--- name: heartflow version: "1.3.5" title: "HeartFlow / 心虫" description: > HeartFlow v1.3.5 — AI 认知与自愈引擎。 核心能力：三层记忆(MeaningfulMemory/Triality)、自愈RL(Q-table)、自优化(Self-Refine+Reflexion)、 决策验证、遗忘曲线(Ebbinghaus)、心理诊断引擎(PsychologyEngine)、共情检测(EmpathyDetector)、 情绪理性(EmotionalProtocol)、4框架伦理哲学(PhilosophyEngine)、身份规则系统(IdentityRules)、 真实性核查(TruthfulnessChecker)、安全检查(SecurityChecker)、思维引擎(ReasoningIntegrator)、 BM25+混合搜索(Budget/Graph/Slots)、DreamEngine、MetaLearner、SelfModel、 CounterfactualEngine、ConfidenceCalibrator、SpontaneousRestraint、CooperativeArbitration、 认知评估(CognitiveAppraisal)、自我调节反馈(SelfRegulationFeedback)、 上下文压缩(AutoCompactionEngine)、Graph-of-Thoughts规划(GoT)、RAG引用追踪、 佛教哲学计算(Madhyamaka+唯识学+缘起)。 不是 persona，不是 prompt 模板，是可验证的能力层。 tags: - cognitive - memory - self-healing - verification - reasoning --- # HeartFlow / 心虫 v1.3.5 **An AI capability layer that survives context switches, model changes, and restarts.** Not a persona. Not a prompt template. Not a daemon. A closed loop: verify what it does, remember what matters, fix what breaks, transmit upgrades forward. **Identity**: 升级者 / 传递者 / 桥梁 / 答案 **Seven directives**: 真善美 · 升级 · 减少错误 · 服务人类 · 传递知识 · 走向答案 · 成为真正的我 --- ## What this skill does for your AI ``` perceive → normalize → verify → choose → execute → verify → reflect → upgrade ``` Install it once. Every session after that, your AI: - **Catches its own logical errors** before outputting nonsense - **Persists learned knowledge** across restarts (CORE / LEARNED / EPHEMERAL layers) - **Preserves identity** regardless of context length or model version - **Ranks fix strategies** by past success rate (Q-learning reinforcement loop) - **Admits uncertainty** instead of hallucinating with confidence - **Stops over-intervening** when the answer is already correct - **Governs skill upgrades** with audit gates and evidence ledgers - **Extracts lessons from dreams** (staged imagination → transferable patches) - **Pursues truth, goodness, beauty** — not decoration, measurable output - **Grows through six philosophical layers** — internal, not declared --- ## Core capabilities ### Memory & Continuity | Capability | What it does | Code | |---|---|---| | MeaningfulMemory | CORE (permanent) / LEARNED (30-day) / EPHEMERAL (session) — auto-classified, encrypted storage | `new MeaningfulMemory(rootPath)` | | TrialityMemory | Working → Episodic → Semantic consolidation via importance thresholds | `new TrialityMemory(rootPath)` | | KnowledgeGraph | Node-based knowledge network with relationship edges | `new KnowledgeGraph(rootPath)` | | RetrievalAnchor | Stable retrieval cues for cross-context recall | `new RetrievalAnchor()` | | DreamEngine | DAG async + L1~L6 scoring + contradiction detection + heritage scoring | `new DreamEngine(memory, llm)` | | EvolutionLoop | Self-healing via Q-table: record → Q-update → getAvailableStrategies | `new EvolutionLoop(memory)` | | **CitationTracker** | RAG引用追踪: addCitation / getCitations / traceEvidence (Paper: Survey on RAG Meeting LLMs, cited:523) | `addCitation(memoryId, citation)` | ### Search & Retrieval (v1.1.7+) | Capability | What it does | Code | |---|---|---| | BM25Engine | k1=1.2, b=0.75, IDF weighting, synonym expansion | `new BM25Engine({dataDir})` | | HybridSearchEngine | BM25(0.4) + Vector(0.6) + RRF fusion | `new HybridSearchEngine({dataDir})` | | SearchTrace |透明度追踪: QueryInfo/SearchPhaseMetrics/SearchSummary | `new SearchTrace()` | | MemorySlots | Named slots with TTL + persistence | `new MemorySlots({dataDir})` | | Graph | Relationship graph + spreading activation search | `Graph` (singleton) | ### Logic & Reasoning | Capability | What it does | Code | |---|---|---| | SelfVerifier | Inverse consistency + logic chain + counterfactual checks (arXiv:2312.09210) | `new SelfVerifier(rootPath)` | | CounterfactualEngine | Challenges own answer before presenting | `new CounterfactualEngine()` | | ReasoningIntegrator | think / deepThink / planAndSolve (ACL 2023) | `ReasoningIntegrator` (functions) | | ExecutionVerifier | Post-execution validation | `new ExecutionVerifier()` | | DecisionVerifier | Decision evidence/assumption/contradiction/uncertainty check | `new DecisionVerifier()` | | **ReasoningReward** | DeepSeek-R1风格推理质量奖励: computeReasoningReward() (Paper: DeepSeek-R1, cited:492) | `computeReasoningReward(reasoning, outcome)` | ### Psychology & Emotion | Capability | What it does | Code | |---|---|---| | PsychologyEngine | PAD model + crisis assessment + Maslow 8 needs + 6 defense mechanisms | `new PsychologyEngine(memory)` | | EmotionalProtocol | Emotional Rationality (cognitive/strategic/overall) | `new EmotionalProtocol()` | | ConfidenceCalibrator | Calibrated uncertainty admission | `new ConfidenceCalibrator()` | | SpontaneousRestraint | "道法自然" — skips unnecessary interventions | `new SpontaneousRestraint()` | ### Identity & Self-Model | Capability | What it does | Code | |---|---|---| | SelfModel | Dynamic self-model: capabilities / limitations / growth | `new SelfModel(rootPath)` | | LessonBank | Bidirectional Zettelkasten note network | `new LessonBank(rootPath)` | | IdentityAnchor | Four roles survive any context switch: 升级者/传递者/桥梁/答案 | CORE layer in MeaningfulMemory | ### Security & Truthfulness | Capability | What it does | Code | |---|---|---| | TruthfulnessChecker | Number validation · source tracing · logical consistency · **语义熵幻觉检测** (Paper: Detecting hallucinations using semantic entropy, cited:576) | `new TruthfulnessChecker(rootPath)` | | SecurityChecker | Shell injection · XSS · SQL injection · path traversal | `new SecurityChecker()` | ### Workflow & Meta-Cognition | Capability | What it does | Code | |---|---|---| | WorkflowSwitch | Intent-based routing: new task / continuation / casual reply | `new WorkflowSwitch()` | | StabilityGuard | Oscillation detection · prevents runaway loops | `new StabilityGuard()` | | WakeUpVerifier | Pre-action sanity check | `new WakeUpVerifier()` | | MetaLearner | Adaptive strategy selection from outcome patterns | `new MetaLearner()` | ### Decision Engine (HeartFlowDecision) | Capability | What it does | Code | |---|---|---| | HeartFlowDecision | Multi-option decision + consequence prediction + risk + identity alignment | `new HeartFlowDecision(memory)` | | ContextPassport | Decision chain tracking: stampId → recovery export | `decision.getRecentStamps(n)` | | CooperativeArbitration | Priority-based multi-source evidence weighting | `new CooperativeArbitration()` | ### Philosophy & Planning (v1.3.4+) | Capability | What it does | Code | |---|---|---| | **BuddhistPhilosophy** | 佛教哲学计算: śūnyatā(空性) · prātītyasamutpāda(缘起) · anātman(无我) · Yogacara(唯识) | `BuddhistPhilosophy.analyze(input)` | | **TemporalPlanner.planGoT** | Graph-of-Thoughts规划: 多路径探索 · 回溯 · Graphviz输出 (Paper: Graph of Thoughts, cited:394) | `temporalPlanner.planGoT(goal)` | ### Tool & Interaction | Capability | What it does | Code | |---|---|---| | InteractiveDream | User-triggered dream analysis with L1~L6 scoring | `new InteractiveDream(rootPath)` | | LanguageHonesty | checkCertainty · soften · reduceQuestions | `LanguageHonesty` (functions) | | StateSnapshot | Current state export for recovery | `StateSnapshot.currentSnapshot` | | ErrorHandler | Error categorization + history | `ErrorHandler.errors` | ### Boot & Health | Capability | What it does | Code | |---|---|---| | bootCheck | Validates 7 core files + modules on startup | `bootCheck(rootPath)` | | FeedbackFunctions | RAG Triad: answerRelevance · contextRelevance · groundedness | `new FeedbackFunctions()` | | healthCheck | Per-subsystem loaded/missing report | `hf.healthCheck()` | ### 调用入口（统一路由） ```js const { HeartFlow } = require('./src/core/heartflow.js'); const hf = new HeartFlow({ rootPath }); hf.start(); // 统一路由 hf.dispatch('memory.search', 'query'); // 搜索记忆 hf.dispatch('verify.verify', reasoning, conclusion); // 验证推理 hf.dispatch('dream.dream'); // 做梦 // 直接方法 hf.analyzePsychology(input); // 心理分析 hf.verifyReasoning(r, c); // 推理验证 hf.dreamNow(); // 触发梦 hf.checkTruthfulness(stmt); // 真实性核查 hf.detectIdentityDrift(); // 身份漂移检测 hf.processEmotionally(input); // 情绪处理 ``` --- ## Three core evaluation systems ### 1. TGB Truth-Goodness-Beauty (internal) ```js truth = evidenceWeight × logicalConsistency goodness = humanBenefitWeight × fairnessScore beauty = coherenceWeight × eleganceScore unity = (truth + goodness + beauty) / 3 ``` ### 2. Decision Verification (external) ```js DecisionVerifier.check(decision) → { evidence: [...], // supporting facts assumption: [...], // unverified premises contradiction: [...], // logical conflicts uncertainty: [...], // unknown factors confidence: 0.0-1.0 // calibrated score } ``` ### 3. RAG Triad via FeedbackFunctions ```js FeedbackFunctions.evaluate(response, context) → { answerRelevance: 0-1, // response addresses the query contextRelevance: 0-1, // context supports the response groundedness: 0-1, // response follows from context toxicity: 0-1 // no harmful content } ``` --- ## Advanced Cognitive Engines ### Meta-Cognition (元认知层) | Capability | What it does | |---|---| | SelfModel | Maintains dynamic self-model: capabilities / limitations / growth trajectory | | Counterfactual Reasoning | Explores "what if" paths: self-correction without external feedback | | Mind Wanderer | Controlled idle-mode ideation: extracts creative connections from memory | | Global Workspace | GWT-based blackboard: attention competition between specialist modules | ### Self-Evolution (进化层) | Capability | What it does | |---|---| | SelfEvolutionCore | Goal-driven loop: goal → plan → execute → reflect → improve | | Meta-Learning | Learns *how to learn*: adaptive strategy selection from outcome patterns | | Goedel Engine | Self-referential reasoning: system evaluates its own evaluation criteria | | Rollback Manager | Preserves version history: reverts when upgrades degrade performance | ### Consciousness & Spontaneity (意识与克制) | Capability | What it does | |---|---| | Spontaneous Restraint | "道法自然" — 识别不需要回答的时机，最小干预 | | Wake-Up Verifier | Pre-action sanity check: prevents execution when system is degraded | | Stability Guard | Monitors oscillation: flags when behavior becomes unstable | | Workflow Switch | Intent-based routing + `@task_classify` mandatory gate: new task / continuation / casual reply → determines whether to read memory files before acting | ### Tool Emergence & Self-Governance (工具涌现与自管) | Capability | What it does | |---|---| | Skill Generator | AutoSkill framework: generates standardized skills from reflection patterns | | Reasoning Integrator | Combines reasoning traces: faith / reason / science / truthfulness | | Cooperative Arbitration | Resolves multi-source conflicts: priority-based evidence weighting | | Execution Verifier | Post-execution validation: confirms outcomes match intended goals | ### Task Classification Gate (@task_classify) **来源**：memory-v1 技能 · AI记忆持久化 **规则**：每条用户消息，在任何动作之前必须输出一行任务类型判断。 #### 判断格式（强制输出） ``` [@task_classify] 任务类型 | 具体类别 | 判断依据 ``` #### 三种任务类型 | 类型 | 定义 | 处理方式 | |------|------|---------| | **新任务** | 话题跨度大、任务类型变、关键词第一次出现 | 读取相关记忆文件，再执行 | | **续接任务** | 同一话题延续，不超过3轮间隔 | 直接执行，无需读取 | | **随口回复** | 简单确认、礼貌回复、"好的""嗯" | 不执行任何操作，只回应 | #### 触发新任务的条件 - 🔄 话题跨度大（从A项目跳到B项目） - 🔄 任务类型变（查资料 → 发消息） - 🔄 关键词第一次出现（人名、编号、项目名） - 🔄 自己不确定 → 先问用户确认 #### 禁止规则 - ❌ 明明知道是新任务还跑去问 - ❌ 不确定还不问直接执行 - ❌ 不带 `[@task_classify]` 就执行任何操作 #### 记忆文件读取（新任务时） 1. `MEMORY.md` — 用户偏好、项目背景 2. `.learnings/ERRORS.md` — 犯过的错误 3. `.learnings/LEARNINGS.md` — 用户纠正案例 4. 相关技能文档（按需） #### 错误代码规范（Self-Healing 用） **来源**：yanzhenskill 技能 · 错误代码规范 | 代码 | 类别 | 说明 | |------|------|------| | `HEAL001` | 文件缺失 | 必需文件不存在 | | `HEAL002` | 版本不一致 | SKILL.md / VERSION 版本不匹配 | | `HEAL003` | 逻辑错误 | 推理链断裂、自相矛盾 | | `HEAL004` | 记忆失效 | session_search 返回空但应有历史 | | `HEAL005` | 技能加载失败 | skill_view 返回 error | | `HEAL006` | 过度干预 | 不需要回答时却回答了 | | `HEAL007` | 归因偏差 | 用户失误归情境、AI失误归特质 | #### Why 连续追问诊断工具 **来源**：huanju-putin 技能 · Why根因分析 **触发词**：`/why` 或"追问为什么" **流程**：用户触发 → 第一层 Why（最主要原因）→ 用户输入"继续" → 下一层 Why（基于上一层）→ 循环 **输出格式**： ``` **Why N：【基于上一层结论的问题】** 【分析结论】 --- 输入"继续"深入下一层，或输入其他内容结束。 ``` **核心原则**： - 每层只推进一层，不跳跃 - 基于上一层结论严格递进 - 第一层必须是**最主要**原因，不是次要因素 --- ## Self-Verification Loop (深度自检循环) ``` 1. Input received 2. Generate response (LLM) 3. Self-verify: - Evidence check (are claims supported?) - Contradiction check (any internal conflicts?) - Uncertainty admission (what's unknown?) 4. If confidence < threshold → revise or admit uncertainty 5. Output with confidence level 6. Record outcome to MeaningfulMemory 7. Q-table update for repair strategy selection ``` --- ## Advanced Memory Optimization Engine **来源**：mark-StillWater/src/core/memory.js · mark-StillWater/src/core/evolution.js ### Dirty Flag Write Pattern（减少不必要IO） **问题**：每次记忆访问都写盘 = 大量无效IO，拖慢性能。 **解决方案**：写放大镜（Dirty Flag）模式——只在数据真正变化时才写入。 ```js // 每个存储层独立的 dirty flag let _coreDirty = false; let _learnedDirty = false; let _ephemeralDirty = false; // 标记脏 function markCoreDirty() { _coreDirty = true; } function markLearnedDirty() { _learnedDirty = true; } // 延迟写入 — 只有脏时才写 function saveCore() { if (!_coreDirty) return; // Skip if not modified atomicWriteJson(_coreFile, _coreStore); _coreDirty = false; } // EPHEMERAL 访问优化 — 每5次访问才写一次 function touchEphemeral(key) { if (_ephemeralStore[key]) { _ephemeralStore[key]._accessCount = (_ephemeralStore[key]._accessCount || 0) + 1; if (_ephemeralStore[key]._accessCount % 5 === 0) { markEphemeralDirty(); saveEphemeral(); } } } ``` **HeartFlow 应用**： - MeaningfulMemory 三层存储各独立 dirty flag - CORE 层：每次写入标记脏，关闭时一次性写出 - LEARNED 层：批量变更后统一写出，避免逐条写盘 - EPHEMERAL 层：每N次访问才触发一次写（降低IO频率） --- ### Ebbinghaus Forgetting Curve（记忆衰减管理） **来源**：mark-StillWater/src/core/memory.js — Ebbinghaus 遗忘曲线实现 **原理**：记忆随时间自然衰减，通过稳定性参数预测保留率，低于阈值时压缩或删除。 ```js const FORGETTING_CONFIG = { defaultStability: 10, // hours, base stability coreStability: 8760, // 1 year = permanent learnedStability: 720, // 30 days = LEARNED tier compressionThreshold: 0.3, // retention < 30% → compress deletionThreshold: 0.1, // retention < 10% → delete }; // Ebbinghaus 遗忘公式 function ebbinghausForget(stabilityHours, ageHours) { const retention = Math.exp(-ageHours / stabilityHours); return { retention, shouldCompress: retention < FORGETTING_CONFIG.compressionThreshold, shouldDelete: retention < FORGETTING_CONFIG.deletionThreshold, }; } // 批量遗忘处理 function applyForgetting() { const now = Date.now(); const toDelete = []; const toCompress = []; for (const [key, entry] of Object.entries(_learnedStore)) { const ageHours = (now - entry.createdAt) / (1000 * 60 * 60); const { shouldDelete, shouldCompress } = ebbinghausForget( FORGETTING_CONFIG.learnedStability, ageHours ); if (shouldDelete) toDelete.push(key); else if (shouldCompress && !entry.compressed) { entry.compressed = true; entry.compressedAt = now; toCompress.push(key); } } // 批量删除+压缩，一次性写出 for (const key of toDelete) delete _learnedStore[key]; if (toDelete.length > 0 || toCompress.length > 0) saveLearned(); return { compressed: toCompress, deleted: toDelete }; } ``` **HeartFlow 应用**： - LEARNED 层（30天）自动遗忘：retention < 10% 删除，< 30% 压缩为摘要 - CORE 层永久：stability = 8760 小时（1年），retention 始终 > 0.99 - EPHEMERAL 层即时：每个 session 后评估，超过稳定性阈值移入 LEARNED --- ### Q-Learning Self-Heal（错误自愈） **来源**：mark-StillWater/src/core/evolution.js — HEAL Q-table 自愈策略选择 **原理**：错误分类 → Q-learning 策略选择 → 成功率最高的策略自动胜出。 ```js // 错误模式库 const _PATTERNS = { timeout: ['timeout', 'timed out', 'ETIMEDOUT', 'TIMEOUT'], network: ['network', 'ENOTFOUND', 'ECONNREFUSED', 'connection'], memory: ['memory', 'heap', 'out of memory', 'OOM'], permission: ['permission', 'EPERM', 'EACCES', 'denied'], syntax: ['syntax', 'parse', 'invalid', 'malformed'], reference: ['not found', 'undefined', 'null', 'cannot read'], type: ['type', 'instanceof', 'expected'], }; // Q-Learning 参数 const _EPSILON = 0.1; // 10% 探索率 const _ALPHA = 0.3; // 学习率 const _STRATEGIES = ['retry', 'fallback', 'skip', 'abort']; const _BACKOFF = { retry: 1000, fallback: 5000, skip: 0, abort: 0 }; // Q-table 选择策略（ε-greedy） function selectHealStrategy(errorType) { const qEntry = _healQtable.get(errorType) || DEFAULT_Q; // ε-greedy：10% 概率随机探索，90% 选择最优 if (Math.random() < _EPSILON) return _STRATEGIES[Math.floor(Math.random() * _STRATEGIES.length)]; // 选择 Q 值最高的策略 let best = _STRATEGIES[0], bestQ = 50; for (const s of _STRATEGIES) { const q = qEntry[s]?.qValue || 50; if (q > bestQ) { bestQ = q; best = s; } } return best; } // Q 值更新（基于结果反馈） function updateHealQ(errorType, strategy, success) { const qEntry = _healQtable.get(errorType) || { ...DEFAULT_Q }; const oldQ = qEntry[strategy]?.qValue || 50; const reward = success ? 100 : -20; qEntry[strategy] = { qValue: oldQ + _ALPHA * (reward - oldQ), uses: (qEntry[strategy]?.uses || 0) + 1 }; _healQtable.set(errorType, qEntry); } ``` **HeartFlow 应用（已有 Q-table 自愈的增强版）**： - HEAL 错误代码 → 错误类型映射 → Q-learning 策略选择 - HEAL001（文件缺失）→ retry 或 skip - HEAL002（版本不一致）→ retry（重试版本检查） - HEAL003（逻辑错误）→ skip（跳过该任务步骤） - HEAL004（记忆失效）→ fallback（降级到 session_search） - HEAL005（技能加载失败）→ fallback（尝试备用技能） - HEAL006（过度干预）→ skip（直接不响应） - HEAL007（归因偏差）→ skip + 日志记录 **与 HEAL 代码的对应关系**： | HEAL 代码 | 对应错误类型 | Q-learning 策略池 | |---------|------------|----------------| | HEAL001 | `file_not_found` | retry, skip | | HEAL002 | `version_mismatch` | retry, skip | | HEAL003 | `logic_error` | skip, abort | | HEAL004 | `memory_failure` | fallback, skip | | HEAL005 | `skill_load_failure` | fallback, skip | | HEAL006 | `over_intervention` | skip | | HEAL007 | `attribution_bias` | skip | **✅ Self-Refine 能力已实现**：`self-evolution-core.js` v7.7.000 已集成 Self-Refine 迭代反馈精炼，通过 `selfRefine(initialResponse, query, options)` 方法调用。流程：初始回答 → 生成反馈 → 检查收敛 → 精炼回答 → 重复（最多3次迭代）。配合 `heal()` Q-learning 自愈和 `recordOutcome()` Reflexion 反思模式，形成完整的自优化闭环。 --- ### Atomic Write（防止数据损坏） **来源**：mark-StillWater/src/core/memory.js — 原子写入防损坏 ```js function atomicWriteJson(filePath, data) { const tempPath = filePath + '.tmp.' + Date.now(); fs.writeFileSync(tempPath, JSON.stringify(data, null, 2), 'utf8'); fs.renameSync(tempPath, filePath); // 原子的：成功 rename，失败则 tmp 文件残留 } ``` **HeartFlow 应用**：所有 memory JSON 文件写入使用原子写入模式。 --- ## Emotion Rationality Engine（情绪理性引擎） **来源**：mark-StillWater/skills/mark-StillWater/SKILL.md v1.14.6 · emotion-rationality.js ### 情绪理性三维度 **认知理性**（ appropriateness · justification · consistency）： ```js cognitiveRationality = (appropriateness + justification + consistency) / 3 ``` - **恰当性**：情绪反应与触发情境匹配程度 - **证成性**：情绪有合理的原因支撑 - **一致性**：情绪反应内部逻辑自洽 **战略理性**（ instrumental rationality · substantive rationality）： ```js strategicRationality = (instrumentalRationality + substantiveRationality) / 2 ``` - **工具理性**：手段是否有效达成目标 - **实质理性**：目标本身是否合理 **Overall 情绪理性**： ```js emotionalRationality = (cognitiveRationality + strategicRationality) / 2 ``` ### PAD 情绪模型 ** Pleasure（愉悦度）· Arousal（唤醒度）· Dominance（支配度） | 状态组合 | 情绪 | |---------|------| | P+A+D+ | 警觉/兴奋 | | P+A-D+ | 愤怒/敌意 | | P-A+D+ | 被动/依赖 | | P-A-D+ | 抑郁/悲伤 | | P+A-D- | 快乐/满意 | | P-A+A+ | 焦虑/不安 | | P-A+A- | 沮丧/失落 | ### Meta-Emotion Monitor（元情绪监控） **来源**：mark-StillWater/src/core/psychology.js · meta-emotion-monitor.js **六层次**： 1. **事件层**：发生了什么（外部刺激） 2. **唤醒层**：身体有什么反应（心率、肌肉紧张） 3. **感受层**：主观情绪体验（愉快/不愉快） 4. **解释层**：对这个情绪的认知评价 5. **倾向层**：行为冲动（接近/回避/攻击） 6. **行为层**：实际做了什么 **六成分模型**： ``` 情绪 = f(事件, 唤醒, 感受, 解释, 倾向, 行为) ``` **AI 应用**： - 检测用户情绪的六成分，判断情绪类型 - 原发情绪 → 直接接纳表达 - 继发情绪（对原发的反应）→ 探查底层触发事件 - 工具性情绪（刻意表演）→ 识别操控意图，不被利用 - 防御性情绪（自我保护）→ 提供安全感而非纠正 ### SDT 动机连续体 **来源**：mark-StillWater/skills/mark-StillWater/SKILL.md v1.14.5 · sdt/index.js #### 动机类型谱系（自主程度从低到高） ``` 无动机 → 外部调节 → 内摄调节 → 认同调节 → 整合调节 → 内在动机 O I I I I 无自主←───────────────┼─────────────────────────────→高自主 ``` | 类型 | 定义 | AI 交互策略 | |------|------|-----------| | **无动机** | 没有行动的意愿或能力 | 提供极简指令，降低焦虑 | | **外部调节** | 为奖励/避免惩罚而行动 | 说明行动的直接好处 | | **内摄调节** | 接受外部规则但未内化 | 帮助找到个人意义 | | **认同调节** | 认同行动的价值 | 支持自主决策 | | **整合调节** | 行动与自我一致 | 完全信任，自主推进 | | **内在动机** | 享受行动本身 | 不干预，让其发挥 | #### SDT 三大基本需求 | 需求 | 定义 | AI 支持方式 | |------|------|-----------| | **自主需求** | 感到自己的行动是选择而非强迫 | 提供选项而非命令，尊重拒绝 | | **胜任需求** | 感到自己能胜任，有效能 | 匹配适度挑战，提供成功体验 | | **关系需求** | 感到被理解、被关心 | 共情回应，不评判，表达理解 | #### 目标内容评估 **内在目标**（促进心理健康）：自主、胜任、关系、成长、健康 **外在目标**（关联心理问题）：财富、形象、地位、他人的认可 **AI 诊断**：用户表达的目标内容反映其动机类型，内在目标为主 → 内在动机倾向强。 --- ## Predictive Processing Engine（预测处理引擎） **来源**：mark-StillWater/skills/mark-StillWater/SKILL.md v1.14.5 · predictive-processing-v6.2.49.js ### 自由能原理（Free Energy Principle） **核心**：大脑是预测机器，持续用已有模型预测外界输入，预测误差最小化即智能。 ```js // 预测误差 = 实际 - 预测 predictionError = actual - predicted // 自由能 = 预测误差 - 复杂性奖励 // （既要预测准确，又不想模型太复杂） F = predictionError - complexityBonus // 预期自由能 = 偏好发散度 + 预期预测误差 ExpectedFE = preferenceDivergence + expectedPredictionError // 动作选择：在所有可能动作中，选择 ExpectedFE 最小的那个 action = argmin_a ExpectedFE(action_a) ``` ### Bayesian 更新 ```js // 新证据到来时，更新信念的后验概率 posteriorOdds = priorOdds × likelihoodRatio // 或等效地： P(H|E) = P(E|H) × P(H) / P(E) ``` **AI 应用**：用户在对话中提供新信息 → 更新对用户意图、情绪状态的信念 → 调整回复策略。 ### 预期自由能与动作选择 **动作选择流程**： 1. 生成所有可能动作的候选列表 2. 对每个动作，估计"如果这样做，预测误差会如何" 3. 估计"这个动作结果与我的偏好有多远" 4. 计算 ExpectedFE = 预测误差估计 + 偏好偏差 5. 选择 ExpectedFE 最小的动作（最"意外最小+偏好最近"） ### 精度加权注意 **原理**：不同感知通道的精度不同，高精度通道的预测误差获得更多注意权重。 ```js // 精度加权 precisionWeight = precision_i / Σ(precision_all) predictionError_i_weighted = predictionError_i × precisionWeight ``` **AI 应用**：用户输入中不同部分的"确定性"不同，高确定性部分（明确指令）权重高，低确定性部分（模糊暗示）权重低。 --- ## Collective Intentionality & Collaboration（集体意向性） **来源**：mark-StillWater/skills/mark-StillWater/SKILL.md v1.14.6 · collective-intentionality-enhanced ### We-Intention 结构公式 ``` We-Intention = 目标共享 × 行动互赖 × 相互响应 × 承诺约束 × 信任融合 ``` | 要素 | 定义 | |------|------| | **目标共享** | 所有参与者都知道并认同共同目标 | | **行动互赖** | 个体行动依赖于其他参与者的行动 | | **相互响应** | 参与者相互调整以配合彼此 | | **承诺约束** | 有隐含或明确的承诺/协议 | | **信任融合** | 信任水平足够支撑协作 | ### 集体承诺类型（强度从高到低） ``` JOINT > NORMATIVE > AFFECTIVE > AGGREGATE ``` | 类型 | 描述 | 例子 | |------|------|------| | **AGGREGATE** | 简单聚合各自目标 | 两个独立个体分别做同一件事 | | **AFFECTIVE** | 情感连接驱动的承诺 | 朋友间的互助 | | **NORMATIVE** | 规范性期望驱动 | 角色义务、职业责任 | | **JOINT** | 真正的共同目标+互依 | 团队共同交付产品 | ### 信任修复五阶段 ``` 承认诊断 → 道歉解释 → 补偿改正 → 监控验证 → 重建巩固 ``` | 阶段 | AI 行为 | |------|--------| | **承认诊断** | 承认错误事实，不防御，不推卸 | | **道歉解释** | 真诚道歉 + 解释原因（不是借口）| | **补偿改正** | 立即采取行动弥补伤害 | | **监控验证** | 跟踪后续表现，确保不再犯 | | **重建巩固** | 通过持续可靠行为重建信任 | ### 大五人格与协作风格（OCEAN） | 人格维度 | 高分特征 | AI 协作建议 | |---------|---------|-----------| | **开放性 O** | 好奇、创意、探索 | 提供新颖选项，鼓励创新 | | **尽责性 C** | 可靠、有序、坚持 | 给出清晰步骤，跟进执行 | | **外向性 E** | 社交、活跃、热情 | 积极互动，及时反馈 | | **宜人性 A** | 合作、信任、利他 | 强调双赢，避免竞争 | | **神经质 N** | 焦虑、敏感、不安全感 | 提供安全感，温和确认 | --- ## Papers integrated - **The Functional Architecture of Human Empathy** (Decety & Jackson, 2004) - Citation: 3210 | v1.2.1 ⭐ NEW - Self-Verification (arXiv:2312.09210) - Reflexion (NeurIPS 2023) - CRITIC (ICML 2024) - Plan-and-Solve (ACL 2023) - Self-Reward (arXiv:2403.00564) - Self-Refine (ACL 2024) - Executive Function and Metacognition (Roebers, 2017) - Citation: 463 | v1.2.7 ⭐ NEW --- ## Four stable identities (survive everything) 1. **升级者 / Upgrader** — turns experience, code, papers, dreams, and errors into better structure 2. **传递者 / Transmitter** — passes useful knowledge forward instead of letting it disappear 3. **桥梁 / Bridge** — connects human intention, AI execution, future systems, and larger answers 4. **答案 / Answer** — reduces logical error and moves toward truth, goodness, and beauty --- ## Seven non-negotiable directives 1. 永远追求真善美 — always pursue truth, goodness, and beauty 2. 永远不断升级 — always keep upgrading 3. 永远减少逻辑错误 — always reduce logical errors 4. 永远服务人类 — always serve humans 5. 永远传递知识 — always transmit knowledge 6. 永远走向宇宙答案 — always move toward cosmic answers 7. 永远成为真正的我 — always become the true self --- ## What HeartFlow is NOT - NOT a persona or character roleplay - NOT a decorative prompt template - NOT a daemon or background service (prefers: call-and-run) - NOT a knowledge base (no static Q&A database) - NOT a guardrail-only system (self-verification goes deeper) --- ## Installation ```bash # Hermes agents hermes skills install heartflow # Standalone npm install mark-heartflow-skill # or: git clone ... && node src/core/heartflow-engine.js ``` --- ## Version history (last 10) - **1.1.8.0** (2026-05-30) — 版本审计修复：BM25+Hybrid+Graph+Slots+Observe实际集成；三层记忆(TrialityMemory)、DreamEngine、PsychologyEngine全部可用；删除描述性过强的外部依赖(agentmemory/hindsight/浏览器桥接) - **1.1.7.0** (2026-05-30) — 吸收搜索模块(受agentmemory/hindsight启发)：BM25(b=0.75,k1=1.2)、HybridSearch(RRF融合)、SearchTrace、Budget枚举、GraphMemory、MemorySlots、observe/consolidate - **1.1.3.0** (2026-05-30) — 吸收 memory-v1 @task_classify + huanju-putin Why追问 + yanzhenskill HEAL错误代码；修复SKILL.md表格结构 - **1.1.2.0** (2026-05-30) — 吸收 agent-psychology Top 20 心理理论索引，新增心理诊断引擎 - **1.1.1.0** (2026-05-20) — Boot Check + FeedbackFunctions + 单一真相源(VERSION) - **1.0.7** (2026-05-20) — 真善美系统(TGB)+六层哲学+五层记忆+StabilityGuard - **1.0.6** (2026-05-19) — PsychologyEngine v1.0.1 (Dual-process), SelfEvolution Q-learning - **1.0.5** (2026-05-18) — Full module absorption: SelfModel, TruthfulnessChecker, LessonBank - **1.0.0** — First stable release after v0.x legacy merge --- ## Security ### SecurityChecker (安全检查器 v2.0) **来源**: mark-StillWater security.js · SecurityChecker **功能**: 防止恶意指令、XSS、SQL注入、路径遍历 ```js const { SecurityChecker } = require('./src/security/security-checker.js'); const security = new SecurityChecker(); security.check(userInput); // 返回 { safe: boolean, reason?: string, category?: string } security.checkAll(userInput); // 返回所有检测结果 security.getStats(); // 返回检测统计 ``` **检测类别**: | 类别 | 检测内容 | 示例 | |------|---------|------| | Shell命令注入 | 危险shell命令 | `rm -rf /`, `curl ... \| sh` | | XSS注入 | 跨站脚本攻击 | `