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应用认知任务分析(ACTA)方法论

系统化方法论,用于从实践者中引出、表示和操作化专家认知技能(模式识别、情境评估、心理模拟),无需研究培训。架起学术严谨性和实用可用性的桥梁。

应用认知任务分析(ACTA)方法论 是什么?

该技能(curiositech/windags-skills)提供了一个系统化的应用认知任务分析(ACTA)方法论,旨在从领域专家中提取和表示认知知识,用于智能体设计和培训。它专注于通过结构化访谈和认知需求表格,使专家技能(如模式识别、情境评估和决策)显式化和可操作化。该方法论支持在Claude Code、Cursor和Codex等平台上的开发,帮助开发者构建能够模仿专家推理的智能体。ACTA特别强调通过决策点表格处理各种专家响应类型(如模糊概括、程序性复述),并利用知识审计维度选择(如感知技能、心理模拟)来深入探测认知维度。该技能还包含关键失效模式分析(如橡皮图章式引出、单一专家权威综合症),以确保知识提取的质量。通过工作示例(如急诊护士分诊、制造安全检查),展示了如何将认知需求转化为智能体能力映射和技术权衡分析。最终,ACTA旨在为设计和验证AI智能体提供可靠的认知基础,使其能够处理需要模式识别和适应性决策的复杂任务。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/applied-cognitive-task-analysis-acta-met

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文档

技能:应用认知任务分析(ACTA)方法论

名称:ACTA — 提取和应用专家认知知识 描述:系统化方法论,用于从实践者中引出、表示和操作化专家认知技能(模式识别、情境评估、心理模拟),无需研究培训。架起学术严谨性和实用可用性的桥梁。

决策点

专家响应模式 → 访谈转向策略

专家响应类型检测信号下一步追问动作
模糊概括“你会找到感觉” / “经验教会你”→ 事件锚定:“请带我回忆你上次……的情景”
程序性复述“我先做X,然后Y,然后Z……”→ 认知维度探查:“是什么告诉你该进入下一步了?”
抽象原则“始终把安全放在第一位” / “关注客户”→ 对比案例:“给我看两个案例——一个适用这个原则,一个不适用”
与其他专家矛盾“永远不要做X”(而专家2说“总是做X”)→ 条件探索:“在什么情况下X是合适的?”
描述结果“然后系统运行得更好了” / “质量提高了”→ 线索检测:“你注意到什么迹象表明它正在生效?”

知识审计维度选择

如果专家提到“看到模式”或“注意到事情”
  则 探查感知技能
    → “请给我看两个X——一个正常,一个有问题的。区别在哪里?”

如果专家提到“弄清楚发生了什么”或“预测结果”
  则 探查心理模拟
    → “为了……必须发生什么?” / “如果你做X,结果会怎样?”

如果专家提到“跟踪多个因素”或“大局观”
  则 探查情境意识
    → “你监控哪些关系?” / “什么趋势很重要?”

如果专家提到“当标准程序不起作用时”
  则 探查即兴发挥
    → “告诉我一次标准操作规程失败的经历” / “你如何适应?”

如果专家提到“知道自己错了”或“自我质疑”
  则 探查元认知
    → “你如何发现自己犯的错误?” / “你什么时候寻求帮助?”

认知需求表填充策略

困难元素识别:
  如果多位专家难以阐明相同方面
    则 高认知需求
  如果新手尽管经过培训仍在此处持续失败
    则 依赖专业知识的元素
  如果标准程序在此处失效
    则 需要认知技能

为何困难分析:
  如果新手错过感知线索 → “缺乏对……的模式识别”
  如果新手应用错误策略 → “无法区分情境类型……”
  如果新手视野狭窄 → “只关注单一因素而非……”
  如果新手不知所措 → “没有……的心理模型”

线索/策略提取:
  如果专家说“我就是知道” → 追问具体的可观察指标
  如果专家给出多种方法 → 识别情境触发因素
  如果专家提到“经验” → 提取模式范例

失效模式

橡皮图章式引出

检测:专家访谈只产生程序性描述或教科书答案。没有认知洞察出现。 症状:访谈记录读起来像标准操作规程。专家说“只需遵循流程”。没有提到判断、适应或情境因素。 根本原因:抽象提问触发合理化反应,而非真实的专家推理。 修复:切换到事件探查。“请带我回忆你上次遇到……的情景”。将所有问题锚定在具体场景中。

单一专家权威综合症

检测:设计决策基于一位专家的方法,被当作普遍真理。 症状:“专家说总是做X”而没有条件推理。未记录专家间的差异。 根本原因:将个体专业知识视为领域专业知识。遗漏情境依赖性。 修复:访谈3-5位专家。当方法冲突时,探查条件:“在什么情况下你会不同地做?”

转录崇拜

检测:原始访谈笔记被当作最终交付物。未转换为可操作的表示。 症状:小时的访谈音频,没有结构化输出。分析止步于“我们捕捉了他们的知识”。 根本原因:混淆数据收集与知识提取。 修复:强制通过认知需求表进行转化。未完成识别困难方面、解释为何困难、提取线索/策略前不能完成。

全面性瘫痪

检测:项目停滞,等待“完整”的专业知识捕获后才构建任何东西。 症状:无休止的额外访谈。“我们首先需要理解一切”。没有部署时间线。 根本原因:完美主义阻碍了务实部署。学术标准应用于实际问题。 修复:提取足够的专业知识以支持初期能力。部署并配合监控和迭代周期。70%的覆盖率即可启用有价值的应用。

程序化幻想

检测:试图将专家判断转换为决策树或规则集。 症状:复杂的分支逻辑无法捕捉真实的专家推理。在新情况下失效的规则。 根本原因:将专业知识视为精炼的程序而非模式识别。 修复:构建模式分类和情境评估能力。关注“这是什么类型的情况?”而非“规定的动作是什么?”

工作示例

示例1:急诊护士分诊

背景:医院需要提高分诊准确性。有2年以上经验的护士在患者优先级分配上显著优于新毕业生。

任务图阶段: 初始访谈揭示了标准分诊程序:生命体征 → 协议查询 → 优先级分配。但专家提到新手忽略的“看起来不对劲”决策。

知识审计阶段: 探查:“请给我看两个生命体征相似的患者——一个你会优先处理,一个不会。” 专家响应:“这个(指着案例)血压和脉搏正常,但你看肤色和她如何摆放身体。看到她前倾了吗?那是呼吸窘迫的代偿。” 跟进:“新护士会错过什么?” 专家响应:“他们会看到正常生命体征并把她送到普通队列。他们不认识这个姿势模式或肤色变化。”

模拟访谈: 呈现场景:45岁男性,胸痛,生命体征正常,说“感觉像胃灼热。” 专家响应:“我会询问是否放射到下颌或左臂。还要看大汗——出汗模式。男性经常淡化心脏症状。即使生命体征正常,这个描述模式也增加了心肌梗死的风险。”

认知需求表输出:

困难元素为何困难线索/策略
检测代偿性呼吸窘迫由于生理代偿,生命体征看似正常前倾姿势、撅嘴、肤色变化、辅助肌使用
识别非典型心脏表现标准症状描述与教科书不符男性淡化模式、“胃灼热”描述伴大汗、下颌/手臂放射

智能体能力映射:

  • 基于姿势/位置数据训练的模式识别系统
  • 结合生命体征趋势的肤色分析
  • 症状描述模式的自然语言处理
  • 风险分层,将行为线索与生理指标加权结合

权衡分析:视觉模式识别需要摄像头系统 vs. 隐私问题。行为线索检测需要训练数据 vs. 患者同意。专家模式识别在多模态输入上运行,这在技术上具有挑战性但在临床上至关重要。

示例2:制造安全巡检

背景:化工厂需要改进启动前安全检查。有经验的巡检员能发现程序性检查表遗漏的危害。

任务图阶段: 标准检查覆盖系统中47项检查表条目。但高级巡检员提到防止事故的“直觉”危害检测。

知识审计阶段: 探查:“请带我回忆你上次发现检查表上没有的东西的经历。” 专家响应:“泵在规格内运行,压力正常,但振动模式感觉不同。不是更响——频率不同。结果发现轴承开始失效。如果在运行中就会变成灾难。” 跟进:“你怎么知道正常的振动模式?” 专家响应:“15年后,你感受到每个泵的运行方式。这个通常有平稳的嗡嗡声。那天下面有轻微的粗糙感。”

模拟访谈: 呈现场景:所有检查表条目通过,但单元3有异常气味。 专家响应:“我会追踪气味。化学气味不应该穿透建筑围护结构。要么是我们有传感器未注册的泄漏,要么是通风系统故障。即使读数正常,两者都很严重。”

认知需求表输出:

困难元素为何困难线索/策略
在传感器警报前检测设备退化需要学习正常运行模式的基线振动频率变化、声音模式转变、微妙的性能变化
通过感官线索识别密封失效化学检测系统有滞后时间和盲区气味追踪、气流模式评估、与环境条件的相关性

智能体能力映射:

  • 基于每个设备学习基线的振动分析
  • 具有异常读数模式识别的化学传感器网络
  • 关联多个感官输入的环境监测
  • 加权微妙退化指标的预测性维护算法

权衡分析:传感器密度 vs. 成本考虑。学习基线需要运行历史 vs. 新设备部署。人类感官融合(气味、振动、声音)技术上难以复制,但对早期危害检测至关重要。

质量关卡

认知需求表完成度:

  • 每个“困难元素”出现在多个专家访谈中(非个体怪癖)
  • “为何困难”的解释是可观察/可测试的(非循环推理如“需要经验”)
  • “线索/策略”足够具体以用于培训设计(非模糊如“模式识别”)
  • 专家在主要认知需求上达成共识(3+专家验证核心元素)
  • 智能体能够使用识别的线索分类新场景(可测试的区分)
  • 新手失败模式映射到特定认知缺口(非仅仅是“缺乏培训”)

访谈质量评估:

  • 专家提供了具体事件,而非仅一般原则
  • 认知维度被系统性地探查(感知、模拟、意识等)
  • 冲突的专家响应通过条件探索得到解决
  • 依赖上下文的推理被捕获(何时使用不同策略)
  • 隐性知识通过结构化探查变得明确

实用性验证:

  • 非专家可以在不参加访谈的情况下使用表格进行培训设计
  • 智能体规格可从线索/策略描述推导
  • 失效模式可从“为何困难”分析预测
  • 知识可跨相似领域/应用移植

不适用范围

不要使用ACTA处理:

  • 简单的程序性任务:如果专家只是更快地遵循程序,请改用流程改进方法
  • 文档完善的领域:如果专业知识已经以可访问形式捕获,请专注于转移/培训优化
  • 个人技能辅导:要发展一个人的专业知识,请使用导师制或刻意练习方法
  • 学术研究要求:如果需要出版级严谨性,请使用完整的认知任务分析方法
  • 实时绩效支持:对于即时帮助,请使用工作辅助或决策支持系统

应委托给:

  • 流程映射 → 用于程序优化
  • 培训设计专家 → 用于根据ACTA输出开发课程
  • 领域建模 → 用于综合知识表示
  • 民族志方法 → 用于文化/组织专业知识因素
  • 心理测量评估 → 用于个体能力测量

ACTA专门针对:提取认知专业知识,以实现卓越的模式识别、情境评估和适应性问题解决,用于培训设计和智能体规格目的。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

curiositech/windags-skills

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

curiositech/windags-skills

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

curiositech/windags-skills

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

curiositech/windags-skills

Implement WCAG 2.2 AA/AAA compliance with automated testing, keyboard navigation, screen reader support, and focus management. Activate on: accessibility audit, WCAG compliance, keyboard navigation, screen reader, aria attributes, axe-core, focus trap. NOT for: design-level accessibility review (use design-accessibility-auditor), color contrast only (use css-in-js-architect).

curiositech/windags-skills

Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

curiositech/windags-skills

Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.

curiositech/windags-skills

>- Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

curiositech/windags-skills

Extend and modify the admin dashboard, developer portal, and operations console. Use when adding new admin tabs, metrics, monitoring features, or internal tools. Activates for dashboard development, analytics, user management, and internal tooling.

curiositech/windags-skills

Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

curiositech/windags-skills

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

curiositech/windags-skills

AI-powered calendar management and agent-based scheduling coordination. Covers calendar APIs (Google Calendar, CalDAV/iCal), AI scheduling assistants (Reclaim, Clockwise, Motion, Cal.com), building custom calendar agents with MCP, multi-calendar merging, timezone management, focus block protection, meeting fatigue detection, and agent-to-agent meeting negotiation protocols. Activate on: "calendar agent", "AI scheduling", "calendar coordination", "meeting scheduling", "calendar API", "focus time protection", "calendar optimization", "Google Calendar MCP", "Reclaim", "Clockwise", "Motion", "Cal.com", "smart scheduling", "calendar-aware agent", "timezone scheduling", "agent negotiation meetings". NOT for: manual calendar UI component design (use form-validation-architect), project management scheduling or Gantt charts (use project-management-guru-adhd), general time-tracking or pomodoro apps (use adhd-daily-planner for time-awareness), building the agent itself from scratch (use agent-creator).

curiositech/windags-skills

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

curiositech/windags-skills

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

curiositech/windags-skills

Automated discovery and matching of agent skills for dynamic task routing and capability assessment

curiositech/windags-skills

Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

curiositech/windags-skills

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

curiositech/windags-skills

Logic-based agent programming language implementing BDI architecture for practical autonomous agent development

curiositech/windags-skills

>- Design AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

curiositech/windags-skills

Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

curiositech/windags-skills

license: Apache-2.0 NOT for unrelated tasks outside this domain.

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