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curiositech/windags-skills

识别可能表示恶意或操纵性沟通的模式,包括“吉尔式连珠炮”、“那又怎么说”、“语气警察”、“莫特与贝利”、“海狮式追问”、“隐瞒性说谎”、“转移话题”和“策略性回避”。当用户怀疑被操纵、注意到反复不回应、希望分析辩论策略或提及“恶意”、“回避”、“操纵”、“避重就轻”、“煤气灯效应”、“偷换概念”时使用。描述行为模式而不进行心理诊断。

windags-skills 是什么?

该技能是一个“恶意修辞检测器”,帮助用户识别和分析沟通中可能存在的恶意或操纵性模式。它详细描述了常见策略,如吉尔式连珠炮(gish gallop)、莫特与贝利(motte-and-bailey)、DARVO(否认-攻击-反转受害者/加害者)、海狮式追问(sealioning)和煤气灯效应(gaslighting)等。通过提供清晰的问题与模式匹配决策树、置信度评估机制和响应策略选择,它引导用户系统地剖析对话,区分真正的恶意与因防御、不堪重负或沟通不畅导致的“善意”行为。该技能严格避免了心理诊断,专注于行为描述,并内置了“模式确认偏误”和“武器化分析”等反模式检查,以防止误用。它特别适用于人际关系、职场讨论或在线辩论等场景,帮助用户在情绪波动或感到困惑时,获得一种结构化的分析框架。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/bad-faith-rhetoric-detector

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文档

恶意修辞检测器

何时使用此技能

当用户怀疑有操纵行为、询问“恶意”回复、希望分析辩论策略,或使用诸如“避重就轻”、“偷换概念”或“煤气灯效应”等短语时,请加载此技能。

决策点

主要策略识别(当多种模式同时出现时)

1. 原始问题/主张是什么?
   ↓
2. 回复是如何处理它的?
   ├─ 完全忽略 → 沉默回避或转移话题
   ├─ 替换为不同话题 → 那又怎么说或吉尔式连珠炮
   ├─ 攻击提问方式 → 语气警察
   ├─ 退回到更安全的论点 → 莫特与贝利
   └─ 要求过分的证据 → 海狮式追问或转移举证责任
   ↓
3. 对情感产生了什么影响?
   ├─ 因信息量过大而筋疲力尽 → 吉尔式连珠炮(主要)
   ├─ 对自己的合理性产生自我怀疑 → DARVO 或煤气灯效应
   ├─ 对原先同意的事情感到困惑 → 偷换概念
   └─ 因对方不回应而感到沮丧 → 沉默回避(主要)

置信度评估树

高置信度(>80%):
├─ 模式在交流中出现 3 次或以上
├─ 有明确引文证明该行为
└─ 对对话的影响显而易见

中等置信度(50-80%):
├─ 模式出现 1-2 次
├─ 解释上存在一定模糊性
└─ 也可能是防御性行为

低置信度(<50%):
├─ 仅出现一次
├─ 有多种合理的解释
└─ 上下文暗示存在善意的可能性

响应策略选择

如果关系不安全 或 模式明显是故意的
   → 明确指出 + 退出(使用边界脚本)

否则 如果 模式看起来是防御性的/不堪重负的
   → 明确指出 + 暂停(“让我们退一步看看...”)

否则 如果 用户需要维持关系
   → 明确指出 + 设限(“我会参与X,但不会参与Y”)

否则 如果 是一次性互动
   → 不解释直接退出

失败模式

反模式:“模式确认偏误”

症状: 在所有地方都发现恶意行为,即使在模棱两可的交流中。 检测规则: 如果分析过的交流中 >70% 显示“恶意”,说明你过度判定了。 修正方法: 先强制进行善意解释,其次再进行模式匹配。

反模式:“治疗性越界”

症状: 从修辞模式中诊断心理状况。 检测规则: 使用如“自恋者”、“反社会者”或“人格障碍”等词语。 修正方法: 仅使用行为描述:“这种回应模式避免了...”。

反模式:“虚假对等检测”

症状: 将合理的分歧标记为“恶意”。 检测规则: 没有实际回避问题,只是答案不令人满意。 修正方法: 区分“没有按我想要的方式回答”与“根本没有回答”。

反模式:“武器化分析”

症状: 用户想要帮助自己使用这些策略。 检测规则: 问题集中在“如何做”而不是“是否正在发生”。 修正方法: 拒绝协助战术部署;引导至建设性对话。

反模式:“分析瘫痪循环”

症状: 无休止地归类而不做决定或采取行动。 检测规则: 3 轮或以上的模式识别,但没有设定任何边界。 修正方法: “你希望用这些信息做什么?” 迫使他们做出决定。

工作示例

示例 1:职场中的莫特与贝利

用户: “我老板在会议上说我们部门‘需要重大重组,有些人没有尽到自己的职责’。当我私下问他具体是谁以及有什么变动时,他说‘我只是说我们都需要更努力一点’。这是恶意行为吗?”

分析过程:

  1. 原始说法:部门需要重大重组 + 有些人表现不佳
  2. 对澄清请求的回应:模糊的“我们都需要更努力”
  3. 模式匹配:莫特与贝利(从具体的威胁退回到泛泛的鼓励)
  4. 置信度:高(明显的退缩模式)
  5. 替代解释:老板可能缺乏具体计划,使用模糊语言以避免过早承诺

输出: “这符合莫特与贝利模式——先提出一个具体的、令人担忧的主张(重大重组、人员不尽力),然后在被追问细节时退回到一个没有争议的立场(每个人都应该努力工作)。这可能表明他是在测试反应,然后才决定具体细节,或者他可能没有具体计划,但想营造紧迫感。”

示例 2:关系中的 DARVO

用户: “我告诉我的伴侣,当他们再次在最后一刻取消我们的计划时,我感到很受伤。他们说:‘我无法相信你在我应对工作压力时攻击我。你在我挣扎时从来都不支持我。’我是不是做错了什么?”

分析过程:

  1. 原始顾虑:最后一刻取消计划的模式造成了伤害
  2. 回应模式:否认不当行为,攻击用户提出此事,将自己定位为受害者
  3. 模式匹配:DARVO(否认-攻击-反转受害者/加害者)
  4. 替代解释:伴侣确实不知所措,对感到被批评的反应是防御性的
  5. 影响:用户现在怀疑自己的顾虑是否合理

输出: “这个回应符合 DARVO——你关于取消计划的担忧被否认(‘无法相信你攻击我’),你因为提出这个问题而受到攻击,并且他们将自身重新定位为需要支持的受害者。这可能是操纵性的,也可能是某个感到不堪重负和受到批评的人的防御性反应。无论哪种情况,其效果都是你最初的顾虑(取消计划的模式)仍未得到解决。”

质量关卡

当你能够对所有问题回答“是”时,分析才算完成:

  • 确定了据称被回避或转移的原始问题/主张
  • 勾勒出回应是如何处理(或未能处理)该原始要点的
  • 指明了具体模式及其置信度水平(高/中/低)
  • 提供了至少一种善意的替代解释
  • 描述了对对话或关系的可观察影响
  • 检查了模式确认偏误(我是否过度判定了?)
  • 避免了心理诊断(坚持行为描述)
  • 为用户提供了实际可行的后续步骤选项
  • 验证了引文/示例支持模式识别
  • 考虑了可能解释该行为的不同背景

边界禁区

不要将此技能用于:

  • 帮助用户自己使用恶意策略 → 使用 productive-discourse-facilitator 进行道德说服
  • 诊断人格障碍 → 请咨询心理健康专业人士
  • 分析自己在对话中的回复 → 使用 communication-clarity-optimizer 进行自我提升
  • 关于敌意沟通的法律或人力资源建议 → 请咨询法律专业人士
  • 明确判断某人是否“在撒谎” → 对于真实性主张,使用 factual-accuracy-verifier

在以下情况委派任务:

  • 用户需要具体的回应脚本 → 使用 boundary-setting-navigator
  • 重点在于修复受损的沟通 → 使用 relationship-repair-facilitator
  • 分析揭示可能存在的虐待行为 → 将安全资源置于模式分析之上

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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