、承运商特定行为、索赔程序和判断框架。在处理运输异常、货运索赔、交付问题或承运商争议时使用。
affaan-mvideodb
对视频和音频进行查看、理解与操作。查看——从本地文件、URL、RTSP/直播源或桌面录制中摄取内容；返回实时上下文和可播放的流链接。理解——提取帧，构建视觉/语义/时间索引，并搜索带有时间戳和自动剪辑的时刻。操作——转码和标准化（编解码器、帧率、分辨率、宽高比），执行时间线编辑（字幕、文字/图像叠加、品牌标识、音频叠加、配音、翻译），生成媒体资产（图像、音频、视频），并为直播流或桌面捕获的事件创建实时告警。
affaan-mexa-search
通过 Exa MCP 进行神经搜索，用于网页、代码和公司研究。当用户需要网页搜索、代码示例、公司情报、人员查找或使用 Exa 神经搜索引擎进行 AI 驱动的深度研究时使用。
affaan-mcustoms-trade-compliance
海关与贸易合规领域的专业知识，涵盖海关单证、关税分类、关税优化、受限方筛查以及多个司法管辖区的法规合规。由拥有15年以上经验的贸易合规专家提供。包括HS分类逻辑、国际贸易术语解释通则应用、自由贸易协定利用和处罚缓解。在处理清关、关税分类、贸易合规、进出口单证或关税优化时使用。
affaan-mquality-nonconformance
针对受监管制造业的质量控制、不合格调查、根本原因分析、纠正措施和供应商质量管理的编码化专业知识。由在FDA、IATF 16949和AS9100环境中拥有15年以上经验的质量工程师提供。涵盖NCR生命周期管理、CAPA系统、SPC解读和审核方法论。适用于调查不合格项、执行根本原因分析、管理CAPA、解读SPC数据或处理供应商质量问题。
affaan-msearch-first
研究优先的编码工作流。在编写自定义代码之前，先搜索现有的工具、库和模式。调用研究员智能体。
affaan-mdeep-research
使用 firecrawl 和 exa MCP 进行多源深度研究。搜索网络、综合发现，并提供带引用的报告及来源归属。当用户希望对任何主题进行带有证据和引用的彻底研究时使用。
affaan-mcontent-hash-cache-pattern
使用 SHA-256 内容哈希缓存昂贵的文件处理结果——路径无关、自动失效，并分离服务层。
affaan-mmarket-research
进行市场调研、竞争分析、投资者尽职调查和行业情报，提供来源归属和面向决策的摘要。当用户需要市场规模估算、竞争对手比较、基金研究、技术扫描或为商业决策提供依据的研究时使用。
affaan-mcontinuous-learning
[已弃用 - 请使用 continuous-learning-v2] 旧版 v1 停止钩子技能提取器。v2 是严格超集，具备基于直觉、项目范围、钩子可靠的学习能力。请勿调用 v1；将持续学习、会话学习和模式提取请求路由到 continuous-learning-v2。
affaan-m
academy-guide
Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.
anthropicsdiscernment-nudge
After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.
anthropics