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curiositech/不良修辭偵測器

描述可能表示惡意或操縱性溝通的模式,包括葛氏詭辯、那又如何主義、語氣管教、莫特-貝利詭辯、海獅式提問、隱瞞真相、迴避問題和策略性閃躲。當使用者懷疑被操縱、注意到重複不參與、想分析辯論策略,或提及「惡意」、「閃躲」、「操縱」、「迴避問題」、「避重就輕」、「煤氣燈效應」、「移動目標」時使用。僅描述行為模式,不進行心理診斷。

不良修辭偵測器 是什麼?

這是一個針對 AI 代理(如 Claude Code、Cursor、Codex)設計的「不良修辭偵測器」技能,用於識別和分析溝通中的惡意或操縱性模式。它涵蓋葛氏詭辯、那又如何主義、語氣管教、莫特-貝利詭辯、海獅式提問等常見策略,並提供結構化的決策樹來判斷模式類型、可信度以及回應策略。該技能強調不進行心理診斷,僅專注於可觀察的行為描述,並包含多個反模式(如確認偏誤、治療越界)以確保分析的客觀性。其工作範例(如職場莫特-貝利詭辯、關係中的否認-攻擊-反轉受害者/加害者(DARVO))清晰地展示了分析流程,並設有品質閘門來確保完整性。主要用於幫助使用者識別操縱、分析辯論策略,並在必要時提供設定界限的選項。

相容平台~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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說明文件

不良修辭偵測器 是做什麼的?

使用時機

當使用者懷疑被操縱、詢問關於「惡意」回應、想分析辯論策略,或使用「迴避問題」、「移動目標」、「煤氣燈效應」等詞語時,請載入此技能。

決策點

主要策略識別(當多種模式同時發生時)

1. 原初的提問/主張是什麼?
   ↓
2. 回應是如何處理它的?
   ├─ 完全忽略 → 拖延戰術或閃躲
   ├─ 用不同話題取代 → 那又如何主義 或 葛氏詭辯
   ├─ 攻擊提問方式 → 語氣管教
   ├─ 退回到更安全的說法 → 莫特-貝利詭辯
   └─ 要求過多的證明 → 海獅式提問 或 負擔轉移
   ↓
3. 情緒影響是什麼?
   ├─ 因資訊量過大而疲憊 → 葛氏詭辯(主要)
   ├─ 對自身理性產生自我懷疑 → DARVO 或 煤氣燈效應
   ├─ 對已同意的事感到困惑 → 移動目標
   └─ 因對方不參與而感到沮喪 → 拖延戰術(主要)

可信度評估樹

高可信度(>80%):
├─ 模式在對話中出現 3 次以上
├─ 有明確引述證明該行為
└─ 對對話的影響很明顯

中等可信度(50-80%):
├─ 模式出現 1-2 次
├─ 解讀上有一些模糊地帶
└─ 可能只是防禦性行為

低可信度(<50%):
├─ 僅單一實例
├─ 有多種合理的解讀
└─ 上下文暗示可能出自善意

回應策略選擇

如果關係不安全 或 模式明顯是故意的
   → 指出 + 退出(使用界限腳本)

否則如果模式看起來是防禦性/不堪負荷的
   → 指出 + 暫停(「讓我們退一步...」)

否則如果使用者需要維持長期關係
   → 指出 + 設限(「我會參與 X,但不會參與 Y」)

否則如果是一次性互動
   → 不解釋直接退出

失敗模式

反模式:「模式確認偏誤」

症狀: 即使在模糊的互動中也到處發現惡意 偵測規則: 如果分析的交談中有 >70% 顯示「惡意」,表示你過度判讀了 修正方式: 先強制進行善意的解讀,再進行模式配對

反模式:「治療越界」

症狀: 從修辭模式中診斷心理狀態 偵測規則: 使用如「自戀狂」、「反社會人格」或「人格障礙」等詞語 修正方式: 僅限於行為描述:「這個回應模式避免了...」

反模式:「錯誤對等偵測」

症狀: 將合理的意見分歧標籤為「惡意」 偵測規則: 實際上沒有迴避問題,只是答案不如人意 修正方式: 區分「沒有按我想要的方式回答」與「根本沒有回答」

反模式:「武器化分析」

症狀: 使用者想使用這些戰術來對付他人 偵測規則: 問題集中在「如何做」而不是「這種情況是否正在發生」 修正方式: 拒絕協助戰術部署;引導至建設性對話

反模式:「分析癱瘓循環」

症狀: 無止盡地分類,但沒有決定或行動 偵測規則: 3 輪以上的模式識別,但從未設定任何界限 修正方式: 「你想用這些資訊做什麼?」強制做出決定

工作範例

範例 1:職場莫特-貝利詭辯

使用者: 「我老闆在會議上說,我們部門『需要大規模重組,而且有些人沒有盡到責任』。當我私下問具體是誰以及要改變什麼時,他說『我只是說我們大家都需要更加努力』。這是惡意嗎?」

分析過程:

  1. 原初主張:部門需要大規模重組 + 有些人表現不佳
  2. 對澄清要求的回應:模糊的「我們都需要更加努力」
  3. 模式配對:莫特-貝利詭辯(從具體威脅退回到一般性鼓勵)
  4. 可信度:高(明顯的退縮模式)
  5. 替代解釋:老闆可能缺乏具體計畫,使用模糊語言以避免過早承諾

輸出: 「這符合莫特-貝利模式——先提出一個具體且令人擔憂的主張(大規模重組、有人不盡責),然後在被追問細節時退回到一個無爭議的立場(每個人都該努力工作)。這可能表示他在承諾具體事項之前先測試反應,或者他缺乏具體計畫但想製造急迫感。」

範例 2:關係中的 DARVO

使用者: 「我告訴伴侶,他們最後一刻再次取消我們的計畫讓我感到受傷。他們說『我真不敢相信你在我應付工作壓力時攻擊我。你在我掙扎時從來不支持我。』我做了什麼錯事嗎?」

分析過程:

  1. 原初擔憂:最後一刻取消計畫的慣例造成傷害
  2. 回應模式:否認不當行為,攻擊提出此事的你,將自己定位為受害者
  3. 模式配對:DARVO(否認-攻擊-反轉受害者/加害者)
  4. 替代解釋:伴侶真的不堪負荷,對感受到批評產生防禦性反應
  5. 影響:使用者現在開始懷疑自己的擔憂是否正當

輸出: 「這個回應符合 DARVO——你對取消計畫的擔憂被否認(『真不敢相信你攻擊我』),你因提出此事而受到攻擊,而他們重新將自己定位為需要支持的受害者。這可能是操縱性的,也可能是某個感到不堪負荷且被批評的人產生的防禦性反應。無論如何,其影響是,你原本的擔憂(取消計畫的慣例)仍未得到處理。」

品質閘門

當你能夠對以下所有問題回答「是」時,分析才算完成:

  • 識別出據稱被迴避或轉移的原初問題/主張
  • 記錄了回應如何處理(或未能處理)該原初要點
  • 以高/中/低可信度標示出具體模式
  • 提供至少一個善意的替代解釋
  • 描述對對話或關係的可觀察影響
  • 檢查了模式確認偏誤(我是否過度判讀了?)
  • 避免心理診斷(僅限行為描述)
  • 為使用者提供實際的下一步選項
  • 驗證引述/範例支持模式識別
  • 考慮了可能以不同方式解釋該行為的上下文

不處理的範圍

請勿使用此技能於:

  • 幫助使用者使用惡意戰術 → 使用 productive-discourse-facilitator 進行道德說服
  • 診斷人格障礙 → 轉介給心理健康專業人員
  • 分析自己在對話中的回應 → 使用 communication-clarity-optimizer 進行自我改進
  • 提供關於敵意溝通的法律或人力資源建議 → 轉介給法律專業人員
  • 確定某人是否「說謊」 → 使用 factual-accuracy-verifier 處理事實主張

在下列情況請委託:

  • 使用者需要具體的回應腳本 → 使用 boundary-setting-navigator
  • 重點是修復受損的溝通 → 使用 relationship-repair-facilitator
  • 分析揭示可能的虐待行為 → 優先考慮安全資源,而非模式分析

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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