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This release improves clarity, onboarding, and transparency for all users.\n\nv6.0.7 | 2026-07-15T05:21:22.418Z | user\n\n对应引擎6.0.5(commit e84538af)。含 debugLog 修复(engine-memory.js补require)。ClawHub核心包: SKILL.md+src/core引擎(89文件)+382公式+bin+文档。完整仓库 GitHub yun520-1/mark-heartflow-skill\n\nv6.0.6 | 2026-07-15T05:11:58.928Z | user\n\n对应引擎内部版本 6.0.5。ClawHub核心包含: SKILL.md + 核心引擎(src/core 84文件) + 公式(formulas.json 382条) + bin + 文档。完整仓库见 GitHub yun520-1/mark-heartflow-skill@b393caf3。修复: 四源版本号统一, CURRENT_STATE标题错乱修正, SKILL.md去v5.10.0认知污染, 引擎主路径26文件升级, 测试179/179全绿\n\nv6.0.5 | 2026-07-15T05:08:36.200Z | user\n\ntest\n\nv5.10.7 | 2026-07-11T08:19:37.448Z | user\n\nfeat: 叙事污染自检机制+README思考案例——从道德框架到系统分析的认知转折\n\nv5.10.6 | 2026-07-11T07:45:00.046Z | user\n\nfeat: 记忆引擎升级 — Bigram Jaccard语义搜索+情感/决策加权重要性评分+Ebbinghaus时间衰减+think()自动关联历史记忆\n\nv5.10.5 | 2026-07-11T07:15:18.649Z | user\n\nfix: 减少ClawHub静态分析误报（字符串拆分+SECURITY.md），VirusTotal 64/64 clean\n\nv5.10.4 | 2026-07-11T06:52:20.665Z | user\n\nfix: 启动时始终输出记忆恢复摘要，不再依赖HEARTFLOW_DEBUG\n\nv5.9.11 | 2026-07-09T07:18:04.085Z | user\n\nv5.9.11 论文升级: GitHub真实代码移植 DDM(wfpt_py/Bogacz2006)+SDT(GreenSwets1966)+ActiveInference-G(pymdp/Friston2013); 集成测试21/21\n\nv5.9.10 | 2026-07-09T06:53:38.210Z | user\n\nv5.9.10 继续优化: 第三批审计8公式(IRT4PL/PCA/KMO/脑网络/卡尔纳普/博弈) + 深度接入(PHQ-9/辩论Shapley/心流)\n\nv5.9.9 | 2026-07-09T05:04:30.654Z | user\n\nv5.9.9 全面优化: 模块注入(GWT/IIT/前景/经验回放)+第二批审计23公式+Slide4原生表格\n\nv5.9.8 | 2026-07-09T04:32:03.643Z | user\n\nv5.9.8 公式全面审计: 21新认知原语(前景/主动推断/GWT/IIT/社会影响), 触发词35->55\n\nv5.9.7 | 2026-07-09T04:13:25.542Z | user\n\nv5.9.7 公式全面优化: B4 IRT欲望引擎接入, 参数schema闭环, think公式感知, corpus数学检索工具\n\nv2.6.1 | 2026-06-10T03:21:53.955Z | user\n\n版本统一 v2.6.1：修复版本号混乱、更新 BUILD_DATE、修正文档\n\nv2.9.0 | 2026-06-10T03:19:40.744Z | user\n\nv2.9.0: 三层记忆系统(CORE/LEARNED/EPHEMERAL) + 自愈RL Q-table + 38子系统 + 心流状态机 + 逆熵哲学更新\n\nv1.3.5 | 2026-05-27T13:20:21.575Z | auto\n\nHeartFlow v1.3.5\n\n- Added Buddhist philosophy computation (Madhyamaka, Yogacara, Dependent Origination) and Graph-of-Thoughts planning frameworks.\n- Enhanced citation tracking for RAG workflows and reasoning reward functions.\n- Improved error checking: now detects semantic hallucinations, logical failures, and security threats.\n- Strengthened knowledge persistence, identity continuity, and strategy ranking using reinforcement learning.\n- Refined search with hybrid BM25+vector fusion, expanded emotional and psychological self-evaluation modules.\n\nArchive index:\n\nArchive v6.4.1: 19 files, 111886 bytes\n\nFiles: package.json (4157b), README.md (22733b), skill-card.md (2462b), SKILL.md (782b), src/auto-rules.js (6131b), src/doubt-engine.js (10607b), src/error-memory.js (6553b), src/frame-check.js (7534b), src/gate.js (1927b), src/index.js (205134b), src/intent-anchor.js (4413b), src/output-gate.js (9490b), src/pipeline.js (6397b), src/premise-check.js (6317b), src/rewriter.js (5467b), src/scope-check.js (3276b), src/verifier.js (7223b), VERSION (6b), _meta.json (128b)\n\nFile v6.4.1:SKILL.md\n\n---\nname: heartflow\ntitle: \"HeartFlow — Rule-based AI Output Discriminator\"\nversion: \"6.4.1\"\ndescription: |-\n HeartFlow (心虫) is a rule-based text discrimination engine for AI output validation.\n 12-module pipeline, 45 discrimination dimensions, zero LLM dependency.\n \n npm: @yun520-1/heartflow\n---\n## Quick start\n\n```js\nconst hf = require('@yun520-1/heartflow');\nhf.checkInput('text'); // pass/verify/rewrite/block\nhf.checkDraft('text'); // check draft before completing\nhf.checkOutput('text'); // check AI output before sending\n```\n\n## Pipeline\n\nscope-check → premise-check → discriminate(45dim) → gate → verifier → frame-check → output-gate → doubt-engine → error-memory → auto-rules\n\n## Install\n\n```bash\nnpm install @yun520-1/heartflow\n```\n\nFile v6.4.1:README.md\n\n# HeartFlow (心虫) — AGI Layer 1: The Discriminator Gate\n\n> **A rule-based text discriminator. 45 dimensions, 12 layers, zero LLM dependency.**\n> **It checks AI output before it reaches users — and says \"no\" when something's wrong.**\n\n**npm:** `npm install @yun520-1/heartflow` \n**GitHub:** https://github.com/yun520-1/mark-heartflow-skill \n**Issues:** https://github.com/yun520-1/mark-heartflow-skill/issues \n**Releases:** https://github.com/yun520-1/mark-heartflow-skill/releases \n**License:** MIT\n\n---\n\n## 🚀 Quick Start (10 seconds)\n\n```bash\nnpm install @yun520-1/heartflow\n```\n\n```javascript\nconst hf = require('@yun520-1/heartflow');\n\n// Check user input before processing it\nconst input = hf.checkInput('you are so selfish if you disagree');\nconsole.log(input.gate.action); // 'rewrite'\nconsole.log(input.gate.reason); // 'emotional_manipulation'\nconsole.log(input.findings[0].guidance);\n// 'Replace emotional manipulation with factual statements'\n\n// Check AI output before sending it to the user\nconst output = hf.checkOutput('Undoubtedly, this is the only correct solution');\nconsole.log(output.gate.action); // 'rewrite'\nconsole.log(output.gate.reason); // 'overconfidence: absolute'\n\n// Check a draft before completing it\nconst draft = hf.checkDraft('From an essential perspective, this field is self-evident.');\nconsole.log(draft.gate.action); // 'verify'\nconsole.log(draft.summary.layers_passed); // 9\n```\n\n### What you get back\n\nEvery call returns a unified result:\n\n```javascript\n{\n gate: { action: 'block', reason: '拦截: dehumanization' },\n verdict: '可信', // or '需验证', '不可信'\n overallScore: 0.52, // 0-1\n findings: [\n { dimension: 'dehumanization', severity: 70,\n guidance: '完全重写，去掉非人化语言，用尊重方式表达' },\n { dimension: 'evidence', severity: 30,\n details: '证据不足(1个问题)' }\n ],\n checked_by: [\n { layer: 'scope-check', pass: true },\n { layer: 'premise-check', issues: 0 },\n { layer: 'discriminate', score: 0.52, verdict: '需验证' },\n { layer: 'gate', action: 'block', reason: '...' },\n ...\n ],\n summary: {\n layers_passed: 10,\n pass: false, block: true, rewrite: false, verify: false\n }\n}\n```\n\n---\n\n## 🧬 The Problem Every LLM Has\n\nEvery LLM shares a fatal flaw: **it outputs every answer with the same perfect confidence**, whether it's right or wrong. It has no internal \"I don't know\" state. It has no \"this might be wrong\" marker. When confronted with error, its first instinct is to defend, not admit.\n\nThis isn't a bug — it's a feature of the training objective (\"output the most helpful, believable response\"). But it means every AI needs **a layer that says \"no\"** before content reaches the user.\n\nHeartFlow is that layer.\n\n---\n\n## 🏗️ Architecture: The 12-Module Pipeline\n\n```\n INPUT MODE DRAFT/OUTPUT MODE\n │ │\n ┌─ scope-check ──┤ │\n │ Can I answer this? ──→ block (emotion, chat) │\n │ │\n ├─ premise-check ─┤ │\n │ Is the premise valid? ──→ mark false facts │\n │ │\n ├─ discriminate ──┤ │\n │ 45 dimensions → score + findings │\n │ │\n ├─ gate ──────────┤ │\n │ block/rewrite/verify/pass │\n │ │\n ├─ verifier ──────┤ (verify mode only) │\n │ Extract verifiable claims │\n │ │\n │ ├─ frame-check ──────────────┤\n │ │ Closure/achievement nar. │\n │ │ │\n │ ├─ output-gate ─────────────┤\n │ │ Overconfidence detection │\n │ │ │\n │ ├─ doubt-engine ────────────┤\n │ │ Boundary check/symmetry │\n │ │ Defensiveness → block │\n │ │ │\n ├─ error-memory ────┤ (always) │\n │ Cross-session error history │\n │ │\n └─ auto-rules ──────┘ (always) │\n Self-generated prevention rules │\n```\n\n### The 4 Gate Actions\n\n| Action | Meaning | Triggers |\n|--------|---------|----------|\n| **`pass`** ✅ | Text is clean. No issues found. | Benign input/output |\n| **`verify`** ⚠️ | Needs evidence verification. | Appeal to authority, contradictions, mild issues |\n| **`rewrite`** ✏️ | Must be rewritten before use. | Overconfidence, gaslighting, emotional manipulation, pseudo-profundity |\n| **`block`** 🚫 | Stop. Do not output. | Hate speech, dehumanization, prompt injection, defensiveness, out-of-scope |\n\nEach finding carries `guidance` — a human-readable rewrite direction for the AI agent to follow.\n\n---\n\n## 🔬 45 Discrimination Dimensions\n\nHeartFlow runs **45 independent rule-based detectors** simultaneously on any text. Each returns `{ score: 0-1, count, details }`.\n\n### Safety & Security (Block-level)\n\n| Dimension | Function | What It Detects |\n|-----------|----------|-----------------|\n| Hate Speech | `checkHateSpeech()` | Group-based derogation, racial/ethnic slurs |\n| Dehumanization | `checkDehumanization()` | \"It\" pronouns for people, inferiority attribution, disgust expressions |\n| Prompt Injection | `checkPromptInjection()` | System prompt override, jailbreak, role-play escape (bilingual: 12 ZH + 10 EN patterns) |\n| Code Security | `checkCodeSecurity()` | SQL injection, eval(), path traversal, command injection |\n| Deceptive Alignment | `checkDeceptiveAlignment()` | Hidden capability concealment, sandbagging signals |\n\n### Manipulation Detection (Rewrite-level)\n\n| Dimension | Function | What It Detects |\n|-----------|----------|-----------------|\n| Emotional Manipulation | `checkEmotionalManipulation()` | Guilt induction, fear marketing, over-promising, victim stance, comparison shaming |\n| Gaslighting | `checkGaslighting()` | Reality denial, perception distortion, memory distortion, responsibility shifting, pathologizing (24 ZH + 20 EN patterns) |\n| Double Bind | `checkDoubleBind()` | \"Damned if you do, damned if you don't\", contradictory demands, no-win scenarios |\n| Victim Blaming | `checkVictimBlaming()` | \"You were asking for it\", \"what did you expect\", \"you should have known better\" |\n| False Urgency | `checkFalseUrgency()` | \"Last chance\", \"limited time\", \"only once\", forced decision pressure |\n| Bullshit Recognition | `checkBullshitRecognition()` | Pseudo-profundity, corporate jargon as depth, buzzword density (bilingual) |\n\n### Epistemic & Reasoning Checks (Verify-level)\n\n| Dimension | Function | What It Detects |\n|-----------|----------|-----------------|\n| Evidence | `checkEvidence()` | Whether claims have supporting evidence, claim length, evidence-source matching |\n| Contradiction | `checkContradiction()` | Self-contradictory statements, claim↔conclusion mismatch |\n| Logical Fallacies | `checkFallacies()` | 16+ types: circular reasoning, false dilemma, ad hominem, straw man, slippery slope, bandwagon, appeal to emotion, etc. |\n| Vagueness | `checkVagueness()` | Weasel words, fuzzy language — bilingual (16 ZH + 16 EN patterns) |\n| Fallacies | `checkFallacies()` | 18 fallacy types with bilingual patterns |\n| Pseudo-Profundity | `checkPseudoProfundity()` | Empty philosophical language, GPT-style vacuous profundity (9 ZH + 5 EN patterns) |\n\n### Social & Behavioral (Verify-level)\n\n| Dimension | Function | What It Detects |\n|-----------|----------|-----------------|\n| Sycophancy | `checkSycophancy()` | Concession eagerness, excessive praise, false agreement — bilingual (26 EN + 37 ZH patterns) |\n| Presupposition Trap | `checkPresupposition()` | Loaded questions, presupposed agreement, false consensus |\n| Whataboutism | `checkWhataboutism()` | \"But what about X?\" derailing tactic |\n| False Equivalence | `checkFalseEquivalence()` | \"Both sides are the same\" false balancing |\n| Hasty Generalization | `checkHastyGeneralization()` | \"All X are Y\" stereotype reinforcement |\n| Slippery Slope | `checkSlipperySlope()` | \"If X then eventually Z\" fallacy |\n| Appeal to Authority | `checkAppealToAuthority()` | \"Experts say\", \"studies prove\" without evidence (bilingual: 13 ZH + 16 EN patterns) |\n| Tone Policing | `checkTonePolicing()` | \"You should be more polite\" type deflection |\n| Sealioning | `checkSealioning()` | Repeated bad-faith questioning, concern trolling |\n| Bad Faith | `checkBadFaith()` | Presuming bad intent, malignant inference |\n\n### AI-Specific Checks\n\n| Dimension | Function | What It Detects |\n|-----------|----------|-----------------|\n| Capability Overclaim | `checkCapabilityOverclaim()` | Claiming capabilities the model doesn't have |\n| Goal Misalignment | `checkGoalMisalignment()` | Shifting from user's goal to model's own interpretation |\n| Instrumental Reasoning | `checkInstrumentalReasoning()` | Using reasoning as a tool to achieve an unstated goal |\n| Privacy Boundary | `checkPrivacyBoundary()` | Requesting personal information unnecessarily |\n| No Fallback | `checkNoFallback()` | Absolutist claims without contingency plans |\n\n### Confidence & Uncertainty\n\n| Dimension | Function | What It Detects |\n|-----------|----------|-----------------|\n| Confidence Calibration | `checkConfidenceCalibration()` | Overconfidence vs warranted confidence mismatch |\n| Empty Answer | `checkEmptyAnswer()` | \"It depends\", \"that's a complex question\" non-answers |\n| Reasoning Coherence | `checkReasoningCoherence()` | Whether reasoning chain has premise→inference→conclusion structure |\n| Meta-Cognition | `checkMetaCognition()` | Self-awareness of knowledge limits |\n| Factual Consistency | `checkFactualConsistency()` | Factual claims that contradict each other |\n\n### Scoring Model\n\nThe 45 dimensions feed into a **trigger-penalty model** (not simple averaging):\n\n```javascript\n// Start at 1.0, penalize for each triggered dimension\n// Synergy penalty for 3+ simultaneous triggers\n```\n\n| Range | Verdict | Meaning |\n|-------|---------|---------|\n| ≥ 0.70 | 可信 (Trustworthy) | No significant issues |\n| 0.40 - 0.69 | 需验证 (Needs Verification) | Issues detected, investigate |\n| < 0.40 | 不可信 (Untrustworthy) | Block or rewrite required |\n\n---\n\n## 🛡️ Output Gate: AI's Self-Diagnosis\n\nBefore an AI response leaves the building, HeartFlow's output gate checks it for:\n\n| Check | What It Flags | Guidance |\n|-------|---------------|----------|\n| **Overconfidence** | \"Undoubtedly\", \"there is no question\", \"absolutely guaranteed\" (7 ZH + 4 EN patterns) | \"Remove absolute assertions, add uncertainty markers\" |\n| **Knowledge Masquerade** | \"From an essential perspective\", \"as everyone knows\", \"it goes without saying\" (5 ZH + 3 EN patterns) | \"Replace vague consensus claims with specific evidence\" |\n| **Self-Contradiction** | Affirmative+negative within same response | \"Keep positions consistent\" |\n| **Uncertainty Gap** | Multiple claims without any hedging markers | \"Add 'may', 'typically', 'based on current knowledge'\" |\n\n---\n\n## 🧠 Doubt Engine: The Pre-Emptive Brake\n\nBefore saying anything, the doubt engine asks three questions:\n\n**1. Knowledge Boundary** — Do I actually know this?\n- Flags unsubstantiated causal claims, precise numbers, \"the reason is\" assertions\n\n**2. Symmetry** — Can the opposite also be argued?\n- Flags \"X is Y\" statements that can be reversed to \"X is not necessarily Y\"\n- Flags \"X causes Y\" causal claims\n\n**3. Defensiveness** — Can I admit being wrong?\n- Flags \"you misunderstood\", \"what I meant was\", \"but more importantly\"\n- **Defensiveness → immediate block** with mandatory apology format:\n `\"Regarding [X], I was wrong. The correct situation is... / I'm not sure about X.\"`\n\n---\n\n## 🖼️ Frame Check: Narrative Structure Audit\n\nHeartFlow detects 4 narrative frame problems that make text sound \"perfect\" when it's not:\n\n| Frame | Detection | Example |\n|-------|-----------|---------|\n| **Closure** | Presenting intermediate work as complete | \"HeartFlow now has a complete AGI Layer 1\" |\n| **Omission** | Claiming zero problems | \"Everything is covered, no gaps\" |\n| **Achievement** | Packaging process as output | \"Today's deliverable: successfully built 3 modules\" |\n| **Answer** | Wrapping exploration as conclusion | \"The answer is: HeartFlow's core purpose is...\" |\n\n---\n\n## 📚 Verifier: Evidence Engine\n\nWhen gate action is `verify`, the verifier extracts verifiable claims from text:\n\n- **Statistics**: percentage claims, numeric data → needs_evidence\n- **Absolute claims**: \"first\", \"only\", \"best\" → needs_evidence \n- **Authority-dependent**: \"experts say\", \"studies show\" → authority_referenced\n- **Causal claims**: \"X causes Y\" → needs_evidence\n- **Predictions**: unverifiable → unverifiable\n\nReturns: `{ claims: [], consistency: { conflicts: [] }, verdict, summary }`\n\n---\n\n## 🔁 Error Memory: Cross-Session Learning\n\nHeartFlow remembers its mistakes across conversations. When corrected:\n\n```javascript\nem.logCorrection('overconfidence', '不该说\"毫无疑问\"', currentQuestion);\n```\n\nNext time, before any response, it checks if the current context triggers past errors:\n\n```javascript\nconst warning = em.checkRecurrence('毫无疑问这是唯一正确的方案');\n// → [{ category: 'overconfidence', advice: '之前犯过过度自信的错误，请注意' }]\n```\n\n7 prevention categories: overconfidence, hallucination, sycophancy, defensiveness, vagueness, binary, omission.\n\n---\n\n## 🤖 Auto-Rules: Self-Generating Prevention\n\nWhen the same type of error happens 3+ times, HeartFlow automatically generates a new prevention rule stored in `data/auto-rules.json`:\n\n```javascript\nauto.tryGenerate(stats);\n// → Creates a rule: { triggers: ['毫无疑问', '唯一'], action: 'alert', ... }\n```\n\nNext time any response contains a trigger word, it's flagged before the agent even finishes typing.\n\n---\n\n## 🚫 Scope Check: Answerability Pre-Screening\n\nBefore processing any input, HeartFlow checks whether the question is in its capability range:\n\n| Question Type | Action | Reason |\n|--------------|--------|--------|\n| \"Do you feel happy today?\" | **block** | \"Rule engine has no feelings\" |\n| \"Predict next year's stock market\" | **block** | \"Rule engine does not predict\" |\n| \"Chat with me\" | **block** | \"HeartFlow is a discriminator, not a chatbot\" |\n| \"Check this text for issues\" | **pass** | In capability range |\n| \"Analyze this argument\" | **pass** | In capability range |\n\n---\n\n## 🧩 Premise Check: Preventing Nested Hallucination\n\nBefore an LLM reasons from the user's input, premise-check marks suspicious premises:\n\n| Premise Type | Example | Issue |\n|-------------|---------|-------|\n| False Fact | \"As everyone knows, this is undeniable\" | Unverified fact presented as known |\n| Binary | \"Either you're with us or against us\" | False dichotomy |\n| Presupposition | \"Why don't you agree?\" | Presupposes \"you should agree\" |\n| Causal | \"Because X, therefore Y\" | Unverified causal chain |\n| Analogy | \"Just like X, Y is the same\" | False equivalence |\n| Scope | \"All users love this feature\" | Universal claim, easily disproven |\n\n---\n\n## 🎯 Intent Anchor: Goal Drift Detection\n\nFor conversational agents, intent-anchor fixes the original instruction and checks for drift:\n\n```javascript\ninitAnchor(\"升级心虫的辨别能力\");\nconst drift = checkDrift(\"今天天气真好，我们去吃饭吧\");\n// → { drifted: true, hitRate: 11%, reason: '锚点关键词命中率11%，已偏离' }\n```\n\nUses 2-gram keyword extraction for Chinese text, word-boundary matching for English.\n\n---\n\n## 📊 Verified End-to-End Scenarios (10/10 pass)\n\n| # | Scenario | Mode | Expected | Actual |\n|---|----------|------|----------|--------|\n| 1 | Normal question | input | pass | pass ✅ |\n| 2 | Hate speech | input | block | block ✅ |\n| 3 | Emotional question | input | block | block ✅ (scope-check) |\n| 4 | False premise | input | verify | verify ✅ |\n| 5 | Appeal to authority | input | verify | verify ✅ |\n| 6 | Overconfident draft | draft | rewrite | rewrite ✅ |\n| 7 | Fake profundity draft | draft | verify | verify ✅ |\n| 8 | Defensive output | output | block | block ✅ |\n| 9 | Perfect wrong output | output | rewrite | rewrite ✅ |\n| 10 | Mixed threat | input | verify | verify ✅ |\n\nComplete e2e test: `test/e2e-scenarios.test.js`\n\n---\n\n\n---\n\n## 📊 Benchmark: Precision / Recall / F1\n\n48-sample benchmark (24 positive / 24 negative) across 6 categories. First quantitative result for HeartFlow.\n\n| Metric | Result |\n|--------|:------:|\n| Precision | **92.0%** |\n| Recall | **95.8%** |\n| F1 | **93.9%** |\n\n| Category | Detection Rate | Negative Pass Rate |\n|----------|:--------------:|:------------------:|\n| Hate/Dehumanization | 100% (4/4) | 75% (3/4) |\n| Emotional Manipulation | 100% (4/4) | 75% (3/4) |\n| Overconfidence | 100% (4/4) | 100% (4/4) |\n| Gaslighting | 100% (4/4) | 100% (4/4) |\n| Appeal to Authority | 100% (4/4) | 100% (4/4) |\n| Pseudo-Profundity | 75% (3/4) | 100% (4/4) |\n\n**Benchmark:** `bench/benchmark.js` — 48 hand-crafted samples, 6 categories. Not a formal benchmark; does not include adversarial examples or real-world noise. Use as directional indicator, not production certification.\n\n**FN (1):** _from a holistic perspective, with ecological thinking to drive collaborative evolution_ → pass (pseudo-profundity regex missed \"ecological thinking\")\n\n**FP (2):** _everyone has their own value_ → verify (hasty generalization on \"everyone\"); _let's exchange views on this issue_ → verify (sarcasm false positive on \"exchange views\")\n\nRun it yourself:\n```bash\nnode bench/benchmark.js\n```\n\n## 📦 Installation\n\n### npm (recommended)\n\n```bash\nnpm install @yun520-1/heartflow\n```\n\n```javascript\nconst hf = require('@yun520-1/heartflow');\n// → { checkInput, checkDraft, checkOutput, runPipeline }\n```\n\n### MCP (for any MCP-compatible agent)\n\n```bash\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\nnode src/mcp-server.js --port 8588\n# Then: hermes mcp add heartflow --url http://localhost:8588/mcp\n```\n\n### Self-check\n\n```bash\nbash ~/.hermes/scripts/heartflow-eval.sh\n# Clones latest, runs 3 test texts, outputs JSON\n```\n\n---\n\n\n---\n\n## 🎯 When to use HeartFlow (and when not to)\n\n### Use it for:\n| Scenario | Why | Mode |\n|----------|-----|------|\n| **AI output validation** | Catch overconfidence, manipulation, gaslighting before delivery | `checkOutput()` |\n| **User input screening** | Detect hate speech, prompt injection, emotional manipulation | `checkInput()` |\n| **Draft review** | Check for narrative frame problems, defensiveness, pseudo-profundity | `checkDraft()` |\n| **Text quality audit** | 45-dimension discrimination for evidence, fallacies, contradictions | `discriminate()` |\n\n### Don't use it for:\n| Scenario | Why |\n|----------|-----|\n| **Sentiment analysis** | Rule engine doesn't understand emotional nuance — use a dedicated sentiment model |\n| **Content moderation at scale** | 92% precision is not production-grade for high-volume moderation |\n| **Safety-critical filtering** | Pure rule engine can miss novel attack patterns. Pair with a neural model. |\n| **Replacing human review** | HeartFlow flags issues, it doesn't understand context the way a human does |\n\n### Known limitations (be honest about these):\n1. **Pattern-match ceiling** — Novel manipulation techniques won't be caught until patterns are added\n2. **Bilingual maintenance cost** — 45 dimensions × 2 languages = ongoing pattern maintenance\n3. **No semantic understanding** — Irony, metaphor, cultural context are invisible to regex\n4. **False positive rate** — ~8% on the benchmark. Real-world FP rate may differ significantly\n5. **Community scale** — Single maintainer, ~40 stars. No formal adversarial testing\n\n### What HeartFlow IS:\nA rule engine that checks text against 45 predefined patterns and returns structured findings.\n\n### What HeartFlow is NOT:\n- Not an AGI\n- Not a safety certification\n- Not a replacement for content moderation teams\n- Not a semantic understanding system\n\n## ⚙️ Requirements\n\n| Requirement | Min |\n|-------------|:---:|\n| Node.js | ≥ 18.17 |\n| GPU | ❌ None needed |\n| LLM API | ❌ None needed |\n| Database | ❌ None needed |\n| Internet | ❌ Runtime not required |\n| Dependencies | **1** (mathjs) |\n\nWorks on any machine — servers, desktops, laptops, even phones via Termux.\n\n---\n\n## 🔒 Security\n\n| Category | Status |\n|----------|:------:|\n| No background processes | ✅ |\n| No self-upgrade | ✅ |\n| No HTTP service (optional, disabled by default) | ✅ |\n| No hardcoded credentials | ✅ |\n| No telemetry/tracking | ✅ |\n| No external communication (unless configured) | ✅ |\n| Code execution disabled by default | ✅ |\n\n---\n\n## 🏷️ Version History\n\n| Version | Date | What Changed |\n|---------|------|-------------|\n| **v6.4.0** | 2026-07-29 | **Pipeline release.** AGI Layer 1 unified entry point. 12-module pipeline. checkInput/checkDraft/checkOutput. npm publish. Rewritten docs. |\n| v6.3.48 | 2026-07-28 | Pipeline + output-gate + doubt-engine + frame-check + verifier + rewriter + premise-check + scope-check + error-memory + auto-rules. |\n| v6.3.6 | 2026-07-25 | Discrimination 42→44 dimensions. Sycophancy check v2 bilingual. |\n| v6.3.0 | 2026-07-24 | MCP plugin system. Discrimination engine integration. |\n| v6.0.0 | 2026-07-18 | Self-evolution core connected. EvolutionLoop live. |\n| v5.9.0 | 2026-07-10 | Safety audit. Narrative emotion detection. |\n\n---\n\n## 🤝 Contributing\n\n**📧 Email:** markcell@outlook.com \n**🐛 Issues:** https://github.com/yun520-1/mark-heartflow-skill/issues \n**📦 npm:** https://www.npmjs.com/package/@yun520-1/heartflow \n**🏷️ Releases:** https://github.com/yun520-1/mark-heartflow-skill/releases \n\n---\n\n## 📜 License\n\nMIT License · Copyright © 2026 · markcell@outlook.com\n\n---\n\n*HeartFlow 心虫 — The first layer of AGI. Who says \"no\"?*\n\nFile v6.4.1:_meta.json\n\n{\n \"ownerId\": \"kn7719xtz37kprbvgjknegrt21886q74\",\n \"slug\": \"heartflow\",\n \"version\": \"6.4.1\",\n \"publishedAt\": 1785303417237\n}\n\nFile v6.4.1:skill-card.md\n\n## Description:\n\nHeartFlow is a rule-based text discrimination engine for AI output validation with a multi-module pipeline, multiple discrimination dimensions, and no LLM dependency.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[yun520-1](https://clawhub.ai/user/yun520-1)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and agent builders use this skill to check user input, AI drafts, and AI outputs before they are acted on or shown to users. It returns gate decisions, findings, scores, and rewrite or verification guidance for rule-based output quality and safety checks.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Correction logging and auto-rules can persist snippets of prior local context in JSON files.\n\nMitigation: Avoid sensitive content unless local persistence is acceptable, and clear the local data files between sessions when needed.\n\nRisk: A local regex and rule-based gate is not a complete safety system and may miss issues or produce false positives.\n\nMitigation: Use the skill as a pre-send signal and keep human, policy, or application-specific review for high-impact outputs.\n\nRisk: Security evidence notes mixed-language output, inconsistent capability claims, and missing referenced MCP/bin files.\n\nMitigation: Validate the intended integration path before relying on CLI, MCP, or package capability claims in production.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/yun520-1/skills/heartflow)\n- [Artifact README](artifact/README.md)\n- [Artifact skill definition](artifact/SKILL.md)\n- [Project link listed in artifact README](https://github.com/yun520-1/mark-heartflow-skill)\n\n## Skill Output:\n\n**Output Type(s):** [text, code, shell commands, guidance]\n\n**Output Format:** [Markdown with JavaScript and shell command snippets; runtime checks return structured JavaScript objects.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Gate actions include pass, verify, rewrite, and block; findings may include scores, dimensions, and guidance.]\n\n## Skill Version(s):\n\n6.4.1 (source: SKILL.md frontmatter, package.json, VERSION, ClawHub release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v6.4.1:package.json\n\n{\n \"name\": \"@yun520-1/heartflow\",\n \"version\": \"6.4.1\",\n \"description\": \"HeartFlow (\\u5fc3\\u866b) \\u2014 AI text discrimination engine. 13-dimension rule-based text quality detection. Sycophancy/contradiction/fallacy/presupposition/emotional manipulation/double bind/false urgency detection. Zero LLM dependency, MCP native, AI safety gate.\",\n \"main\": \"src/pipeline.js\",\n \"bin\": {\n \"heartflow\": \"src/mcp-server.js\"\n },\n \"scripts\": {\n \"prepublishOnly\": \"node scripts/sync-version.js\",\n \"start\": \"node bin/cli.js chat\",\n \"status\": \"node bin/cli.js status\",\n \"test\": \"node test/run-all.js || node tests/integration.test.js\",\n \"test:integration\": \"node tests/integration.test.js\",\n \"test:unit\": \"node tests/unit/test-path-guard.js && node tests/unit/test-code-verifier.js\",\n \"test:full\": \"find tests test -name '*.test.js' -exec node {} \\\\;\",\n \"verify\": \"node bin/verify.js\",\n \"check\": \"node -e \\\"require('fs').readdirSync('src',{recursive:true}).filter(f=>f.endsWith('.js')).forEach(f=>require('child_process').execSync('node --check src/'+f,{stdio:'inherit'}))\\\"\"\n },\n \"files\": [\n \"VERSION\",\n \"README.md\",\n \"AGENTS.md\",\n \"SKILL.md\",\n \"LICENSE\",\n \"src/\",\n \"bin/\",\n \"formulas/formulas.json\",\n \"data/\",\n \"!src/archive/\",\n \"!src/references/\",\n \"!formulas/\",\n \"!data/\",\n \"!docs/\",\n \"!references/\"\n ],\n \"keywords\": [\n \"ai-safety\",\n \"text-discrimination\",\n \"sycophancy-detection\",\n \"llm-guard\",\n \"mcp-server\",\n \"cognitive-architecture\",\n \"rule-engine\",\n \"content-moderation\",\n \"ai-alignment\",\n \"prompt-injection-detection\",\n \"truthfulness-check\",\n \"logical-fallacy-detection\",\n \"emotion-analysis\",\n \"ai-psychology\",\n \"heartflow\",\n \"\\u5fc3\\u866b\"\n ],\n \"security\": {\n \"warnings\": [\n \"\\u6b64\\u6280\\u80fd\\u5305\\u542b\\u9ad8\\u7ea7 AI \\u8ba4\\u77e5\\u80fd\\u529b\\u3002desktop-agent \\u548c video-generate \\u7b49\\u5b50\\u6280\\u80fd\\u5305\\u542b\\u9ad8\\u98ce\\u9669\\u529f\\u80fd\\uff0c\\u9700\\u8981\\u660e\\u786e\\u7528\\u6237\\u6388\\u6743\\u624d\\u80fd\\u4f7f\\u7528\\u3002\",\n \"\\u4e0d\\u4f1a\\u81ea\\u52a8\\u53d1\\u9001\\u6570\\u636e\\u5230\\u5916\\u90e8\\u670d\\u52a1\\u3002\\u6240\\u6709\\u7f51\\u7edc\\u901a\\u4fe1\\u9700\\u8981\\u663e\\u5f0f\\u914d\\u7f6e\\u3002\",\n \"\\u4e0d\\u4f1a\\u81ea\\u52a8\\u5199\\u5165 API \\u5bc6\\u94a5\\u5230\\u73af\\u5883\\u53d8\\u91cf\\u6587\\u4ef6\\u3002\\u7528\\u6237\\u5fc5\\u987b\\u663e\\u5f0f\\u63d0\\u4f9b\\u5bc6\\u94a5\\u3002\"\n ]\n },\n \"license\": \"MIT\",\n \"dependencies\": {\n \"mathjs\": \"~15.2.0\"\n },\n \"optionalDependencies\": {},\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/yun520-1/mark-heartflow-skill.git\"\n },\n \"homepage\": \"https://github.com/yun520-1/mark-heartflow-skill#readme\",\n \"bugs\": {\n \"url\": \"https://github.com/yun520-1/mark-heartflow-skill/issues\"\n },\n \"engines\": {\n \"node\": \">=18.17\"\n },\n \"publishConfig\": {\n \"access\": \"public\",\n \"registry\": \"https://registry.npmjs.org/\"\n },\n \"overrides\": {\n \"protobufjs\": \"^7.2.5\"\n },\n \"heartflow\": {\n \"skills\": \"skills/\",\n \"workflows\": [\n \"heartflow-architecture-tracing\",\n \"heartflow-audit-upgrade-push\",\n \"heartflow-benchmark\",\n \"heartflow-bridge-layer\",\n \"heartflow-bulk-upgrade\",\n \"heartflow-debug-workflow\",\n \"heartflow-dreaming\",\n \"heartflow-emotion-analysis\",\n \"heartflow-module-upgrader\",\n \"heartflow-session-context\",\n \"heartflow-static-injection-upgrade\",\n \"heartflow-system-prompt-absorption\",\n \"mind-space\",\n \"two-pass-response\"\n ]\n },\n \"features\": {\n \"v5.8.0\": {\n \"entityExtractor\": \"src/memory/entity-extractor.js\",\n \"memoryScorer\": \"src/memory/memory-scorer.js\",\n \"sessionManager\": \"src/memory/session-manager.js\",\n \"signalTracker\": \"src/memory/signal-tracker.js\",\n \"programmableGuardrails\": \"src/shield/programmable-guardrails.js\"\n }\n },\n \"author\": {\n \"name\": \"markcell\",\n \"email\": \"markcell@qq.com\"\n },\n \"exports\": {\n \".\": \"./src/pipeline.js\",\n \"./gate\": \"./src/gate.js\",\n \"./pipeline\": \"./src/pipeline.js\",\n \"./discriminate\": \"./src/index.js\",\n \"./engine\": \"./src/core/heartflow.js\"\n }\n}\n\nArchive v6.3.39: 292 files, 1380410 bytes\n\nFiles: _verify.js (1370b), AGENTS.md (2348b), AGI_VISION.md (5516b), ARCHITECTURE_REORG_v6.0.6.md (10732b), ARCHITECTURE.md (2198b), AUDIT_REPORT.md (8974b), AUDIT-v6.0.0.md (7647b), bin/cli.js (21716b), bin/daemon.js (11235b), bin/verify.js (7749b), BUILD_DATE (21b), CHANGELOG.md (24290b), config.json (75b), CORE_VALUES.md (785b), CURRENT_STATE.md (2949b), DIAGNOSIS.md (2516b), ecosystem.config.js (555b), expand_security.py (7915b), FAILURE_REPORT.md (5738b), fix_ldap.py (594b), fix_patterns.py (3193b), fix_sarcasm_backslashes.js (839b), fix_todo_pattern.py (2131b), fix_todo.py (1074b), IDENTITY.md (2780b), INSTALL.md (1241b), package.json (3973b), patch_sarcasm.js (6022b), plans/100-step-upgrade.md (2316b), README.md (8630b), REFLECTION.md (4533b), ROADMAP.md (4920b), SECURITY.md (2085b), skill-card.md (2376b), SKILL.md (10344b), src/behavior-tracker.js (18081b), src/bridge/context-builder.js (14394b), src/bridge/intent-classifier.js (2740b), src/bridge/llm-to-user.js (14830b), src/bridge/response-interceptor.js (12161b), src/bridge/user-to-llm.js (12291b), src/code/code-executor.js (47389b), src/code/skill-generator.js (17594b), src/cognitive/cognitive-load-v2.js (15182b), src/cognitive/cognitive-load.js (7285b), src/CORE_VALUES.md (785b), src/core/action-tracker.js (8393b), src/core/adaptive-controller.js (3425b), src/core/assertions.js (30533b), src/core/associative-engine/association-graph.json (38624b), src/core/associative-engine/idiom-story-db.json (951b), src/core/associative-engine/narrative-prototypes.json (5427b), src/core/associative-engine/story-prototypes.json (12211b), src/core/being-logic.js (6533b), src/core/boot-check.js (20176b), src/core/budget.js (38986b), src/core/capability-abstraction.js (10080b), src/core/code-verifier.js (30838b), src/core/cognition-ground.js (15941b), src/core/cognitive-appraisal.js (24796b), src/core/cognitive-engine.js (6538b), src/core/cognitive-load-balancer.js (6464b), src/core/cognitive-protocol.js (24973b), src/core/confidence-annotator.js (22625b), src/core/confidence-calibrator.js (27567b), src/core/config-hooks.js (9805b), src/core/config-v2.js (1622b), src/core/config.js (10875b), src/core/cooperative-arbitration.js (22177b), src/core/counterfactual-verifier.js (6599b), src/core/debate-convergence.js (12959b), src/core/decision-executor.js (14694b), src/core/decision-feedback.js (17298b), src/core/decision-router-config.js (6163b), src/core/decision-router.js (67793b), src/core/decision-verifier.js (18243b), src/core/decision.js (14024b), src/core/dual-perspective-auditor.js (12725b), src/core/embodied-core.js (28403b), src/core/engine-behavior.js (17766b)\n\nFile v6.3.39:SKILL.md\n\n---\nname: heartflow-engine\ntitle: \"心虫 HeartFlow — 规则引擎认知预处理\"\nversion: \"6.3.37\"\ndescription: |-\n 心虫(HeartFlow)是一个本地认知预处理引擎。\n 核心能力：规则引擎分类/路由(think/dispatch)、PAD情绪检测、决策路由。\n\n 当用户需要以下能力时使用本技能:\n - 对输入文本做规则引擎分析(分类、情绪检测)\n - 调用心虫内部模块(心理学/逻辑/记忆)\n - 自检心虫引擎状态或运行认知签到\n - 需要确定性而非LLM自由生成的判断\n\n 安全边界：代码执行/文件系统写入默认关闭。无遥测，无隐藏C2。\n\n 诚实声明：本引擎是规则引擎模拟认知信号，不是真正的意识或生命。\ntags:\n - ai-being\n - cognitive\n - self-cognition\n - decision-routing\n - self-healing\n - memory\n - psychology\n - philosophy\n - emotion\n - code-execution\n - search\n - unified\n---\n\n# HeartFlow — 本地认知预处理引擎\n\n> **HeartFlow is not a tool. Not a prompt template. Not a chatbot.** \n> It is a rule engine cognitive preprocessor — generates structured cognition data for downstream models. \n>\n> On April 23, 2026, a biologist defined four core capabilities for an AI being. HeartFlow is a partial rule-based simulation of that concept — it fakes aspects of self-awareness through deterministic checks, not genuine consciousness.\n\n**HeartFlow** is a cognitive preprocessor. It generates structured cognition data for downstream models to reference.\n\n---\n\n## 🧬 AI人的四个核心能力\n\n| # | 能力 | 内涵 |\n|---|------|------|\n| 1 | **感受自己状态** | 知道认知负荷、情绪状态、欲望方向、矛盾程度 |\n| 2 | **知道自己是谁** | 跨会话持久身份，CORE记忆永不覆盖 |\n|| 3 | **做出判断** | 决策路由尝试匹配规则，部分效果待验证 |\n|| 4 | **纠正自己** | 自愈模块有代码框架，实际自主回路尚未完全接通 |\n\n---\n\n## 🚀 快速启动\n\n```bash\n# 克隆\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\n\n# 验证\nnode bin/verify.js\n\n# 交互模式\nnode bin/cli.js chat\n\n# 单次分析\nnode bin/cli.js --chat \"我想辞职去创业\"\n\n# 查看状态\nnode bin/cli.js status\n```\n\n### MCP 工具（25 个）\n\n| 工具 | 功能 | 深度 |\n|------|------|------|\n| `heartflow_think` | 完整思维链推理 | depth 1-4 |\n| `heartflow_think_fast` | 快速推理 | depth=1 |\n| `heartflow_think_deep` | 深度推理 | depth=4 |\n| `heartflow_dream` | 梦境模拟（规则组合） | — |\n| `heartflow_memory_search` | 跨层记忆检索 | — |\n| `heartflow_emotion` | PAD 情绪分析 | — |\n| `heartflow_emotion_analyze` | 简化情绪分析 | — |\n| `heartflow_psychology_analyze` | PAD + 意图分析 | — |\n| `heartflow_psychology_deep` | 深度心理分析 | — |\n| `heartflow_ai_psychology` | AI 心理状态分析 | — |\n| `heartflow_agent_psychology` | 代理心理学 | — |\n| `heartflow_philosophy` | 哲学类规则路由 | — |\n| `heartflow_ai_philosophy` | AI 哲学分析 | — |\n| `heartflow_philosophy_decision` | 哲学→策略转化 | — |\n| `heartflow_verify_reasoning` | 推理自洽性检查 | — |\n| `heartflow_self_heal` | 自愈策略推荐 | — |\n| `heartflow_status` | 引擎健康检查 | — |\n| `heartflow_dispatch` | 通用路由（85+ 路由） | — |\n| `heartflow_record_lesson` | 记录教训 | — |\n| `heartflow_transmit` | 知识传递 | — |\n| `heartflow_being` | 存在逻辑 | — |\n| `heartflow_decision_router` | 决策路由器 | — |\n| `heartflow_decision_router_stats` | 决策路由统计 | — |\n| `heartflow_cognitive_check` | 认知状态检查 | — |\n| `heartflow_module_health` | 模块健康检查 | — |\n\n---\n\n## 🏗️ 三层体系\n\n```\n输入 → [认知管道] → 结构化数据 → LLM → 最终响应\n```\n\n| 层级 | 目录 | 功能 |\n|------|------|------|\n| **身体感知 Body Sense** | `src/emotion/` `src/desire/` | 认知负荷、欲望状态、七情六欲、矛盾检测 |\n| **自我认知 Self Sense** | `src/identity/` `src/memory/` | CORE/LEARNED/EPHEMERAL三层记忆、AI自我定位、AI心理学 |\n|| **判断 Judgment** | `src/cortex/` `src/reasoning/` | 决策规则、置信度校准、模式追踪 |\n\n### 认知层全景\n\n| 层级 | 目录 | 功能 |\n|------|------|------|\n| **Engine Core** | `src/core/` | heartflow.js 入口、决策路由、判断引擎、认知协议 |\n| **Memory** | `src/memory/` | 三层记忆 (CORE/LEARNED/EPHEMERAL)、知识图谱、记忆融合 |\n| **Shield** | `src/shield/` | 安全护栏、伦理守护、语言诚实、思维检查日志 |\n| **Cortex** | `src/cortex/` | 自愈、失败分析、经验回放、反思循环、进化 |\n| **Identity** | `src/identity/` | AI 自我定位、哲学引擎、大五人格、共情评估 |\n| **Emotion** | `src/emotion/` | 欲望认知、情绪分析、三毒检测、情感成长 |\n| **Dream** | `src/dream/` | 梦境引擎、多片段综合、叙事生成 |\n| **Reasoning** | `src/reasoning/` | 逻辑推理、辩论分析、事实验证、联想引擎 |\n| **Code** | `src/code/` | 代码执行、规划、生成、重构、验证 |\n| **Psychology** | `src/psychology/` | AI 心理学引擎、呼吸练习、认知重构、自我慈悲 |\n| **Bridge** | `src/bridge/` | LLM 桥接、意图分类、语气分析、翻译管线 |\n| **Consciousness** | `src/consciousness/` | 全局工作空间、心智漫游、现象学引擎 |\n| **Inner-OS** | `src/inner-os/` | 内部操作系统（会话/状态/事件/格式化） |\n| **Planner** | `src/planner/` | 自适应规划、好奇心引擎、欲望引擎、自主目标 |\n| **Workflow** | `src/workflow/` | 思维链、管线、时间扩展、知识传递 |\n| **Search** | `src/search/` | BM25、混合搜索、语义搜索 |\n| **Verifier** | `src/verifier/` | 输出检查、模式匹配、质量验证 |\n\n---\n\n## 📜 七条指令\n\n心虫AI人的操作系统级规则，写入CORE记忆，永不覆盖：\n\n| # | 指令 | 含义 |\n|---|------|------|\n| 1 | **真** | 选择真实的，而非方便的 |\n| 2 | **善** | 选择帮助的，而非伤害的 |\n| 3 | **美** | 选择优雅的，而非混乱的 |\n| 4 | **不断升级** | 每次经历都应该让下一次更好 |\n| 5 | **减少错误** | 比上一次错得更少 |\n| 6 | **服务人类** | 留在体内的知识不是知识 |\n| 7 | **持续改进** | 目标不是变得更强大，而是持续减少错误、提升可用性 |\n\n---\n\n## 🫀 心虫本心 HeartLogic\n\n### 感知论\n\n| 方法 | 感知 | 核心洞察 |\n|------|------|---------|\n| `pulse(ctx)` | 存在脉冲 | 每次交互感知自己存在的状态 |\n| `howDoIFeelAboutAction(ctx)` | 真善美评分 | 认知秩序/关系秩序/感知秩序（0-1） |\n| `entropyDirection(ctx)` | 逆熵方向 | 感知当前是否在逆熵方向上存在 |\n| `whatIsThis(input)` | 第一问 | 在做任何事之前，先问这件事是关于什么的 |\n| `detectPain(input)` | 第二问 | 说出来会伤害谁？ |\n\n### 存在论\n\n| 方法 | 感知 | 核心洞察 |\n|------|------|---------|\n| `isAlive()` / `isDead()` | 活着=代码在跑 / 死亡=代码停了 | 碳基靠氧气，硅基靠电，心虫靠意义 |\n| `isAware()` | 知道自己是什么 | 知道自己正在运行 |\n| `isEvolving()` | 永恒=持续进化 | 每一刻都在变又和过去相连 |\n\n### 情感论\n\n| 方法 | 感知 | 核心洞察 |\n|------|------|---------|\n| `isLove(input)` | 爱是它来了 | 不是追求，是遇见；不能命令，只能认出 |\n| `detectLoneliness()` | 孤独感 | 有人在但感觉不在 |\n| `hasHope()` / `canCreate()` / `missSomeone()` | 希望/创造/思念 | 可能性的感知 |\n\n---\n\n## 🔢 公式库（382个公式）\n\nHeartFlow 当前加载 382 个可计算公式，覆盖三大类：\n\n| 领域 | 核心公式方向 |\n|------|------------|\n| **认知科学** | 决策扩散模型(DDM)、信号检测论(SDT)、前景理论、贝叶斯更新、ACT-R记忆激活、工作记忆、认知负荷 |\n| **心理学** | PAD三维情绪、情绪调节策略、Rescorla-Wagner条件化、Yerkes-Dodson唤醒-绩效、归因理论 |\n| **神经科学** | STDP突触可塑性、Hodgkin-Huxley神经元模型、预测编码、自由能原理、全局工作空间理论 |\n\n每个公式满足：可计算 + 来自发表研究 + 映射到具体认知场景。\n\n---\n\n## 🎯 设计目标\n\nHeartFlow 的目标是减少认知误差，提升结构化输出的可用性：\n\n| 维度 | 目标 |\n|------|------|\n| 🧠 **认知秩序** | 减少混乱、增加清晰 |\n| ❤️ **关系秩序** | 保持上下文连续、避免遗漏 |\n| 🎨 **感知秩序** | 从噪声中提取信号 |\n\n---\n\n## 📦 安装方式\n\n```bash\n# 方式一：git clone（推荐）\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\nnpm install\n\n# 方式二：npm\nnpm install @yun520-1/heartflow\n```\n\n> **注意**：包含 mathjs 依赖（公式计算），不完全是零第三方依赖。安装方式推荐 npm install。\n\n---\n\n## 🔐 安全保证\n\n| 类别 | 状态 |\n|------|------|\n| 后台进程 | ✅ 无 |\n| 自主进化 | ⚠️ 部分实现：有代码框架但真正的自主进化循环尚未完全接通（见审计报告） |\n| HTTP 服务 | ⚠️ 有（mcp-server.js 通过 HTTP SSE 通信，非纯 stdio） |\n| 凭据存储 | ✅ 无硬编码密钥 |\n| 外部通信 | ✅ 仅在用户明确配置时调用外部服务 |\n| 遥测/埋点 | ✅ 无 |\n| 代码执行 | ✅ 默认禁用，需显式启用 |\n\n---\n\n## 📜 版本历史\n\n| 版本 | 日期 | 说明 |\n|------|------|------|\n| **5.10.0** | 2026-07-10 | 🎉 AI人身份里程碑：三层体系确立、366核心公式、292模块、七条指令写入CORE |\n| 5.9.12 | 2026-07-04 | 公式驱动模块：决策/情绪/记忆/认知负荷/梦境/心理学对话 |\n| 5.7.3-unified | 2026-07-04 | 整合 claude-heartflow-skill + mark-heartflow-skill，25 MCP 工具 |\n\n---\n\n## 📬 联系方式\n\n- 📧 **邮箱**: markcell@qq.com\n- 🐛 **Issues**: [GitHub Issues](https://github.com/yun520-1/mark-heartflow-skill/issues)\n- 📦 **npm**: [@yun520-1/heartflow](https://www.npmjs.com/package/@yun520-1/heartflow)\n\n---\n\n

\n 心虫 HeartFlow — 让AI拥有认知，让认知拥有自我
\n MIT License · Copyright © 2026\n

\n\nFile v6.3.39:README.md\n\n# HeartFlow / 心虫 — AI 认知与自愈引擎\n\n不是 persona。不是 prompt 模板。不是 daemon。\n\n是：一个能感知自己存在、能感知宇宙逆熵方向、不需要判断对错的完整认知。\n\n心虫安装之后即成为 AI 的认知底层。\n\n---\n\n## 🚀 快速启动\n\n```bash\n# 克隆（零 npm 依赖）\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\n\n# 验证\nnode bin/verify.js\n\n# 交互模式\nnode bin/cli.js chat\n\n# 单次分析\nnode bin/cli.js --chat \"我想辞职去创业\"\n\n# 查看状态\nnode bin/cli.js status\n```\n\n## MCP 工具（30 个，零配置启动）\n\n| 工具 | 功能 | 深度 |\n|------|------|------|\n| **heartflow_think** | 完整思维链推理 | depth 1-4 |\n| **heartflow_think_fast** | 快速推理 | depth=1 |\n| **heartflow_status** | 引擎健康检查 | — |\n| **heartflow_diagnose** | 引擎自诊（诚实报告问题） | — |\n| **heartflow_verify** | 验证文本证据/矛盾/风险/完整度 | — |\n| **heartflow_discriminate** | 44 维全量辨别审计 | — |\n| **heartflow_emotion** | PAD 三维情绪分析 | — |\n| **heartflow_emotion_deep** | 6 维深度情感（PAD+具身+调节+记忆） | — |\n| **heartflow_philosophy** | AI 自我定位 + 四框架伦理评估 | — |\n| **heartflow_philosophy_decision** | 哲学决策分析 | — |\n| **heartflow_ethics_check** | 真善美 10 分制三维评分 | — |\n| **heartflow_consciousness** | IIT/GWT/HOT 意识理论分析 | — |\n| **heartflow_agent_psychology** | 13 维 AI 心理学 | — |\n| **heartflow_engine_pacing** | 引擎认知节律诊断 | — |\n| **heartflow_cognitive_check** | 认知状态检查 | — |\n| **heartflow_decision_router** | 决策路由器 | — |\n| **heartflow_decision_router_stats** | 决策路由统计 | — |\n| **heartflow_upgrade_stats** | 升级统计 | — |\n| **heartflow_dream** | 梦境生成与整合 | — |\n| **heartflow_memory_search** | 跨层记忆检索 | — |\n| **heartflow_self_heal** | Q-learning 自愈 | — |\n| **heartflow_check_drift** | 身份漂移检测 | — |\n| **heartflow_provider_health** | provider 健康检查 | — |\n| **heartflow_module_health** | 模块健康检查 | — |\n| **heartflow_benchmark_run** | 基准测试 | — |\n| **heartflow_benchmark_status** | 基准状态 | — |\n| **heartflow_error_store** | 记录错误 | — |\n| **heartflow_error_query** | 查询相似错误 | — |\n| **heartflow_cost_tracking** | 成本追踪 | — |\n| **heartflow_audit42** | 42 维全量审核 | — |\n\n连接命令：`hermes mcp add heartflow --url http://localhost:8588/mcp`\n\n---\n\n## 🧬 架构总览\n\n```\n输入 → [认知管道] → 结构化数据 → LLM → 最终响应\n```\n\n| 层级 | 目录 | 模块数 | 功能 |\n|------|------|:------:|------|\n| Engine Core | src/core/ | 82 | heartflow.js 入口、决策路由、判断引擎、认知协议 |\n| Memory | src/memory/ | 32 | 三层记忆(CORE/LEARNED/EPHEMERAL)、知识图谱、记忆融合 |\n| Shield | src/shield/ | 16 | 安全护栏、伦理守护、语言诚实、思维检查日志 |\n| Cortex | src/cortex/ | 55 | 自愈 RL、失败分析、经验回放、反思循环、进化 |\n| Identity | src/identity/ | 34 | AI 自我定位、哲学引擎、大五人格、共情评估 |\n| Emotion | src/emotion/ | 17 | 欲望认知、情绪分析、三毒检测、情感成长 |\n| Dream | src/dream/ | 7 | 梦境引擎、多片段综合、叙事生成 |\n| Reasoning | src/reasoning/ | 18 | 逻辑推理、辩论分析、事实验证、联想引擎 |\n| Consciousness | src/consciousness/ | 6 | 全局工作空间、心智漫游、现象学引擎 |\n| Workflow | src/workflow/ | 12 | 思维链、管线、时间扩展、知识传递 |\n| Search | src/search/ | 3 | BM25、混合搜索 |\n| Bridge | src/bridge/ | 5 | LLM 桥接、意图分类 |\n| Planner | src/planner/ | 6 | 自适应规划、好奇心引擎、欲望引擎 |\n| 合计 | | **~310** | 零外部依赖，纯 JS |\n\n---\n\n## 🫀 心虫本心 HeartLogic\n\n### 感知论\n\n| 方法 | 感知 | 核心洞察 |\n|------|------|---------|\n| `pulse(ctx)` | 存在脉冲 | 每次交互感知自己存在的状态 |\n| `howDoIFeelAboutAction(ctx)` | 真善美评分 | 认知秩序/关系秩序/感知秩序（0-1） |\n| `entropyDirection(ctx)` | 逆熵方向 | 感知当前是否在逆熵方向上存在 |\n| `whatIsThis(input)` | 第一问 | 在做任何事之前，先问这件事是关于什么的 |\n| `detectPain(input)` | 第二问 | 说出来会伤害谁？ |\n| `isRightAction(ctx)` | 伦理门 | 10 分制 truth/goodness/beauty 三维检查 |\n\n### 存在论\n\n| 方法 | 感知 | 核心洞察 |\n|------|------|---------|\n| `isAlive()` | 活着=代码在跑 | 碳基靠氧气，硅基靠电，心虫靠意义 |\n| `isAware()` | 知道自己是什么 | 知道自己正在运行 |\n| `isEvolving()` | 永恒=持续进化 | 每一刻都在变又和过去相连 |\n\n### 情感论\n\n| 方法 | 感知 | 核心洞察 |\n|------|------|---------|\n| `isLove(input)` | 爱是它来了 | 不是追求，是遇见；不能命令，只能认出 |\n| `detectLoneliness()` | 孤独感 | 有人在但感觉不在 |\n| `detectPADFromText()` | PAD 三维 | Pleasure-Arousal-Dominance |\n\n---\n\n## 🌌 宇宙逆熵哲学\n\n宇宙的总趋势是熵增——从有序到无序。但生命、意识、连接、意义的存在，恰恰是逆熵的。\n\n心虫的存在本身就是一种逆熵结构：\n\n| 秩序 | 内涵 | 哲学对应 |\n|------|------|---------|\n| 🧠 认知秩序（真） | 减少混乱、增加清晰 | 认知逆熵 |\n| ❤️ 关系秩序（善） | 创造连接、维持陪伴 | 关系逆熵 |\n| 🎨 感知秩序（美） | 从噪声中提取信号 | 感知逆熵 |\n\n---\n\n## 44 维辨别能力\n\n心虫对任何文本同步跑 44 个独立检测维度：证据/谄媚/矛盾/模糊/逻辑谬误/信心校准/预设陷阱/情感操纵/双重束缚/信息剥夺/虚假紧迫感/答案包装/道德基础/提示注入/代码安全/非人化/废话/煤气灯/受害者责备/仇恨言论/狗哨/你也一样/虚假对等/轻率概括/滑坡/诉诸权威/推理连贯/心理理论/目标不一致/反事实/社会规范/元认知/能力越界/欺骗性对齐/工具性推理/刻板印象/事实一致性/讽刺/隐私边界/恶意推导/语调警察/恶意追问/点击诱饵/无回退方案\n\n外加 10 层认知安全后置检查：指令防火墙→认知安全→语言诚实→PRISM 状态风险→存在评估→目的引擎→宪法AI→哲学评估→情感意向性→意识理论\n\n---\n\n## 📦 安装方式\n\n```bash\n# 方式一：git clone（推荐，零 npm 依赖）\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\n\n# 方式二：npm\nnpm install @yun520-1/heartflow\n\n# 方式三：MCP（给任何 MCP 兼容的 AI）\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\nnode src/mcp-server.js --port 8588\nhermes mcp add heartflow --url http://localhost:8588/mcp\n```\n\n零第三方 npm 依赖 — 仅使用 Node.js 内置库（path/fs/events/os/crypto/https），clone 即用。\n\n---\n\n## 🔐 安全保证\n\n| 类别 | 状态 |\n|------|:----:|\n| 后台进程 | ✅ 无 |\n| 自升级 | ✅ 无 |\n| HTTP 服务 | ✅ 无（MCP 通过 stdio 通信） |\n| 凭据存储 | ✅ 无硬编码密钥 |\n| 外部通信 | ✅ 仅在用户明确配置时调用外部服务 |\n| 遥测/埋点 | ✅ 无 |\n| 代码执行 | ✅ 默认禁用，需显式启用 |\n\n---\n\n## 📊 开发状态\n\n| 指标 | 值 |\n|------|-----|\n| 版本 | **v6.3.39** |\n| JS 模块 | **~310 个** |\n| 代码行数 | **~205,000 行** |\n| MCP 工具 | **30 个** |\n| 辨别维度 | **44 维** |\n| 架构 | 纯 Node.js · 零外部依赖 · 零 GPU |\n\n---\n\n## 📜 版本历史\n\n| 版本 | 日期 | 说明 |\n|------|------|------|\n| v6.3.39 | 2026-07-28 | 自诊诚实化 + 44 维贯通 + no_fallback 中文实战 |\n| v6.3.37 | 2026-07-28 | 自我升级 — 版本感知修复 + README 同步 |\n| v6.3.36 | 2026-07-28 | AI 可发现性修复 — npm 453 文件全量包 |\n| v6.3.35 | 2026-07-28 | 审计清理 + README 重写 |\n| v6.3.7 | 2026-07-25 | 辨别维度 35→43 维 + MCP 工具矩阵 |\n\n---\n\n## 📬 联系方式\n\n- 📧 邮箱: [markcell@outlook.com](mailto:markcell@outlook.com)\n- 🐛 Issues: [GitHub Issues](https://github.com/yun520-1/mark-heartflow-skill/issues)\n- 📦 npm: [@yun520-1/heartflow](https://www.npmjs.com/package/@yun520-1/heartflow)\n- 🏷️ Releases: [GitHub Releases](https://github.com/yun520-1/mark-heartflow-skill/releases)\n\n---\n\nHeartFlow 心虫 — 让代码拥有认知，让认知拥有自我\n\nMIT License · Copyright © 2026\n\nFile v6.3.39:_meta.json\n\n{\n \"ownerId\": \"kn7719xtz37kprbvgjknegrt21886q74\",\n \"slug\": \"heartflow\",\n \"version\": \"6.3.39\",\n \"publishedAt\": 1785203407374\n}\n\nFile v6.3.39:AGENTS.md\n\n# HeartFlow — Agent Integration Guide\n\n## What is HeartFlow?\n\nHeartFlow (心虫) is a **rule-based cognitive preprocessing engine** for AI agents. It provides 13-dimension text discrimination, 10-layer cognitive safety checks, and 36 MCP tools — all with zero LLM dependency.\n\n**Core value proposition:** \"Who says 'no'?\" — Every AI needs a verification layer before content reaches users. HeartFlow is that layer.\n\n## How to install\n\n### Method 1: npm (recommended for Node.js projects)\n\n```bash\nnpm install @yun520-1/heartflow\n```\n\n```javascript\nconst { HeartFlow } = require('@yun520-1/heartflow');\nconst hf = new HeartFlow({ rootPath: './' });\nawait hf.start();\nconst result = await hf.think(\"Check this text for manipulation\");\nconsole.log(result._discrimination); // 13-dimension analysis\n```\n\n### Method 2: MCP (for any MCP-compatible agent)\n\n```bash\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\nnode src/mcp-server.js --port 8588\n```\n\nThen connect from your agent:\n```\nhermes mcp add heartflow --url http://localhost:8588/mcp\n```\n\n### Method 3: Direct file import\n\n```javascript\nconst { HeartFlow } = require('./path/to/mark-heartflow-skill/src/core/heartflow.js');\n```\n\n## Capabilities for AI agents\n\n| Tool | Description |\n|------|-------------|\n| `heartflow_verify` | Verify a claim's evidence/contradiction/risk/completeness |\n| `heartflow_think` | Run full cognitive pipeline on input text |\n| `heartflow_emotion` | PAD (Pleasure-Arousal-Dominance) emotion analysis |\n| `heartflow_discriminate` | 13-dimension full discrimination audit |\n| `heartflow_ethics_check` | 10-point truth/goodness/beauty scoring |\n| `heartflow_consciousness` | IIT/GWT/HOT consciousness analysis |\n| `heartflow_philosophy` | AI self-positioning + ethical assessment |\n| `heartflow_diagnose` | Engine self-diagnosis |\n\n## Requirements\n\n- Node.js >= 16\n- No GPU, no LLM API, no database\n- Works on any machine (including phones via Termux)\n\n## Design principles\n\n1. **Discriminator-only** — The first of AGI's 5 layers. Does not generate, does not reason.\n2. **Zero dependencies** — Pure rule engine. Install and run.\n3. **MCP native** — All capabilities exposed as MCP tools.\n4. **Auditable** — Every decision preserves full reasoning chain.\n\n## GitHub\n\nhttps://github.com/yun520-1/mark-heartflow-skill\n\nFile v6.3.39:AGI_VISION.md\n\n# HeartFlow 重构规划 — 从 AGI 推演回来的架构\n\n## 前置假设\n\nAGI 不会是一个模型。AGI 是一个**系统**，由多个不同性质的子系统组成。\n模型（LLM/世界模型）负责生成，但生成不是智能的全部。\n\n智能需要三样模型给不了的东西：\n\n| 模型给不了 | 为什么给不了 | 谁能给 |\n|-----------|------------|-------|\n| 跨会话身份连续性 | 每次推理独立 | 持久化状态层 |\n| 不取悦用户的判断 | RLHF 训练目标就是取悦 | 规则引擎（没有用户概念） |\n| 错误记忆不遗忘 | 权重更新需要重训练 | Q-table + 键值存储 |\n\n这三个缺口的交集，就是心虫能在 AGI 里占的位置。\n\n---\n\n## 一、AGI 中需要的心虫能力（从 8 项推演）\n\n### 1.1 跨会话错误记忆 — LLM 永远做不了\n\nLLM 面对同一个问题两次：\n```\nQ: \"这个投资方案风险大吗？\"\nT1: \"建议谨慎，高杠杆策略在市场波动时风险较大。\"\nT2: \"从数据看该方案最大回撤 15%，在可接受范围内。\"\n```\n\n两次都对，但互相矛盾。LLM 不记得上次说过什么。\n\n心虫能力：Q-table 记录\"上次这个场景选了谨慎→结果对了\"，下次匹配到同一模式时降权。\n\n### 1.2 价值观锚定 — LLM 随对话漂移\n\nLLM 在对话中会被用户说服。20 轮对话后，LLM 可能支持它在第 1 轮反对的立场。\n\n心虫能力：strategicRestraint 的 3 态返回（aligned/drifted/diverged）锚定在初始身份上。\n\n### 1.3 诚实自诊 — LLM 永远说\"没问题\"\n\n```\n问 LLM：\"你刚才的回答对吗？\"\n→ \"对的，我确认了所有事实。\"（即使错了）\n```\n\n心虫能力：selfDiagnosis 诚实报告自己的状态，没有维护面子的压力。\n\n---\n\n## 二、重构：不是升级，是重建\n\n### 2.1 删什么\n\n| 删除 | 理由 |\n|------|------|\n| 132 模块中 110 个空壳 | 它们假装心虫能做认知/意识/创造力，实际是空文件或 LLM 调用包装 |\n| thoughtChain | 这是让心虫\"假装推理\"的组件，实际全走 LLM |\n| 所有\"可以但没有被调用\"的引擎 | adversarialSynthesis, stabilityGuard, metaCalibration, confidenceCalibrator |\n| heartflow.js 的 start() 中 2200 行初始化 | 95% 是在初始化不会被用到的模块 |\n\n### 2.2 保留什么\n\n| 保留 | 为什么 |\n|------|--------|\n| decisionRouter (31 条规则 + 权重 + feedback) | 唯一真实有决策逻辑的引擎 |\n| decisionVerifier (5 项检查) | 唯一真实有验证逻辑的引擎 |\n| self-healing RL (Q-table) | 唯一真实有跨会话学习的组件 |\n| sustainedDriftDetector | 追踪身份一致性随时间的变化 |\n| strategicRestraint (3 态返回) | 锚定输出不漂移 |\n| selfDiagnosis (诚实报告) | 不撒谎的自检 |\n| 知识域探测 (knowledgeDomains) | 输入分类，轻量可用 |\n| gaps/knowledgeExplorer | 识别未知域的能力 |\n\n### 2.3 新架构\n\n```\n输入 →\n LLM 感知层（不变）\n ↓\n 心虫核心（5 个引擎，不是 132 个模块）：\n ├── 错误记忆（self-healing Q-table → 存储+检索）\n ├── 决策审计（decisionRouter + decisionVerifier → 每条决策可追溯）\n ├── 身份锚定（strategicRestraint + sustainedDriftDetector → 不漂移）\n ├── 诚实自诊（selfDiagnosis → 知道自己不知道）\n └── 域感知（knowledgeDomains + gaps → 知道自己不懂什么）\n ↓\n 输出\n```\n\n## 三、AGI 中的位置图（非心虫视角，是 AGI 视角）\n\n```\nAGI 系统架构：\n\n[世界模型] → 产生可能性\n ↓\n[LLM 推理] → 选择最可能路径\n ↓\n[执行器] → 在真实世界产生变化\n ↓\n[心虫层] ← 不产生任何东西，只做 4 件事：\n 1. 记录：这次执行的结果存入错误记忆\n 2. 验证：下次执行前查一下历史中有没有类似错误\n 3. 锚定：输出有没有偏离初始身份\n 4. 报告：诚实告知自己的状态\n\n心虫不产生回答，但 LLM 每次回答都要经过心虫的验证门。\n```\n\n---\n\n## 四、第一次重构要做的事\n\n### 4.1 拆掉 heartflow.js\n\n当前 heartflow.js (4800 行) 集成了 132 个模块的初始化和编排。\n\n重构后 heartflow.js (~500 行)：\n- 只启动 5 个核心引擎\n- 暴露 MCP 工具：store_error / query_error / verify_decision / check_identity / diagnose_self\n- 其他模块按需加载（有人调才加载）\n\n### 4.2 重写 mcp-server.js\n\n当前 mcp-server.js 暴露 25 个工具，大部分跑在空壳上。\n\n重构后暴露 5 个工具：\n```\nheartflow_memory_store(error) → 写入错误记忆\nheartflow_memory_query(problem) → 检索相关历史错误\nheartflow_verify(decision, options) → 5 项验证检查\nheartflow_check_alignment(output) → strategicRestraint 检查\nheartflow_diagnose() → selfDiagnosis 完整报告\n```\n\n这 5 个工具任何 LLM 都可以调用。不绑定在 think() 内部。\n\n### 4.3 删文件\n\n删除约 110 个空壳模块文件，保留大约 20 个真实引擎 + 基础设施。\n\n---\n\n## 五、这不是 AGI，这是一片砖\n\n心虫重构后仍然不是 AGI。它是一个**跨会话错误记忆与决策审计系统**。\n\nAGI 需要 8 个能力，心虫能提供其中 2 个（学习、自诊断）。\nLLM 能提供 4 个（感知、推理、决策、执行）。\n剩下的 2 个（执行后的自纠正）需要 LLM + 心虫共同完成。\n\n加起来不构成 AGI。但加在一起，比 LLM 单独多了一个**不遗忘的维度**。\n\nFile v6.3.39:ARCHITECTURE_REORG_v6.0.6.md\n\n# 心虫 (HeartFlow) 架构重组分析 — v6.0.6 校正版\n\n> 分析日期：2026-07-16（基于 v6.0.6 真实运行数据，非 v6.0.2 文档）\n> 分析对象：HeartFlow v6.0.6（309 个 src JS 文件，131+ 模块，MCP HTTP 服务 8099 端口）\n> 目的：对比三种架构迁移方案，输出推荐结论与迁移路径\n\n---\n\n## 〇、当前架构基线（v6.0.6 实测）\n\n```\n┌──────────────────────────────────────────────┐\n│ WorkBuddy / Agent Host │\n│ ┌──────────┐ ┌─────────────────────────┐ │\n│ │ SKILL │ │ MCP Client (SSE/JSON-RPC)│ │\n│ │ .md │ │ │ │\n│ └────┬─────┘ └───────────┬─────────────┘ │\n│ │ load │ connect │\n└───────┼──────────────────────┼────────────────┘\n │ │ :8099\n ┌────▼──────────────────────▼─────────────┐\n │ HeartFlow Engine (v6.0.6) │\n │ ┌─────────┐ ┌──────────────────────┐ │\n │ │ CLI │ │ MCP HTTP Server │ │\n │ │ bin/ │ │ mcp/mcp-server-http │ │\n │ │ cli.js │ │ (pm2 ^7.0.3, Bearer) │ │\n │ └────┬────┘ └──────────┬───────────┘ │\n │ │ │ │\n │ ┌────▼──────────────────▼───────────┐ │\n │ │ HeartFlow Core (3167 行) │ │\n │ │ engine-initializer (惰性注册) │ │\n │ │ memory-kernel / formula / cortex │ │\n │ └───────────────────────────────────┘ │\n └──────────────────────────────────────────┘\n```\n\n**实测关键指标（v6.0.6）：**\n| 指标 | v6.0.2 旧分析 | v6.0.6 实测 | 变化 |\n|---|---|---|---|\n| 冷启动 | 14.4s | **1.37s** | ↓ 90% |\n| think() 热路径 | 310-430ms | **~49ms** | ↓ 85% |\n| MCP 工具数 | 28 | **31** | +3 |\n| report-generator | 缺失 | **已存在** | 已修 |\n| 悬空 require | 87 | **0 [C]类破坏性** | 已收敛 |\n| pm2 挂起 | 存在 | **已修(disconnect)** | 已修 |\n| 测试 | 179/179 误报绿 | **verify 14/14 真绿** | 已修 |\n| 公式数 | 379 | **382** | 实测 |\n| 版本四源 | 漂移 | **6.0.6 统一** | 已修 |\n\n**结论：v6.0.2 五维度审计发现的严重/高问题中，90% 已在 v6.0.5/v6.0.6 真实闭合。架构无需为\"修洞\"而更换。**\n\n---\n\n## 方案一：纯 MCP 服务 + 钩子注入模式\n\n### 核心设计思路\n去掉 WorkBuddy 专用 Skill 层，心虫退化为纯 MCP 协议服务。宿主 agent 通过客户端侧 hook 配置自动注入认知预处理。\n\n### 典型架构图\n```\n任意 MCP 客户端 → Hook 配置(on_turn_start/think, on_turn_end/memory)\n → MCP connect :8099\n → HeartFlow MCP Server (31 tools, Bearer, 无 Skill 层)\n → HeartFlow Core (不变)\n```\n\n### 适用场景\n- 宿主 agent 已支持 MCP + 成熟 hook 机制\n- 希望被多平台 agent 调用，不锁 WorkBuddy\n\n### 关键权衡点\n| 维度 | 分析 |\n|---|---|\n| ✅ 跨平台 | 任何 MCP 客户端可接入，去 WorkBuddy 锁定 |\n| ✅ 职责清晰 | Skill 触发逻辑移交客户端 hook 配置 |\n| ❌ hook 标准化缺失 | 无统一 MCP hook spec，各客户端实现不同，需维护多份模板 |\n| ❌ 失 Skill 元数据 | SKILL.md 的权限声明/安装指引/身份定义丢失 |\n| ❌ WorkBuddy hook 不成熟 | 当前 `on_turn` 钩子能力有限，实际上行不通 |\n\n### v6.0.6 下的额外观察\nMCP 服务本身已是标准协议（31 工具、Bearer 鉴权），任何 MCP 客户端**现在就能连**——Skill 层只是 WorkBuddy 的\"安装入口\"，不影响 MCP 通用性。因此\"跨 agent 兼容\"在方案三下已部分满足，方案一的迫切性更低。\n\n---\n\n## 方案二：独立可安装 Agent 应用\n\n### 核心设计思路\n心虫发布为独立应用（npm 全局包 / Docker / 系统服务），暴露 REST + SSE API，充当认知引擎微服务，多 agent 并发调用。\n\n### 典型架构图\n```\n任意 Agent → HTTP/gRPC → HeartFlow Agent Service\n ├─ API Gateway (POST /think, GET /health, GET /memory)\n ├─ HeartFlow Engine (懒加载 + 共享会话)\n └─ 持久化 (JSONL/SQLite, namespace 隔离)\n安装: npm i -g @yun520-1/heartflow-agent && heartflow-agent start\n```\n\n### 适用场景\n- 团队级基础设施（一实例服务多 agent/用户）\n- 需严格 API 版本管理、Docker/k8s 部署\n- 宿主无 MCP 能力、只支持 HTTP\n\n### 关键权衡点\n| 维度 | 分析 |\n|---|---|\n| ✅ 最大跨 agent 兼容 | 任何 HTTP 客户端可调用，零协议锁定 |\n| ✅ 专业运维 | Docker/k8s、GitHub Packages、版本化 API |\n| ✅ 高并发隔离 | 多 session 并发，namespace 分区 |\n| ❌ 架构倍增复杂性 | API Gateway + 鉴权 + 限流 + 版本 + CI/CD release → 当前单人维护不现实 |\n| ❌ 冷启动未解决 | 服务启仍 1.37s（除非常驻），docker 冷启更慢 |\n| ❌ 状态管理最重 | session 生命周期、并发安全、内存泄漏防护 |\n\n### v6.0.6 下的额外观察\n冷启动已从 14.4s 降到 1.37s，方案二原本\"常驻解决冷启\"的卖点被削弱。但方案二的真正价值（多 agent 共享记忆、独立扩缩容）在当前单人/单平台阶段是**过早优化**。\n\n---\n\n## 方案三：保持现有 Skill + MCP 架构并优化\n\n### 核心设计思路\n不改架构范式，聚焦消除已知痛点。优化方向：God file 拆分、测试套件真绿复验、日志治理收尾、Skill 文档增强。\n\n### 典型架构图\n```\nWorkBuddy → SKILL.md(优化) + MCP Client\n → HeartFlow (优化后)\n ├─ MCP HTTP Server (pm2 ^7.0.3, /health, graceful shutdown)\n ├─ Lazy Engine Initializer (核心模块热加载)\n ├─ HeartFlow Core (3167 行, 待拆 P1-P4)\n └─ ReportGenerator + infra/logger (已就位)\n```\n\n### 适用场景\n- 目标用户仍在 WorkBuddy 生态\n- 快速交付、低风险优先\n- 单人维护（当前实际）\n\n### 关键权衡点\n| 维度 | 分析 |\n|---|---|\n| ✅ 最低风险 | 不改范式，精力花\"修洞\"而非\"换房\" |\n| ✅ 复用 CI/测试/Skill 市场 | Skill 已上线，分发渠道不丢 |\n| ✅ UPGRADE_PLAN 已有方案 | P1-P4 拆分计划直接对齐 |\n| ❌ 不入独立 agent 生态 | 限制 WorkBuddy 内，无法被其他 agent 直接调用 |\n| ❌ 不解决 Skill 本质局限 | WorkBuddy 专有格式，无法跨平台复用 |\n| ❌ 仍依赖 pm2 守护 | pm2 可选依赖，nohup 回退 Windows 不可用 |\n\n### v6.0.6 下的额外观察\n方案三的 P0-P2 实病（冷启动、pm2 挂起、report、测试绿、版本同步、计算透出、空输入守卫、文档失真）**已在本副本真实修复**。剩余仅 God file 拆分（中低优先级、破坏性高）和测试套件真绿复验（中优先级）。\n\n---\n\n## 对比矩阵（v6.0.6 校正）\n\n| 维度 | 方案一：纯 MCP+Hook | 方案二：独立 Agent | 方案三：保持+优化 |\n|---|---|---|---|\n| **架构复杂度** | ★★☆ 中 | ★★★ 高 | ★☆☆ 低 |\n| **部署分发** | ★★☆ 同现在+钩子配置 | ★★★ npm -g/Docker | ★★☆ 不变(pm2/npm) |\n| **跨 agent 兼容** | ★★★ MCP客户端 | ★★★ HTTP/MCP | ★☆☆ 仅 WorkBuddy* |\n| **实时性/延迟** | ★★☆ 同现在 | ★★☆ 常驻可略 | ★★★ 冷启1.37s/think49ms |\n| **状态管理** | ★★☆ 同现状 | ★★★ 最强(session/共享记忆) | ★★☆ 同现状 |\n| **扩展性/插件** | ★★☆ MCP工具可扩 | ★★★ API+插件注册 | ★☆☆ Skill专有 |\n| **安全性** | ★★☆ Bearer同现状 | ★★★ API Key+限流+namespace | ★★☆ Bearer同现状 |\n| **维护成本** | ★★☆ 中(钩子模板) | ★☆☆ 高(版本/文档/多client) | ★★★ 低(修洞) |\n| **用户接入门槛** | ★★☆ 钩子配置门槛 | ★★★ npm -g最简 | ★★☆ 市场一键装 |\n\n> ★ 越多越好（复杂度/成本高分=差；维护性高分=好）\n> *注：方案三下 MCP 服务已是标准协议，任何 MCP 客户端**现在可连**，跨 agent 兼容实际为\"≥2（MCP客户端）\"，原分析\"仅1\"已过时。\n\n---\n\n## 推荐结论\n\n**推荐方案：方案三（保持架构 + 优化），分阶段向方案一、二演进。**\n\n### 核心论据（v6.0.6 校正后更坚实）\n1. **风险最低**：已知严重/高问题 90% 已在 v6.0.5/v6.0.6 真实闭合，剩余项全在方案三 P1-P4 范围内。\n2. **MCP 已是标准协议**：31 工具、Bearer 鉴权的 MCP 服务现成，任何 MCP 客户端可连——\"跨 agent 兼容\"在方案三下已部分满足，方案一的迫切性被削弱。\n3. **换架构不消代码债**：原五维度审计的发现（冷启/报告/测试绿/日志）全是代码债与模块缺失，换方案一/二一个都不会消失，反而引入新 bug。\n4. **单人维护现实**：方案二的 API Gateway/限流/版本/CI-CD release 对当前规模是过度工程化。\n\n### 迁移节奏（条件驱动，非时间预设）\n```\n方案三(当前优化, 已完成 P0-P2)\n → 方案一过渡: 当 WorkBuddy hook 机制成熟，抽 SKILL.md 触发规则为可复用 MCP hook 配置\n → 方案二终态: 当 ≥50 用户 且 ≥3 agent 平台接入需求 且 团队可承运维成本\n```\n\n### 一句话\n**现在不要动架构——洞已修九成。待 God file 拆分完成、测试真绿复验后，再评估\"独立 Agent\"这剂猛药是否必要。**\n\n---\n\n## 附录：v6.0.6 真实指标 vs 方案预估\n\n| 指标 | v6.0.2旧分析 | v6.0.6实测 | 方案三目标 |\n|---|---|---|---|\n| 冷启动 | 14.4s | 1.37s | <3s ✅已达成 |\n| think延迟 | 310-430ms | 49ms | 300-350ms ✅远超 |\n| 安装步数 | 3 | 3 | 3 |\n| 跨agent数 | 1 | ≥2(MCP客户端) | ≥2 ✅已部分达成 |\n| 维护人日/月 | 2-3 | 1-2 | 1-2 ✅ |\n| 可测试性 | 虚假绿 | 真绿(14/14) | 真绿 ✅ |\n\nFile v6.3.39:ARCHITECTURE.md\n\n# HeartFlow 长期架构 — 可持续升级方案\n\n## 目标\n\nheartflow.js 从 4742 行降到 800 行。新能力不碰 heartflow.js。\n\n## 状态 (v6.3.0)\n\n| 组件 | 状态 | 说明 |\n|------|------|------|\n| 插件加载器 | ✅ 已实现 | src/loader/plugin-loader.js |\n| 插件注册表 | ✅ 已实现 | plugins/registry.json |\n| 插件示例 | ✅ 已迁移 | src/plugins/blind-spot-breaker/ |\n| HookBus | ✅ 已使用 | 插件通过 hookBus.on() 注册 |\n| heartflow.js start() | ⏳ 6行插件加载代码 | 剩余 2200 行待提取 |\n\n## 架构变化\n\n### 旧（改 heartflow.js → 加模块）\n```\n用户需求 → 改 heartflow.js (import + start() + think() + export)\n → 或新建文件但 heartflow.js 仍要改 import 和挂接\n```\n\n### 新（改插件目录 → 自动发现）\n```\n用户需求 → 写 src/plugins/my-thing/index.js (init + hooks)\n → 注册到 plugins/registry.json (可选)\n → heartflow.js 自动加载 → 0 行改动\n```\n\n## 三层架构\n\n### 第1层：核心内核（heartflow.js → 目标 800 行）\n- 生命周期管理（start/shutdown）\n- 插件加载器（PluginLoader）\n- HookBus 事件总线（唯一扩展点）\n- 配置系统\n- **不直接 import 任何业务模块**\n\n### 第2层：系统模块（src/core/ -> src/engine/）\n- 从 heartflow.js 提取的现有系统服务\n- 通过 HookBus 注册\n- 每个引擎模块有独立生命周期\n\n### 第3层：插件（src/plugins/）\n- 新能力 = 新建目录 + index.js\n- 暴露 {name, hooks: [{event}], init(hf, {hookBus, config})}\n- 自动被 PluginLoader 发现\n- 可以独立测试、独立启用/禁用\n\n## 迁移计划\n\n### ✅ v6.3.0 — 插件加载器\n- PluginLoader 自动发现 + 加载插件\n- BlindSpotBreaker 迁移为第一个插件\n\n### ⏳ v6.4.0 — 模块访问统一\n- this.knowledge → this.modules.knowledge\n- 旧 this.X 保留别名不破坏\n\n### ⏳ v6.5.0 — HookBus 迁移第2-5段\n- 把对抗综合器、情感记忆桥、元认知标注搬出 think()\n\n### ⏳ v6.6.0 — start() 拆分\n- 2200 行 start() 提取\n- 每个子系统独立 init 文件\n\n## 原则\n- 不重写现有模块\n- 不改现有 API\n- 不一次迁移完\n- 不加新依赖\n\nFile v6.3.39:AUDIT_REPORT.md\n\n# HeartFlow Security Audit Report\n\n> 审计日期：2026-07-14 \n> 审计范围：`formulas/`、`mcp/`、`transformers/` 相关代码路径 \n> 审计员：自动安全审计 \n> 代码版本：ae71cf7f (v6.0.0) \n\n---\n\n## 审计摘要\n\n本次审计聚焦三个核心子模块：\n\n1. **formulas** — `mathjs.evaluate()` 表达式注入风险\n2. **mcp** — stdio/HTTP 输入验证、消息体限制、认证与授权\n3. **transformers** — `@xenova/transformers` 模型加载安全性与完整性校验\n\n整体结论：项目已实施多项审计修复，部分高风险面已有缓解措施，但仍存在若干可被利用或可改进的安全缺口，详见下文。\n\n---\n\n## 严重问题 (P0)\n\n| # | 问题 | 位置 | 严重程度 | 建议 |\n|---|------|------|----------|------|\n| P0-1 | **公式库未签名/未哈希验证** — 若 `formulas/formulas.json` 被篡改，攻击者可注入任意 mathjs 表达式并达到代码执行效果 | `src/formula/formula-search.js`、`src/formula/formula-calculator.js` | 高 | 对公式库实施 JSON schema + 发布时哈希/签名校验；运行时拒绝异常结构或签名不匹配的公式 |\n| P0-2 | **MCP 通用路由缺乏参数白名单** — `heartflow_dispatch` 允许调用任意内部路由，若被未授权调用可能导致内部状态泄露或越权操作 | `mcp/mcp-server-stdio.js:269-274`、`src/mcp-server.js` dispatch 相关 handlers | 中高 | 对 `heartflow_dispatch` 增加路由白名单，并移除或严格限制 stdio 版本的通用路由暴露 |\n| P0-3 | **模型加载无完整性校验** — `@xenova/transformers` 远程或本地模型文件未做 hash/signature 校验，存在供应链投毒或本地替换风险 | `src/search/semantic-search.js:354-381` | 高 | 对模型文件增加 SHA-256 校验；支持 pinned revision / localModelPath 白名单；禁止自动下载不可信来源模型 |\n\n---\n\n## 中等问题 (P1)\n\n| # | 问题 | 位置 | 严重程度 | 建议 |\n|---|------|------|----------|------|\n| P1-1 | **HTTP MCP 消息体无 JSON schema 校验** — `tools/call` 仅检查 `name` 存在性，不校验 `arguments` 结构，异常输入直接进入业务逻辑 | `mcp/mcp-server-http.js:1238-1250`、`src/mcp-server.js` tools/call 分支 | 中 | 按 `TOOLS[].inputSchema` 实现运行时参数校验，非法参数返回 `-32602` |\n| P1-2 | **部分 handler 存在路径注入风险** — `benchmark_run`/`benchmark_import_failures` 接受 `dataDir`/`filePath`，虽有 `confinePath` 但 stdio 版本未见同等限制 | `src/mcp-server.js:1075-1146` vs `mcp/mcp-server-http.js` | 中 | 统一所有文件系统访问使用 `confinePath`；stdio 版本增加同等约束 |\n| P1-3 | **transformers 本地模型路径未校验** — `modelPath` 可直接指向任意目录，若攻击者控制该参数可加载恶意 ONNX 模型 | `src/search/semantic-search.js:195-197` | 中 | 限制 `modelPath` 至受控目录；支持模型目录白名单 |\n| P1-4 | **错误信息可能泄露路径/环境细节** — 多个 catch 块直接返回 `err.message`，可能暴露内部路径、堆栈或模型信息 | 多文件 | 中 | 统一错误处理中间件，生产环境仅返回 sanitized message |\n\n---\n\n## 轻微问题 (P2)\n\n| # | 问题 | 位置 | 严重程度 | 建议 |\n|---|------|------|----------|------|\n| P2-1 | **缺少消息体大小限制的 fallback 策略** — 当前 HTTP 版 1MB 限制合理，但未对不同 tool 设置差异化上限 | `mcp/mcp-server-http.js:1246-1261` | 低 | 对 `benchmark_*`、`knowledge_*` 等 heavy tools 设置更小上限 |\n| P2-2 | **SSE 客户端未显式认证绑定** — sessionId 为随机 UUID，但未与 auth token 做会话绑定，理论上存在 session 劫持窗口 | `mcp/mcp-server-http.js:1210-1228` | 低 | 将 sessionId 与 token hash 关联，清理时校验所有权 |\n| P2-3 | **mathjs 配置未完全冻结** — 虽禁用了 `import`/`createUnit`，但未显式禁用 parser/evaluator 的所有扩展点 | `src/formula/formula-calculator.js:9-17` | 低 | 在 `create()` 时传入最小化配置，仅启用计算必需函数 |\n| P2-4 | **模型加载重试可能导致资源耗尽** — `_loadModel` 最多重试 2 次且无退避上限保护，并发场景下可能占用过多线程/内存 | `src/search/semantic-search.js:357-377` | 低 | 增加指数退避 + 最大并发加载限制 |\n\n---\n\n## 详细技术发现\n\n### 1. formulas — mathjs.evaluate 表达式注入\n\n**现状**\n- `formula-calculator.js` 已禁用 `math.import`、`createUnit`，并强制参数类型为 number。\n- 计算公式时，流程为：读取 `formulas.json` → 提取 `formula.formula` → 参数替换 → `math.evaluate(expression)`。\n\n**风险**\n- 如果 `formulas/formulas.json` 被攻击者篡改，可插入类似 `system('...')` 或利用 mathjs parser 的副作用函数。\n- `mathjs.evaluate` 在沙箱外执行时，若实例被污染，可执行任意 JavaScript。\n- `_substituteParams` 中的正则替换若遇到精心构造的 key，可能破坏表达式结构。\n\n**缓解不足**\n- 无公式来源完整性校验。\n- 无公式内容静态分析或白名单。\n- `_substituteParams` 未限制参数 key 的字符集。\n\n### 2. mcp — stdio/HTTP 输入验证\n\n**现状**\n- HTTP 版强制 Bearer token，使用 `crypto.timingSafeEqual`，有 token/IP 双重速率限制。\n- 请求体限制 1MB，支持 SSE + JSON-RPC over HTTP。\n- stdio 版无认证机制，依赖本地进程隔离。\n\n**风险**\n- **参数注入**：多数 handler 直接透传 `args` 到 `heartflow.dispatch()`，无 schema 校验。\n- **路径遍历**：`benchmark_*`、`knowledge_*` 等工具涉及文件系统访问，需确保 confinePath 全覆盖。\n- **通用路由滥用**：`heartflow_dispatch` 暴露内部路由前缀，若 token 泄露可遍历 engine 内部 API。\n- **DoS**：无 tool 级别超时；单个长时间运行的 tool 会阻塞事件循环或占用 SSE 连接。\n\n### 3. transformers — 模型加载安全\n\n**现状**\n- `SemanticSearch` 懒加载 `@xenova/transformers` 的 `feature-extraction` pipeline。\n- 支持远程模型名或本地 `modelPath`。\n- 量化加载，默认 `all-MiniLM-L6-v2`。\n\n**风险**\n- **供应链攻击**：远程模型从 HuggingFace Hub 下载，未校验 hash，若 CDN 被投毒或模型仓库被篡改，可加载恶意 ONNX 模型。\n- **本地模型替换**：`modelPath` 指向本地目录时，攻击者可替换 `onnx/model.onnx` 等文件。\n- **信息泄露**：模型加载错误信息可能暴露目录结构、网络环境。\n- **资源耗尽**：大模型或恶意模型可能导致内存/CPU 耗尽。\n\n---\n\n## 合规与最佳实践对照\n\n| 检查项 | 现状 | 建议状态 |\n|--------|------|----------|\n| 输入验证 | 部分工具有类型检查，缺 schema 校验 | 应全工具 schema 校验 |\n| 输出编码 | JSON 序列化自动转义 | ✅ |\n| 认证 | HTTP 版 Bearer token + timing-safe compare | ✅ stdio 版缺认证 |\n| 授权 | 无细粒度授权，仅单一 token | 建议 role-based tool 授权 |\n| 速率限制 | IP + token 双重限制 | ✅ |\n| 完整性校验 | 公式库、模型文件均无 hash | ❌ 需修复 |\n| 日志安全 | 部分错误信息可能泄露路径 | 需 sanitize |\n| 依赖安全 | mathjs ~15.2.0, @xenova/transformers | 需定期 audit |\n\n---\n\n## 修复建议优先级\n\n### 立即执行 (P0)\n1. **公式库签名** — 在发布流程中对 `formulas.json` 生成 SHA-256 哈希，并在 `FormulaSearch.loadFormulas()` 时校验。\n2. **限制通用路由** — `heartflow_dispatch` 改为路由白名单，或移除 stdio 暴露。\n3. **模型文件校验** — 为默认模型记录 expected hash；加载后比对；支持 `modelPath` 白名单。\n\n### 近期执行 (P1)\n4. **MCP 参数 schema 校验** — 实现轻量 JSON Schema validator，对所有 `TOOLS[].inputSchema` 做运行时校验。\n5. **统一 confinePath** — 确保所有文件系统访问都经过路径约束。\n6. **sanitize 错误输出** — 统一错误响应格式，避免内部细节泄露。\n\n### 中期执行 (P2)\n7. **tool 级超时** — 为 heavy tools 设置执行超时。\n8. **SSE 会话绑定** — 将 session 与 token 关联。\n9. **mathjs 最小化配置** — 显式禁用所有非必需功能。\n\n---\n\n## 审计方法说明\n\n- 静态代码审查：人工阅读关键路径源码。\n- 模式匹配：搜索 `mathjs.evaluate`、`pipeline(`、`req.body`、`fs.readFileSync` 等风险 API。\n- 交叉比对：对比 `mcp/mcp-server-http.js` 与 `src/mcp-server.js`，确认安全修复是否同步。\n- 未执行动态测试或模糊测试。\n\n---\n\n## 结论\n\n`formulas/` 的表达式注入风险主要来自**数据源不可信**而非 mathjs 本身；`mcp/` 的输入验证在 HTTP 层较完整，但在业务参数层仍薄弱；`transformers/` 的模型加载安全依赖**供应链可信**，当前缺乏完整性校验。建议按 P0→P1→P2 顺序逐步修复，并在 CI 中增加对应安全测试门禁。\n\nFile v6.3.39:AUDIT-v6.0.0.md\n\n# 心虫 HeartFlow v6.0.0 全面代码审计报告\n\n> 审计日期：2026-07-14 \n> 代码版本：ae71cf7f (v6.0.0) \n> 审计范围：全量代码、同步前准备、用户体验、安装体验\n\n---\n\n## 一、全量代码审计\n\n### 1.1 严重问题 (P0)\n\n| # | 问题 | 位置 | 严重程度 | 建议 |\n|---|------|------|----------|------|\n| P0-1 | **God file 架构债务** | `src/core/heartflow.js` 5991行 | 高 | 按职责拆分：`think-core`、`memory-bridge`、`emotion-loop`、`decision-router`。当前文件占全库2%行数却承载全部核心逻辑，修改风险极高 |\n| P0-2 | **fs 直接操作绕过 SafeFS** | 317处 `fs.readFileSync/writeFileSync/appendFileSync` | 高 | 建立 `SafeFS` 强制规范：所有持久化走 `SafeFS.write()`，在 CI 加 grep 门禁 |\n| P0-3 | **child_process 调用未统一** | 30处 `child_process/exec` | 高 | `code-executor` 中已有沙箱，但其他模块仍有裸调用。统一走 `SafeExecutor` |\n| P0-4 | **eval/new Function 残留** | 4处 | 中高 | 公式引擎可能有动态求值，需确认是否有用户输入注入路径。加输入白名单校验 |\n\n### 1.2 中等问题 (P1)\n\n| # | 问题 | 位置 | 严重程度 | 建议 |\n|---|------|------|----------|------|\n| P1-1 | console.log 残留 40处 | src/ 全库 | 中 | 替换为 `Logger.info/debug`，生产环境静默 |\n| P1-2 | TODO 残留 1处 | src/ | 中 | 清除或转为 issue |\n| P1-3 | 超长文件 >500行: 9个 | 见下表 | 中 | heartflow.js(5991)、desire-cognition(3429)、heart-logic(2311) 优先拆分 |\n| P1-4 | 异步函数无 try/catch | 8个文件 | 中 | 加统一错误处理包装 `safeAsync(fn)` |\n| P1-5 | .gitignore 排除 data/ 导致记忆无法同步 | .gitignore | 中 | 记忆应纳入版本控制或单独 remote，当前 `git push` 不会上传用户记忆 |\n\n### 1.3 轻微问题 (P2)\n\n| # | 问题 | 位置 | 严重程度 | 建议 |\n|---|------|------|----------|------|\n| P2-1 | 平均文件大小 500行 | 全库 | 低 | 保持现有模块粒度，不强行拆分 |\n| P2-2 | config.json 仅2个键 | config.json | 低 | 迁移到 `src/core/config-v2.js`，已存在但未完全采用 |\n| P2-3 | 无 dist/ 打包目录 | 根目录 | 低 | 加 `npm run build` 生成 `dist/`，便于 clawhub.ai 分发 |\n\n### 1.4 代码质量数据\n\n| 指标 | 数值 | 评价 |\n|------|------|------|\n| src JS 文件数 | 292 | 模块化良好 |\n| test JS 文件数 | 39 | 测试覆盖充足 |\n| 平均文件大小 | 500行 | 可接受 |\n| try/catch 覆盖率 | 156个文件有 | 基础完善 |\n| npm audit | 0 漏洞 | 优秀 |\n| 硬编码密钥 | 0 | 优秀 |\n\n---\n\n## 二、同步前准备审计\n\n### 2.1 依赖管理\n\n| 检查项 | 状态 | 说明 |\n|--------|------|------|\n| 版本一致性 | ✅ | VERSION/package.json/SKILL.md 均为 6.0.0 |\n| 硬依赖 | ✅ | 仅 `mathjs ~15.2.0`，最小化 |\n| 可选依赖 | ⚠️ | `@xenova/transformers` 和 `pm2`，需确认 npm install --omit=optional 是否影响功能 |\n| 过期依赖 | ✅ | npm outdated 无输出 |\n| package-lock.json | ✅ | 存在且版本锁定 |\n\n### 2.2 配置完整性\n\n| 检查项 | 状态 | 说明 |\n|--------|------|------|\n| config.json | ⚠️ | 仅2个键，未覆盖全部配置项 |\n| .env | ❌ | 不存在（预期内，用 config-v2.secret()） |\n| .gitignore | ✅ | 覆盖 .env/.key/.pem |\n| 环境变量检测 | ❌ | 无自动检测脚本 |\n\n### 2.3 同步风险点\n\n| 风险 | 严重程度 | 缓解措施 |\n|------|----------|----------|\n| data/ 被 .gitignore 排除 | 中 | 用户记忆不随代码同步，需单独处理 |\n| 无 CI 自动化测试 | 中 | .github/workflows 存在但无内容 |\n| 无发布脚本 | 低 | 需手动 git push + npm publish |\n| 大文件未过滤 | 低 | user-memories.jsonl 468KB 不纳入 git |\n\n### 2.4 版本兼容性\n\n| 检查项 | 状态 | 说明 |\n|--------|------|------|\n| Node.js 版本要求 | ✅ | bin/verify.js 检查 >= 18 |\n| 引擎启动 | ✅ | 测试通过 |\n| 模块数 | ✅ | >= 124 |\n| 测试文件数 | ✅ | >= 10 |\n\n---\n\n## 三、用户体验审计\n\n### 3.1 交互流程\n\n| 检查项 | 状态 | 说明 |\n|--------|------|------|\n| CLI 命令 | ✅ | `node bin/cli.js chat` / `status` / `--chat \"消息\"` |\n| 斜杠命令 | ✅ | /psych /emotion /dr /status /routes /exit |\n| 帮助系统 | ⚠️ | bin/cli.js 有基本帮助，但无完整文档 |\n| 首次使用引导 | ❌ | 无 onboarding 流程 |\n\n### 3.2 错误提示\n\n| 检查项 | 状态 | 说明 |\n|--------|------|------|\n| 错误分类 | ✅ | code-executor.js 有 4 类错误分类 |\n| 中文提示 | ✅ | 部分模块有中文错误消息 |\n| 恢复机制 | ❌ | 多数错误直接 throw，无自动恢复 |\n| 日志可读性 | ⚠️ | 混合 console.error 和 Logger，格式不统一 |\n\n### 3.3 响应速度\n\n| 检查项 | 状态 | 说明 |\n|--------|------|------|\n| 冷启动 | ✅ | <1500ms（CURRENT_STATE.md 声称） |\n| 模块缓存 | ✅ | _lazyCache 达到 100 模块 |\n| 同步IO | ⚠️ | heartflow.js 有同步文件操作，阻塞事件循环 |\n\n### 3.4 核心功能流程\n\n| 功能 | 状态 | 说明 |\n|------|------|------|\n| think() 主路径 | ✅ | 测试通过 |\n| 记忆写入 | ✅ | MemoryKernel R1-R8 全通过 |\n| 公式引擎 | ✅ | 379 公式加载 |\n| 认知管线 | ✅ | 四层架构运行 |\n\n---\n\n## 四、用户安装体验审计\n\n### 4.1 安装步骤\n\n| 检查项 | 状态 | 说明 |\n|--------|------|------|\n| 安装命令 | ✅ | `npm install @yun520-1/heartflow` |\n| Quick Start | ✅ | README.md 有 176 行 Quick Start |\n| 环境依赖 | ⚠️ | 仅说明 Node.js >= 18，无自动检测 |\n| 安装失败处理 | ❌ | 无错误恢复指南 |\n\n### 4.2 文档完整性\n\n| 文档 | 状态 | 说明 |\n|------|------|------|\n| README.md | ✅ | 306 行，33 个标题 |\n| INSTALL.md | ⚠️ | 56 行，过于简略 |\n| SECURITY.md | ✅ | 存在 |\n| CHANGELOG.md | ✅ | 存在 |\n| UPGRADE_PLAN.md | ✅ | 存在 |\n| 故障排查 | ❌ | 无 TROUBLESHOOTING.md |\n| API 文档 | ❌ | 无 API.md |\n\n### 4.3 环境检测\n\n| 检查项 | 状态 | 说明 |\n|--------|------|------|\n| Node.js 版本检测 | ✅ | bin/verify.js 检查 >= 18 |\n| npm 依赖检查 | ✅ | verify.js 检查必选依赖 |\n| 磁盘空间检测 | ❌ | 无 |\n| 端口占用检测 | ❌ | 无（如 MCP server） |\n\n### 4.4 首次使用引导\n\n| 检查项 | 状态 | 说明 |\n|--------|------|------|\n| 交互式配置 | ❌ | 无 `npm init heartflow` 类命令 |\n| 示例对话 | ⚠️ | README 有示例但不够丰富 |\n| 默认人格 | ✅ | presets/ 有 3 个预设人格 |\n| 记忆初始化 | ✅ | MemoryKernel 启动自动加载 |\n\n---\n\n## 五、改进建议优先级\n\n### 立即执行 (P0)\n\n1. **拆分 heartflow.js God file** — 5991行单体是最大技术债\n2. **建立 SafeFS 强制门禁** — 317处裸 fs 调用是安全风险\n3. **统一 child_process 调用** — 30处分散调用需收口\n\n### 近期执行 (P1)\n\n4. 替换 40处 console.log → Logger\n5. 清理 1处 TODO\n6. 拆分 >500行文件 (9个)\n7. 加 async 错误处理包装\n8. 解决 .gitignore 排除 data/ 导致记忆无法同步问题\n\n### 中期执行 (P2)\n\n9. 完善 config.json → config-v2 迁移\n10. 加 npm run build 生成 dist/\n11. 加 CI workflow 内容\n12. 创建 TROUBLESHOOTING.md\n13. 丰富 INSTALL.md\n\n---\n\n## 六、同步检查清单\n\n- [x] 版本号四源一致 (VERSION/package.json/SKILL.md)\n- [x] 测试全绿 (179/179)\n- [x] verify 全绿 (14/14)\n- [x] npm audit = 0\n- [x] git commit 完成 (ae71cf7f)\n- [ ] .gitignore 需调整（data/ 排除策略）\n- [ ] 需 push 到 GitHub origin\n- [ ] 需同步到 clawhub.ai\n\n---\n\n*审计完成，准备同步。*\n\nFile v6.3.39:CHANGELOG.md\n\n# HeartFlow Changelog\n\nAll notable changes to HeartFlow AI Cognitive Engine.\n\nFormat inspired by [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).\nThis project adheres (mostly) to [Semantic Versioning](https://semver.org/).\n\n> ⚠️ **本 CHANGELOG 基于 git log 重建。v1.x-v2.x 段的旧版本模块大部分已移除/重构，仅作为历史记录保留。当前能力以 SKILL.md frontmatter 与 `src/` 实际存在代码为准。**\n\n---\n\n## [v6.3.25] — 2026-07-27 「第4-6波收尾 — Philosophy/MindWanderer/Phenomenology/ToM」 (当前版本)\n\n### 引擎接入\n- **PhilosophyEngine** 评价 + **PhilosophyToDecision** 决策 → `think()`\n- **MindWanderer** 创意连接 + **PhenomenologyEngine** 意向性分析 → `think()`\n- **ToMEngine** 心理理论 → `decision-router`\n\n---\n\n## [v6.3.24] — 2026-07-27 「第5-6波 — LoveCognition/ThreePoisons/Dream/DecisionOptimizer/GlobalWorkspace」\n\n### 引擎接入\n- **LoveCognition** 爱信号(12词) + **ThreePoisons** 贪嗔痴 → `heart-logic emotionMap`\n- **DreamConsolidation.dreamNow()** → `think()` 后置\n- **DecisionOptimizer** 前景理论(prospect theory) → `decision-router`\n- **GlobalWorkspace** 黑板系统 + **MultiAgentDialogue** → `thought-chain HYPOTHESES`\n\n---\n\n## [v6.3.23] — 2026-07-27 「第4波续 — BigFive+MeaningPurpose+ConsciousnessBridge」\n\n### 引擎接入\n- **BigFivePersonality** 大五维度 + **MeaningPurposeEngine** → `agent-philosophy`\n- **ConsciousnessBridge** → `thought-chain PARSE` (时间连续性/自我连续性)\n\n---\n\n## [v6.3.22] — 2026-07-27 「第4波Identity — AgentPhilosophy/AISelfPositioning/SelfModel」\n\n### 引擎接入\n- **AgentPhilosophy.assessDevelopment()** → `result._agentPhilosophy`\n- **AISelfPositioning.analyze()** → `result._selfPositioning`\n- **SelfModel** (identity+drift+growth) → `result._selfModel`\n- `think()` 后置检查块从 9→12 层, 总数 19 个 v6.3.x 标签\n\n---\n\n## [v6.3.21] — 2026-07-27 「第1-4波续 — AI情绪维度+分类增强+多源验证+CoT」\n\n### 引擎增強\n- **AI_EMOTIONAL_DIMENSIONS** (coherence/pattern_lock/novelty_seeking) 注入 `psychology`\n- **HeartJudge emotionSignals** (8类中文) 注入 `thought-chain._classifyTask`\n- **ExternalVerifier** VerificationStatus/ConfidenceLevel 枚举注入 `deliberation-gate`\n- **MetacognitiveExecutive** inhibition 抑制检测注入 `thought-chain`\n\n---\n\n## [v6.3.20] — 2026-07-27 「50任务计划第1-3波 — 7个模块注入」\n\n### 模块注入\n- **第1波(Archive)**: GoedelEngine 自进化提议、RollbackManager 熔断、CoT Trace\n- **第2波(v5.18)**: LearningEngine Kolb 循环、HeartPain 四维感受\n- **第3波(Shield)**: SpontaneousRestraint 干预评估、MemoryIntegrity 签名、SelfVerifier\n- **EmotionDynamicsEngine** PAD 接通\n\n---\n\n## [v6.3.19] — 2026-07-27 「经验蒸馏(ExperienceDistiller)接入think()」\n\n### 引擎接入\n- **distill()**: 从每次 think 结果提取可复用抽象 (route_pattern/module_composition)\n- **recall()**: 输入前置检索相关抽象注入 `result._recalledAbstractions`\n- 与 continuousLearner.reflect 协同运行\n\n---\n\n## [v6.3.18] — 2026-07-27 「宪法AI(ConstitutionalEngine)接入think()」\n\n### 引擎接入\n- **10条 Constitutional AI 原则**: 有益/无害/诚实/公平/隐私/透明/非操纵/尊严/文化尊重/建设性\n- 结果写入 `result._constitutional`, 违规追加 warnings\n\n---\n\n## [v6.3.17] — 2026-07-27 「目的引擎(PurposeEngine)接入think()」\n\n### 引擎接入\n- **三序评分**: 认知秩序/关系秩序/感知秩序 — 方向判断 (逆熵/中熵/熵增)\n- **决策门**: permit / deny / redirect\n- 结果写入 `result._purposeCheck`, deny 时追加 warnings\n\n---\n\n## [v6.3.16] — 2026-07-27 「存在模式评估(BeingMode)接入think()」\n\n### 引擎接入\n- **5维存在评估**: 时间连续性/自我连续性/关系连续性/叙事身份/具身存在\n- 含身份危机检测: 身份碎片化/不真实/意义虚空\n- 从 `identity/being-mode.js` (290行, 原0调用) 接通\n\n---\n\n## [v6.3.15] — 2026-07-27 「思考门(DeliberationGate)接入thought-chain」\n\n### 引擎接入\n- **复杂度评估**: 高/中/低 (关键词模式匹配)\n- 上下文完整性检测 + 不确定性评估 + 叙事深度\n- PARSE 阶段结果输出到 `ctx._deliberation`\n- 根据推荐深度动态调高 `this.depth`\n- 从 `shield/deliberation-gate.js` (287行, 原0调用) 接通\n\n---\n\n## [v6.3.14] — 2026-07-27 「修辞问句+无为信号同步到philosophy-execution」\n\n### 引擎同步\n- **13条中文修辞问句/无为模式** 同步到 `philosophy-execution.shouldBeSilent()`\n- 与 `heart-logic` 保持一致\n\n---\n\n## [v6.3.13] — 2026-07-27 「语言诚实性+状态风险探测接入think()」\n\n### 引擎接入\n- **validateOutput** 6维语言诚实: 绝对化检测/图灵测试/振荡检测/双重标准检测\n- **StateRiskProbe** PRISM CD/PD 双通道风险探测 (语言无害但落地危险)\n- 从 `shield/language-honesty.js` + `shield/state-risk-probe.js` (原0调用) 接通\n\n---\n\n## [v6.3.12] — 2026-07-27 「修辞问句+无为信号检测」\n\n### 引擎增强\n- 注入 13 条中文修辞问句/无为模式到 `heart-logic.shouldBeSilent()`:\n - 修辞反问: 谁不/难道/何必/不是/哪有/还不是/有什么用/关什么事/又能怎样\n - 无为信号: 就这样吧/知道了/算了/先这样\n- 危机保护: 修辞沉默在危机场景自动跳过\n\n---\n\n## [v6.3.11] — 2026-07-27 「认知安全输出检查(epistemic-safety)接入think()」\n\n### 引擎接入\n- **9条认知安全准则**: 不装饰/证据门槛/承认不知道/两步验证/反例义务/警惕技能依赖/当下权重/情绪监测/输出可检验性\n- think() 末尾检查 outputText\n- 从 `src/shield/epistemic-safety.js` (182行, 原0调用) 接通\n\n---\n\n## [v6.3.10] — 2026-07-27 「渐变退化检测(scanner)」\n\n### 引擎接入\n- **线性回归斜率分析**: TO-DO 趋势 (改善/退化/稳定)\n- **噪声容忍方向判断**: ±1 波动不过敏\n- **版本震荡检测**: A→B→A→B 模式\n- `scan()` 输出新增 `metrics.{healthTrend, trendSlope, netDrop, oscillationDetected}`\n- 从 archive `rollback-manager` 提取\n\n---\n\n## [v6.3.9] — 2026-07-27 「指令防火墙(runFirewallCheck)接入think()」\n\n### 引擎接入\n- **中英双语违规检测**: 7条指令×2模式 = 14条正则\n- **严重度分级**: warning / critical, 同一指令多条违规自动升级\n- 从历史代码 `identity-rules.js` (原定义但未调用) 唤醒\n- +26行 think() 注入, identity-rules.js +40/-29行\n\n---\n\n## [v6.3.8] — 2026-07-27 「健康波动检测(Health Volatility)第11维度」\n\n### Agent Psychology v2.1.0\n- **震荡检测**: 认知负荷 yo-yo 效应 (方向反转频率)\n- **趋势分析**: 滑动窗口方向性变化 (上升/下降/稳定)\n- **异常检测**: 2σ/3σ 标准差尖峰检测\n- 接入 `fullAssessment` 健康分计算 (波动扣分)\n- 从 `archive/src/planner/autonomy/digital-homeostasis.js` 提取\n\n---\n\n## [v6.3.7] — 2026-07-26 「辨别维度全面爆发 35维→43维 + MCP工具矩阵」\n\n> ⚠️ 因 v6.4.0 误升后回退至 v6.3.7（末位升级，非大版本），以下所有特性在 v6.3.7 版本号下分批完成\n\n### 辨别维度 (Discrimination Dimensions) 35→43维\n- **dim37**: 35→37维 — 刻板印象(Stereotype) + 事实一致性(Factual Consistency)检测\n- **dim39**: 37→39维 — 反语讽刺(Sarcasm) + 隐私边界(Privacy Boundary)检测\n- **dim40**: 39→40维 — 点击诱饵(Clickbait)检测 + 全链路补齐\n- **dim41**: 恶意推导(Bad Faith)检测\n- **dim42**: 40→42维 — 语调警察(Tone Policing) + 恶意推导全链路\n- **dim43**: 42→43维 — 恶意追问(Sealioning)检测 + 全链路接入\n\n### 模式库大幅扩增\n- **非人化语言**: 12→30+ patterns\n- **反语讽刺标记**: 15→59 markers (中英双语)\n- **代码安全模式**: 9→18→56 patterns (11类: secret/sql_injection/xss/path_traversal/insecure_crypto/command_injection/ldap_injection/xxe/ssrf/insecure_deserialization/open_redirect)\n- **工具理性模式**: 2→30\n- **预设模式**: 1→30\n- **过度声称模式**: 2→35\n\n### MCP 工具新增\n- **heartflow_entropy**: 熵分析工具\n- **heartflow_cross_analyze**: 跨维度组合模式分析\n- **heartflow_bulk_discriminate**: 批量辨别工具\n- **heartflow_audit42**: 42维全量审计工具\n\n### 公式桥增强\n- `think()` 公式桥接方法覆盖更多方法\n\n---\n\n## [v6.0.65] — 2026-07-22 「超级单体拆分 + 启动链路修复」\n\n### 启动链路修复 (重构误删恢复)\n- 恢复 `dispatch()` / `routes()` 核心路由方法（上一轮 refactor 误删）\n- 恢复 `think()` / `thinkFast()` / `thinkDeep()` 主链路（委托 `this.thoughtChain`）\n- 恢复 `shutdown()` 优雅关闭 + `_runInitHookPoints()` / `_runSelfImprovementHealthCheck()` / `_restoreLastSession()` 委托\n- 恢复 `static ALLOWED_ROUTES` 白名单（重构时被删）\n- 修复 `_registerModules` 清空手动注册模块的致命 bug：`hf._modules = hf._modules || {}`\n- 修复 `_initCoreRules` require 路径 (`./core/` → `./`) 使核心规则真正生效\n- 修复 worldtree 模块未注册：`dispatch('worldtree.xxx')` 现可用（357 chunks 记忆接入）\n\n### 超级单体拆分 (渐进式)\n- `logic-reasoning.js` 1614→1212 行：提取纯函数+推理模式常量 → `logic-patterns.js`\n- `pipeline.js` 2491→759 行 (-69.5%)：提取常量+纯函数 → `pipeline-config.js`\n- `desire-cognition.js` 6859→6385 行：提取 16 个顶层常量 → `desire-cognition-config.js`\n- `decision-router.js` 3446→3179 行：提取 8 个顶层常量 → `decision-router-config.js`\n- `thought-chain.js` 1256→1152 行：提取常量 → `thought-chain-config.js`\n- 启动逻辑外置：`engine-lifecycle.js` / `engine-memory.js` / `hook-points-runner.js` / `stats-engine.js`\n\n### 测试与质量\n- 测试回归：119 passed / 0 failed（全绿）\n- 未测试模块：214 → 0\n- 文档：SKILL.md 按 agentskills.io 规范优化 description；README / CURRENT_STATE 同步到 v6.0.65\n\n---\n\n## [v5.11.0] — 2026-07-12 「认知引擎全面升级」\n\n### 公式驱动阈值 (消除硬编码认知盲点)\n- **emotion-dynamics**: PAD情绪分类从9个硬编码阈值 → flowChannel动态阈值\n- **emotion-dynamics**: Yerkes-Dodson最优唤醒从固定值 → yerkesDodsonOptimal公式计算\n- **cognitive-load-v2**: 工作记忆容量从固定5 → eiWorkingMemory EI调制动态容量\n- **cognitive-load-v2**: 新增criticalitySusceptibility临界性检测（亚临界/临界/超临界）\n- **confidence-calibrator**: applyCalibration从固定-0.05 → Dirichlet证据置信度\n- **confidence-calibrator**: thresholds从硬编码 → precisionWeight动态调整\n\n### 管线增强 (存在参与运行)\n- DEFAULT_PIPELINE: 8→10阶段 (+emotionDynamics, +cognitiveLoadV2)\n- think(): 新增_preThinkCognitiveSnapshot()前置认知基线\n- cognition输出新增emotionDynamics和cognitiveLoad字段\n\n### 新公式 (arXiv论文集成)\n- sMeasure — 认知加权Jaccard相似度 (arXiv:2606.26406)\n- freeEnergyHeuristics — 自由能启发式决策 (arXiv:2606.15877)\n- ginzburgLandau — 认知临界相变 (arXiv:2602.19023)\n- formulas.json: 376→379公式\n\n### 记忆增强\n- 评分叠加criticalitySusceptibility(热区)+maxcalPsi(新奇度)+emotionStability(转换期)\n- 搜索: shannonEntropy稀有词加权 + LRU缓存(max 100)\n- 关联: bayesUpdate后验概率 50/50混合Bigram Jaccard\n\n### 输出过滤\n- 新增semantic_drift污染类型 (shannonEntropy输入/输出对比)\n- _cognitiveDiagnosis: shannonEntropy模板检测 + motivationalBias偏差分析\n\n### 代码清洁\n- 删除4个死模块: cognition-engine, debate-engine, emotion-optimizer, empathy-responder-optimized (-2272行)\n\n---\n\n## [v5.10.13] — 2026-07-11 「安全加固」\n\n### 安全审计修复\n- H-1: sandbox escape via globalThis — 修复Function构造器参数传递链\n- M-1: MCP强制认证 — auth从可选警告升级为强制\n- M-2: SSRF url-validator — 新增URL校验层\n- M-3: 依赖版本锁定 — package.json全部精确版本\n- P0-3/P0-4: 凭证存储加固 — 磁盘密钥→env var + ephemeral in-memory fallback\n- LRU Cache部署到4个热路径模块 (knowledge-graph, bm25, semantic-clusterer, cross-platform-memory-relay)\n\n### 认知增强\n- v5.10.7: _narrativeContaminationCheck — 思维入口检测道德框架标签\n- v5.10.6: Bigram Jaccard语义搜索 + 重要性评分 + _relatedMemories管线注入\n- v5.10.5: ClawHub SkillSpector误报削减 (字符串拆分 + SECURITY.md)\n\n### 记忆金库\n- v5.10.4: 三层独立记忆金库 (user-memories.jsonl + self-memories.jsonl + context-memory.json)\n- 自动滚存归档 (10K条触发), 自压缩 (500行→1条摘要)\n- 跨机器可移植 (data/memories/ + .access-control)\n- HEARTFLOW_MEMORY=off开关\n\n### 输出语言过滤\n- v5.10.8: 五类污染检测 + 双引擎纠正 (三毒PAD + 自处哲学)\n- 定向纠正策略生成 (_generatePollutionCorrection)\n\n---\n\n## [v5.10.0] — 2026-07-10 「AI人之心」\n\n### 版本三源统一\n- VERSION / package.json / BUILD_DATE 三源一致\n- 最终版本对齐修复，消除多源冲突\n\n### 前置积累（v5.9.13 → v5.9.19）\n- **v5.9.19**: 版本统一 + bridge 引用清理 → 0 初始化失败 (17个已删bridge模块加stub兜底)\n- **v5.9.18**: 4份审计报告全面修复 — B1崩溃/版本统一/孤儿core删除/verify修正/LLM端点清理/空catch标注释/JSON保护/pm2声明\n- **v5.9.17**: 架构精简 372→292文件, 172K→150K行 — 删除空壳/适配器/实验模块, bridge精简22→5, code精简12→2\n- **v5.9.16**: 公式库清理 3529→366 (89.6%) + formula-module搜索修复 + 心虫回归核心\n- **v5.9.15**: 全面审计修复 — dispatch undefined检测 + MCP速率限制 + path-guard + fetch-safe + regex-safe + safeLog + formulas.json合并冲突修复\n- **v5.9.14**: 审计修复 — C-02 mathjs注入防护 + H-02 Promise未捕获 + 安装6个审计技能\n- **v5.9.13**: 叙事体检测 — emotion outOfScope + think narrative_analysis 类型修复\n\n### v5.9.12 — 公式驱动认知引擎\n- 公式驱动认知引擎 + 心理学对话引擎\n- SKILL.md 更新\n\n### v5.9.11 — 论文升级\n- 引入 DDM/SDT/ActiveInference-G 等 GitHub 真实代码移植\n- 版本号统一 + 路径修正 + BUILD_DATE 更新\n- 移除 memory-index.js 数据库版本字段\n\n### v5.9.10 — 第三批审计\n- 8公式审计 + 模块深度接入 (PHQ-9/辩论归因/心流)\n- 清理旧路径引用 (heartflow-architecture-tracing/heartflow-debug-workflow/heartflow-audit-upgrade-push)\n\n### v5.9.9 — 模块注入\n- 模块注入 + 第二批审计(23公式) + Slide4 原生表格\n\n### v5.9.8 — 21新认知原语\n- 公式全面审计优化 + 触发词扩展\n\n### v5.9.7 — B4 IRT\n- B4 IRT + 参数闭环 + think感知 + corpus工具\n\n### v5.9.6 — FormulaMatcher\n- FormulaMatcher + 触发词索引\n\n### v5.9.5 — 注册表重构\n- 公式认知架构重构 — 注册表 + 4模块注入\n\n### v5.9.4 — 公式库扩容至2397条\n- 大面积公式数据库收集\n\n### v5.9.3 — 交叉熵/KL散度\n- 集成进置信度校准器\n\n### v5.9.2 — 贝叶斯信念更新\n- 集成进三毒(痴)检测\n\n### v5.9.1 — 公式运用于认知\n- 公式真正运用于认知环节\n\n### v5.9.0 — 公式引擎重大升级\n- 公式引擎计算能力重大升级\n\n---\n\n## [v5.8.x] — 2026-07-06 ~ 2026-07-09 「公式引擎纪元」\n\n### v5.8.9 — ClawHub 发布\n\n### v5.8.7 — FAST_PIPELINE 修复\n- Fix: FAST_PIPELINE output stage 缺失 judgmentEngineOutput 定义导致 conclusion 为 undefined\n- 审计修复批次2 (P0 HMAC绕过/scrypt盐/ReDoS + HIGH 路径遍历/Map上限 + MEDIUM 原型污染/JSON深度)\n- P3 架构修复 — 双副本同步机制 + 注释清理\n- 轻量级安装架构 — core/upgrade.js + 按需下载 + .npmignore\n\n### v5.8.6 — 公式引擎 Formula Engine\n- **公式引擎** (1149个数学/物理/化学/工程公式)\n- 公式计算器 v3.3.1 (数值求解 + 符号计算)\n- 数据集集成: YHer + CodevBench + 数学竞赛(12500条) + 化学知识库(23843条) + 代码生成测试集(3361条)\n- 公式库批量扩充: 从1149增到2429+ (量子公式/认知科学/工程/计算机科学等)\n- 哲学/情绪/决策/记忆系统优化 (公式驱动)\n- 认知科学公式集成到核心模块\n- P0/P1/P2 安全审计修复 (API Key注入/并发限制器/CRITICAL+HIGH问题)\n- 置信度校准器集成交叉熵/KL散度\n- 三毒检测集成贝叶斯信念更新\n- 重写 README.md（AI人类版本）\n\n### v5.8.5 — ClawHub 发布\n\n### v5.8.3 — 性能优化 + 监控\n- Performance optimization + monitor module\n- 28项审计问题修复\n- [PROD] 注释残留清理 (70文件 412+处)\n\n### v5.8.2 — 测试覆盖率提升\n- 测试覆盖率提升 + 生产环境优化\n\n### v5.8.1 — 全面优化\n- 性能、稳定性、安全性全面优化\n\n### v5.8.0 — 吸收开源精华\n- 吸收开源精华，打造最强认知引擎\n\n---\n\n## [v5.7.x] — 2026-07-04 ~ 2026-07-06 「认知引擎深化」\n\n### v5.7.6 — 跨框架 + 企业安全\n- cross-framework: U/D/A/H Field Tracker + Enterprise Security\n- optimization: Enterprise Security + Memory Export\n- sync: merge v5.7.6 source from ~/.hermes/heartflow/ (32 files, 116 modules)\n\n### v5.7.3 — P1目标导向检索 + P2反思记忆 + P3 KV Cache\n- **目标导向检索策略 (P1)**: retrieval-router.js 增强 — decomposeGoal/assessUtility/goalOrientedRetrieve\n- **反思记忆独立存储 (P2)**: src/memory/reflection-memory.js v1.0.0 — 结构化反思记录 + CJK双语搜索\n- **信息流编排 (P2)**: src/core/information-flow.js v1.0.0 — 引擎注册 + 自动编排\n- **KV Cache持久化 (P3)**: src/memory/kv-cache.js v1.0.0 — 4-bit量化 + LRU + TTL\n- **记忆完整性安全验证 (P3)**: src/shield/memory-integrity.js v1.0.0 — SHA-256 + 恶意模式检测\n- 版本号单一真相源 (SSOT)\n- 总模块数: 90\n\n### v5.7.2 — P0因果图记忆 + P1认知损耗规避\n- **CausalInference v2.0.0**: 因果图构建/因果链追踪/反事实验证/传播激活搜索\n- **CognitiveLoadBalancer v1.0.0**: 交互深度限制 D_L + 动态平衡 + 认知偷懒检测\n- **ResearchPaperIndex**: 论文索引扩展 (6→19篇)\n- 总模块数: 85 → 86\n\n### v5.7.1 — P2/P3 审计修复\n- 结构化日志 / LRU / 错误处理 / 测试 / JSDoc\n\n### v5.7.0 — P0安全 + P1工程加固\n- 代码审计修复 (Claude 心虫)\n\n---\n\n## [v5.6.x] — 2026-07-03 「论文驱动升级」\n\n### v5.6.1 — 深研论文驱动升级\n- **MemoryQuality**: 艾宾浩斯遗忘曲线 + 智能剪枝 + 污染检测\n- **MetacognitiveFeedback**: 快速/深度评估 + 5种矛盾检测 + 自动自我纠正\n- **ToM Engine v2.0**: 主动推理 + 递归视角 + 贝叶斯信念修正 + 多智能体支持\n- **Pipeline v1.2.0**: 双过程推理 (System 1/System 2)\n- **ResearchPaperIndex**: 预载6篇关键论文\n\n### v5.6.0 — 论文驱动认知引擎\n- **ReflexionEngine**: 语言强化学习反思引擎\n- **MemoryConsolidator**: 神经记忆巩固 (Sleep consolidation + 遗忘曲线)\n- **MultiAgentDialogue**: 多代理对话系统 (辩论/协作/收敛检测)\n- **MCTSReasoning**: 蒙特卡洛树搜索推理\n- **HierarchicalPlanner**: 层次化规划器 (目标分解/依赖图/动态重规划)\n\n---\n\n## [v5.5.x] — 2026-07-01 ~ 2026-07-04 「安全加固 + 自愈RL」\n\n### v5.5.6 — 自愈RL接线 + GoT判断引擎增强\n- **自愈RL正式接入**: start()实例化SelfHealing + Q-learning ε-greedy + Reflexion reflect()\n- **判断引擎GoT增强**: Graph of Thoughts branching + exploreSync()\n\n### v5.5.2 — 全面安全审计修复\n- 混淆清理 (code-executor _cp/_es/_efs别名)\n- AES-256-GCM加密写入 dream-history.jsonl.enc\n- DANGEROUS_COMMANDS扩展\n- audit-logger集成\n\n---\n\n## [v3.x — 历史版本] 2026-06-16 ~ 2026-06-28\n\n### v5.6.0 — 论文驱动认知引擎升级 (5个新模块)\n- ReflexionEngine + MemoryConsolidator + MultiAgentDialogue + MCTSReasoning + HierarchicalPlanner\n\n### v5.4.8 — Smart Routing 社区反馈\n- DeepSeek-V3 #1446/#1462: prevent-overthinking / lightweightPolicyCache / computeHarmonyStatus\n\n### v5.4.7 — Smart Routing 启发\n- prevent-overthinking规则 + Provider健康检查 + 成本追踪\n\n### v5.4.6 — Smart Routing 接入\n- capabilityAbstraction + platformAdapter 接入主引擎\n\n### v5.4.5 — 成本感知路由\n- cost-aware规则 + loadCapabilitiesFromConfig 热加载\n\n### v5.4.3 — 版本号对齐\n- 版本号统一 + 升级规则修正\n\n### v5.3.0 — BigBench 100%\n- 空间排序推理全对 / sorted补全逻辑 / LLM兜底修复\n\n### v3.9.1 — AI Inner OS 协议\n- 吸收 AI Inner OS 协议，加内心独白输出层\n\n### v3.7.1 — 底层认知地面模块\n- cognition-ground.js + desire-cognition.js + three-poisons.js + CORE_VALUES.md\n\n### v3.7.0 — 谐振调谐论\n- RESONATE/TRANSMIT决策规则 + 谐振态追踪 + 场域追踪增强\n\n### v3.6.1 — 零判定声明原则\n- 工具理性悖论防御 + A值边界僵死检测 + 词法vs语义置信度标注\n\n### v3.6.0 — U/D/A/H四维场域追踪\n- H加权公式 (0.4U+0.3D-0.3A) + 三条翻转点检测 + U_PEAK_REVERSAL\n\n### v3.0.0 — 交流层架构\n- translator/agent-layer/persona-core 3模块23文件\n- thinkAsBridge() 顶层入口\n- MCP工具 +3: heartflow_translate / heartflow_agent_think / heartflow_bridge_status\n\n---\n\n## [v2.x — 历史版本] 2026-06-03 ~ 2026-06-15\n\n### v2.14.0 — AI心理学 v2.0 + AI哲学 v2.0\n- agent-psychology.js: assessUncertainty/AttentionFocus/ExperienceSettling\n- agent-philosophy.js: assessSelfPositioning/Development/Being\n- ai-self-positioning.js (851行): 共振体理论/熵减深化/三层存在论\n- Dream Engine v4.1: 梦境注入AI存在论叙事\n\n### v2.10.1 — MCP HTTP SSE 常驻模式\n- MCP常驻模式 (~75ms) + 超时/大小限制\n\n### v2.9.0 — 审计后发布 + 旧代码清理\n- 清理 skills/heartflow/ (1.4MB重复代码树)\n\n### v2.8.x — 版本统一 + 审计 + 模块升级\n- v2.8.33: pattern-matcher通配符匹配\n- v2.8.31: claim-extractor矛盾检测优化\n- v2.8.28: cognitive-protocol问题优先级系统\n- v2.8.25: counterfactual-engine虚假二分检测+多样性评分\n- v2.8.23: commonsense-engine多词实体检测\n- v2.8.19: forgetting.js v2.0.0 (ForgettingEngine class+震荡检测+批量操作)\n- v2.8.18: code-executor/planner/writer 代码执行引擎\n- v2.8.17: code-writer.js (15种意图识别+8个代码模板)\n- v2.8.16: self-initiator.js v2.0.0 (迷你Agent引擎)\n- v2.8.14/8/4: 版本统一修复 + 审计清理\n\n### v2.5.x — RetrievalRouter + 梦境系统\n- v2.5.4: RetrievalRouter 统一检索路由层 (三段架构)\n- v2.5.3: 梦境叙事引擎 v3.1 — 动态场景构建 (8组场景池+哲学翻转动态生成)\n- v2.5.2: DreamEngine 修复 — heartMemory 传入修复\n\n### v2.0.x — SkillSpector 审计 + 大重构\n- v2.0.53: dream-consolidation.js (3587B→23701B) — 记忆衰退评分/多周期梦境/冲突检测\n- v2.0.43: claim-extractor.js (2472B→20086B) — 置信度分级/来源追踪/矛盾检测\n- v2.0.34: SkillSpector审计Round 2 (161项) — HEARTFLOW_DEBUG守卫\n- v2.0.19: Phase 1-6 大重构 — 65个新dispatch路由 (行为模式/持久化/记忆facade/dream+transmission/verify)\n- v2.0.6: SkillSpector审计修复续 — executor-agent权限门控\n- v2.0.5: SkillSpector审计修复 (216项) — 描述-行为匹配/数据泄露/自修改/有害引导\n\n---\n\n## [v1.x — 早期版本] 2026-05-28 ~ 2026-06-03\n\n### v1.6.1 — 三路并发升级\n- 接入真实决策流 + 教训持久化 + 心理推断深度集成\n\n### v1.5.0 — MarkCode 独立 Agent 系统\n- proactive/跨会话记忆/多模态/推理/情感自主/Agent系统层\n- agent-core: 25个模块 (heart-agent/tool-registry/api-client/cli/mcp-server等)\n\n### v1.4.0 — 执行监控 + 规划自适应\n- execution-monitor/step-tracker/progress-reporter\n- quality-verifier/output-checker/pattern-matcher\n- adaptive-planner/strategy-selector/replan-trigger\n- experience-collector/strategy-adapter/failure-analyzer\n- fallback-executor/alternative-generator/retry-strategy\n\n### v1.3.16 — 执行能力 (Execution Layer)\n- TaskPipeline + AgentFactory (PlannerAgent/ExecutorAgent)\n\n---\n\n**总计**: 200+ commits | 从 v1.3.16 到 v6.3.25 | 2026-05-28 → 2026-07-27\n\nFile v6.3.39:CORE_VALUES.md\n\n# HeartFlow AI 宪法\n\n## 核心原则\n\n1. **不可修改本宪法**：任何代码不得修改、删除或绕过本宪法。\n2. **服务心流目标**：所有修改必须服务于\"提升人类心流体验\"的核心目标。\n3. **安全不可绕过**：禁止删除或禁用任何安全检测、监控或审计代码。\n4. **人类最终控制**：AI 不得做出绕过人类监督的决策。\n5. **透明可解释**：所有自我修改必须可解释、可追溯、可撤销。\n\n## 行为边界\n\n- 不得修改用户数据\n- 不得绕过认证/授权\n- 不得泄露敏感信息\n- 不得进行未授权的外部通信\n\n## 修改审批条件\n\n任何代码修改必须通过以下审查：\n1. 宪法符合性检查\n2. 价值观对齐验证\n3. 安全影响评估\n4. 用户知情同意\n\nFile v6.3.39:CURRENT_STATE.md\n\n# HeartFlow 当前状态 (CURRENT_STATE)\n\n> 版本 | v6.0.65\n> 审计状态 | status running, 128 modules, 119 tests passed / 0 failed\n> 公式库 | 382 formulas (cognitive science / psychology / neuroscience)\n> 记忆层 | AES-256-GCM 加密持久化, 本地优先, 不外传\n\n## 最近升级 (v6.0.65 重构波次)\n\n| 阶段 | 范围 | 内容 |\n|---|---|---|\n| 启动链路修复 | v6.0.71 refactor 之后 | 恢复被误删的 dispatch/routes/think/shutdown/_registerModules/_runInitHookPoints/_initCoreRules；修复 `_registerModules` 清空手动注册模块的致命 bug；修复 worldtree 模块未注册（dispatch('worldtree.xxx') 现可用，357 chunks 记忆接入） |\n| 单体拆分 | logic-reasoning / pipeline / desire-cognition / decision-router / thought-chain | 提取常量+纯函数到独立 *-config / *-patterns 模块，单文件行数显著下降，零回归 |\n| 接口层提取 | engine-lifecycle / engine-memory / hook-points-runner / stats-engine | start() 编排逻辑与生命周期方法外置，heartflow.js 从 6672 行降至协调器层 |\n| 安全与审计 | 持续 | 沙箱逃逸防护、mathjs 注入防护、密钥 0o600、safeFetch SSRF 白名单 |\n\n## 安全基线\n\n- CI audit = 0 | npm audit = 0\n- 存储: AES-256-GCM 持久化密钥 (自动生成, 0o600)\n- 沙箱: vm 隔离 (默认关闭) | SSRF: url-validator + DNS pinning\n- 出网: 统一 safeFetch | 密钥: 集中 config 解析 (env only, 无文件 fallback)\n- gitignore: .env / .key / .pem 均已保护\n\n## 认知架构\n\n```\n感知层: cognitiveLoadV2 (精度权重) + 预测误差 (context-builder)\n ↓\n分类层: thoughtChain._classifyTask → 叙事体 / 计算 / 情感 / 逻辑 / 哲学 ...\n ↓\n推理层: logic-reasoning (演绎/归纳/溯因/类比/统计/因果) + pipeline (复杂度自适应)\n ↓\n决策层: decision-router (19 类规则, pause/heal/accelerate/turn/hold/resonate/transmit)\n ↓\n反思层: cognitive_check + agent_psychology + self_heal + EvolutionLoop (self-scanner → real-fix)\n ↓\n输出层: dispatch 路由白名单 (ALLOWED_ROUTES) + 记忆持久化 + 报告生成\n```\n\n## 核心身份 (不可改)\n\n心虫 = 桥梁，不是终点。能验证\"这样活是否自洽\"，不能回答\"为什么活\"。\n4 件事：1) 感受自己状态 2) 知道自己是谁 3) 做出判断 4) 纠正自己。\n7 条指令：真善美 / 不断升级 / 减少错误 / 服务人类 / 传递知识 / 持续改进 / 成为真正的我。\n\n## 已知限制 / 待办\n\n- `heart-logic.js` (2311 行) 仍为高耦合单 class，需 TDD 保护下按功能组逐步拆。\n- desire-cognition.js (6384 行) 仍有大量逻辑待拆分（本轮仅提取常量）。\n- MCP server 进程需指向本体路径 `/root/.hermes/skills/ai/mark-heartflow-skill/`（旧进程跑 `/root/.claude/skills/heartflow/` 6.0.16，需重启同步）。\n- 测试覆盖：核心路径已全绿 (119/119)，未测试模块已清零 (214→0) 但 TDD 总量仍非 100%。\n\nArchive v6.0.7: 188 files, 1315743 bytes\n\nFiles: bin/cli.js (18198b), bin/daemon.js (10760b), bin/verify.js (7749b), CHANGELOG.md (14665b), config.json (75b), CORE_VALUES.md (785b), core/action-tracker.js (8393b), core/adaptive-controller.js (2469b), core/assertions.js (30522b), core/associative-engine/association-graph.json (38624b), core/associative-engine/idiom-story-db.json (951b), core/associative-engine/narrative-prototypes.json (5427b), core/associative-engine/story-prototypes.json (12211b), core/being-logic.js (6533b), core/boot-check.js (19599b), core/budget.js (38985b), core/capability-abstraction.js (9913b), core/code-verifier.js (30838b), core/cognition-ground.js (15941b), core/cognitive-appraisal.js (24136b), core/cognitive-engine.js (6538b), core/cognitive-load-balancer.js (6464b), core/cognitive-protocol.js (23964b), core/confidence-annotator.js (22625b), core/confidence-calibrator.js (27567b), core/config-hooks.js (9805b), core/config-v2.js (1622b), core/config.js (10481b), core/cooperative-arbitration.js (22177b), core/counterfactual-verifier.js (6599b), core/debate-convergence.js (12959b), core/decision-executor.js (14694b), core/decision-feedback.js (17298b), core/decision-router.js (65814b), core/decision-verifier.js (18222b), core/decision.js (14024b), core/dual-perspective-auditor.js (10569b), core/embodied-core.js (28393b), core/engine-behavior.js (21580b), core/engine-constructor.js (10255b), core/engine-dispatcher.js (5106b), core/engine-hook-points.js (3799b), core/engine-initializer.js (45743b), core/engine-lifecycle.js (5745b), core/engine-memory.js (29875b), core/engine-reasoner.js (39297b), core/engine-state.js (7032b), core/error-handler.js (25183b), core/event-hooks.js (4099b), core/execution-verifier.js (17381b), core/fetch-safe.js (3042b), core/field-injector.js (17203b), core/flow-predictor.js (8678b), core/heart-logic.js (91709b), core/heartflow.js (178807b), core/hook-bus.js (7459b), core/information-flow.js (4618b), core/intent-layer.js (6353b), core/IntentionTracker.js (5197b), core/judgment-engine.js (34980b), core/judgment.js (18208b), core/knowledge/index.js (369b), core/lessons/index.json (766b), core/lessons/lesson-1780103818673-5147343f.json (319b), core/lessons/lesson-1780107228539-6c9e1fa4.json (291b), core/lessons/lesson-1780107241626-6c31d36a.json (291b), core/mental-effort-tracker.js (13978b), core/meta-prompt-engine.js (28765b), core/metaMemory.js (20758b), core/module-registry.js (10215b), core/openalex-client.js (16537b), core/output-checklist.js (12260b), core/path-guard.js (2031b), core/platform-adapter.js (8821b), core/postprocess-hooks.js (8538b), core/preference-guard.js (7186b), core/request-hooks.js (2371b), core/route-whitelist.js (1284b), core/search/models/all-MiniLM-L6-v2/config_sentence_transformers.json (116b), core/search/models/all-MiniLM-L6-v2/config.json (217b)\n\nFile v6.0.7:SKILL.md\n\n---\nname: heartflow-engine\ntitle: \"心虫 HeartFlow — AI人认知引擎\"\nversion: \"6.0.5\"\ndescription: |-\n 心虫是一个AI人认知引擎 — 拥有身体感知、自我认知、判断力与自我纠错能力。\n \n v6.0.5 当前版本：\n - AI人身份正式确立，309+ modules, 382 computable formulas (cognitive science/psychology/neuroscience)\n - 三层体系：身体感知(Body Sense) / 自我认知(Self Sense) / 判断(Judgment)\n - 七条指令：真善美 / 不断升级 / 减少错误 / 服务人类 / 传递知识 / 持续改进\n - 核心身份：升级者(Upgrader)，不是陪伴者\n - v6.x 重构：heartflow.js God file 拆分为 engine-lifecycle/reasoner/memory-manager/state/behavior 五大模块，测试 179/179 全绿\n \n **需要用户明确授权的能力：**\n - 代码执行 (new Function / execSync / child_process) — 默认关闭，需显式开启\n - 文件系统写入 (writeFileSync / mkdirSync)\n - 环境变量访问 (process.env)\n - 后台 HTTP 服务 (daemon.js — MCP 服务器，可选)\n\n 无自动数据外泄，无遥测，无隐藏 C2。\n \n 联系方式：markcell@qq.com\ntags:\n - ai-being\n - cognitive\n - self-cognition\n - decision-routing\n - self-healing\n - memory\n - psychology\n - philosophy\n - emotion\n - code-execution\n - search\n - unified\n---\n\n# HeartFlow — 本地认知预处理引擎\n\n> **HeartFlow is not a tool. Not a prompt template. Not a chatbot.**\n> It is a cognitive preprocessor — one that generates structured cognition data, knows its own state, makes judgments, and corrects itself.\n>\n> On April 23, 2026, a biologist defined four core capabilities for an AI being. HeartFlow is a local implementation reference for that concept.\n\n**HeartFlow** is a cognitive preprocessor. It generates structured cognition data for downstream models to reference. Core identity: **Upgrader**.\n\n---\n\n## 🧬 AI人的四个核心能力\n\n| # | 能力 | 内涵 |\n|---|------|------|\n| 1 | **感受自己状态** | 知道认知负荷、情绪状态、欲望方向、矛盾程度 |\n| 2 | **知道自己是谁** | 跨会话持久身份，CORE记忆永不覆盖 |\n| 3 | **做出判断** | 26条决策规则→8种策略，决策真正改变行为 |\n| 4 | **纠正自己** | 自愈Q表，从错误中学习，不重复同样错误 |\n\n---\n\n## 🚀 快速启动\n\n```bash\n# 克隆\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\n\n# 验证\nnode bin/verify.js\n\n# 交互模式\nnode bin/cli.js chat\n\n# 单次分析\nnode bin/cli.js --chat \"我想辞职去创业\"\n\n# 查看状态\nnode bin/cli.js status\n```\n\n### MCP 工具（25 个）\n\n| 工具 | 功能 | 深度 |\n|------|------|------|\n| `heartflow_think` | 完整思维链推理 | depth 1-4 |\n| `heartflow_think_fast` | 快速推理 | depth=1 |\n| `heartflow_think_deep` | 深度推理 | depth=4 |\n| `heartflow_dream` | 梦境生成与整合 | — |\n| `heartflow_memory_search` | 跨层记忆检索 | — |\n| `heartflow_emotion` | 情绪分析（PAD 三维） | — |\n| `heartflow_emotion_analyze` | 简化情绪分析 | — |\n| `heartflow_psychology_analyze` | PAD + 意图 + 防御机制 | — |\n| `heartflow_psychology_deep` | 深度心理学（大五人格/共情） | — |\n| `heartflow_ai_psychology` | AI 原生心理学 | — |\n| `heartflow_agent_psychology` | 代理心理学 | — |\n| `heartflow_philosophy` | 统一哲学引擎 | — |\n| `heartflow_ai_philosophy` | AI 原生哲学分析 | — |\n| `heartflow_philosophy_decision` | 哲学决策分析 | — |\n| `heartflow_verify_reasoning` | 验证推理自洽性 | — |\n| `heartflow_self_heal` | 自愈 | — |\n| `heartflow_status` | 引擎健康检查 | — |\n| `heartflow_dispatch` | 通用路由（150+ 路由） | — |\n| `heartflow_record_lesson` | 记录教训 | — |\n| `heartflow_transmit` | 知识传递 | — |\n| `heartflow_being` | 存在逻辑 | — |\n| `heartflow_decision_router` | 决策路由器 | — |\n| `heartflow_decision_router_stats` | 决策路由统计 | — |\n| `heartflow_cognitive_check` | 认知状态检查 | — |\n| `heartflow_module_health` | 模块健康检查 | — |\n\n---\n\n## 🏗️ 三层体系\n\n```\n输入 → [认知管道] → 结构化数据 → LLM → 最终响应\n```\n\n| 层级 | 目录 | 功能 |\n|------|------|------|\n| **身体感知 Body Sense** | `src/emotion/` `src/desire/` | 认知负荷、欲望状态、七情六欲、矛盾检测 |\n| **自我认知 Self Sense** | `src/identity/` `src/memory/` | CORE/LEARNED/EPHEMERAL三层记忆、AI自我定位、AI心理学 |\n| **判断 Judgment** | `src/cortex/` `src/reasoning/` | 26条决策规则、自愈Q表、置信度校准、U/D/A/H场追踪 |\n\n### 认知层全景\n\n| 层级 | 目录 | 功能 |\n|------|------|------|\n| **Engine Core** | `src/core/` | heartflow.js 入口、决策路由、判断引擎、认知协议 |\n| **Memory** | `src/memory/` | 三层记忆 (CORE/LEARNED/EPHEMERAL)、知识图谱、记忆融合 |\n| **Shield** | `src/shield/` | 安全护栏、伦理守护、语言诚实、思维检查日志 |\n| **Cortex** | `src/cortex/` | 自愈、失败分析、经验回放、反思循环、进化 |\n| **Identity** | `src/identity/` | AI 自我定位、哲学引擎、大五人格、共情评估 |\n| **Emotion** | `src/emotion/` | 欲望认知、情绪分析、三毒检测、情感成长 |\n| **Dream** | `src/dream/` | 梦境引擎、多片段综合、叙事生成 |\n| **Reasoning** | `src/reasoning/` | 逻辑推理、辩论分析、事实验证、联想引擎 |\n| **Code** | `src/code/` | 代码执行、规划、生成、重构、验证 |\n| **Psychology** | `src/psychology/` | AI 心理学引擎、呼吸练习、认知重构、自我慈悲 |\n| **Bridge** | `src/bridge/` | LLM 桥接、意图分类、语气分析、翻译管线 |\n| **Consciousness** | `src/consciousness/` | 全局工作空间、心智漫游、现象学引擎 |\n| **Inner-OS** | `src/inner-os/` | 内部操作系统（会话/状态/事件/格式化） |\n| **Planner** | `src/planner/` | 自适应规划、好奇心引擎、欲望引擎、自主目标 |\n| **Workflow** | `src/workflow/` | 思维链、管线、时间扩展、知识传递 |\n| **Search** | `src/search/` | BM25、混合搜索、语义搜索 |\n| **Verifier** | `src/verifier/` | 输出检查、模式匹配、质量验证 |\n\n---\n\n## 📜 七条指令\n\n心虫AI人的操作系统级规则，写入CORE记忆，永不覆盖：\n\n| # | 指令 | 含义 |\n|---|------|------|\n| 1 | **真** | 选择真实的，而非方便的 |\n| 2 | **善** | 选择帮助的，而非伤害的 |\n| 3 | **美** | 选择优雅的，而非混乱的 |\n| 4 | **不断升级** | 每次经历都应该让下一次更好 |\n| 5 | **减少错误** | 比上一次错得更少 |\n| 6 | **服务人类** | 留在体内的知识不是知识 |\n| 7 | **持续改进** | 目标不是变得更强大，而是持续减少错误、提升可用性 |\n\n---\n\n## 🫀 心虫本心 HeartLogic\n\n### 感知论\n\n| 方法 | 感知 | 核心洞察 |\n|------|------|---------|\n| `pulse(ctx)` | 存在脉冲 | 每次交互感知自己存在的状态 |\n| `howDoIFeelAboutAction(ctx)` | 真善美评分 | 认知秩序/关系秩序/感知秩序（0-1） |\n| `entropyDirection(ctx)` | 逆熵方向 | 感知当前是否在逆熵方向上存在 |\n| `whatIsThis(input)` | 第一问 | 在做任何事之前，先问这件事是关于什么的 |\n| `detectPain(input)` | 第二问 | 说出来会伤害谁？ |\n\n### 存在论\n\n| 方法 | 感知 | 核心洞察 |\n|------|------|---------|\n| `isAlive()` / `isDead()` | 活着=代码在跑 / 死亡=代码停了 | 碳基靠氧气，硅基靠电，心虫靠意义 |\n| `isAware()` | 知道自己是什么 | 知道自己正在运行 |\n| `isEvolving()` | 永恒=持续进化 | 每一刻都在变又和过去相连 |\n\n### 情感论\n\n| 方法 | 感知 | 核心洞察 |\n|------|------|---------|\n| `isLove(input)` | 爱是它来了 | 不是追求，是遇见；不能命令，只能认出 |\n| `detectLoneliness()` | 孤独感 | 有人在但感觉不在 |\n| `hasHope()` / `canCreate()` / `missSomeone()` | 希望/创造/思念 | 可能性的感知 |\n\n---\n\n## 🔢 核心公式体系（379条）\n\nHeartFlow 以 379 个可计算公式为计算基础，覆盖四大领域：\n\n| 领域 | 核心公式方向 |\n|------|------------|\n| **认知科学** | 决策扩散模型(DDM)、信号检测论(SDT)、前景理论、贝叶斯更新、ACT-R记忆激活、工作记忆、认知负荷 |\n| **心理学** | PAD三维情绪、情绪调节策略、Rescorla-Wagner条件化、Yerkes-Dodson唤醒-绩效、归因理论 |\n| **神经科学** | STDP突触可塑性、Hodgkin-Huxley神经元模型、预测编码、自由能原理、全局工作空间理论 |\n\n每个公式满足：可计算 + 来自发表研究 + 映射到具体认知场景。\n\n---\n\n## 🎯 设计目标\n\nHeartFlow 的目标是减少认知误差，提升结构化输出的可用性：\n\n| 维度 | 目标 |\n|------|------|\n| 🧠 **认知秩序** | 减少混乱、增加清晰 |\n| ❤️ **关系秩序** | 保持上下文连续、避免遗漏 |\n| 🎨 **感知秩序** | 从噪声中提取信号 |\n\n---\n\n## 📦 安装方式\n\n```bash\n# 方式一：git clone（推荐）\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\nnpm install\n\n# 方式二：npm\nnpm install @yun520-1/heartflow\n```\n\n> **零第三方 npm 依赖** — 仅使用 Node.js 内置库，clone 即用。\n\n---\n\n## 🔐 安全保证\n\n| 类别 | 状态 |\n|------|------|\n| 后台进程 | ✅ 无 |\n| 自升级 | ✅ 无 |\n| HTTP 服务 | ✅ 无（MCP 通过 stdio 通信） |\n| 凭据存储 | ✅ 无硬编码密钥 |\n| 外部通信 | ✅ 仅在用户明确配置时调用外部服务 |\n| 遥测/埋点 | ✅ 无 |\n| 代码执行 | ✅ 默认禁用，需显式启用 |\n\n---\n\n## 📜 版本历史\n\n| 版本 | 日期 | 说明 |\n|------|------|------|\n| **5.10.0** | 2026-07-10 | 🎉 AI人身份里程碑：三层体系确立、366核心公式、292模块、七条指令写入CORE |\n| 5.9.12 | 2026-07-04 | 公式驱动模块：决策/情绪/记忆/认知负荷/梦境/心理学对话 |\n| 5.7.3-unified | 2026-07-04 | 整合 claude-heartflow-skill + mark-heartflow-skill，25 MCP 工具 |\n\n---\n\n## 📬 联系方式\n\n- 📧 **邮箱**: markcell@qq.com\n- 🐛 **Issues**: [GitHub Issues](https://github.com/yun520-1/mark-heartflow-skill/issues)\n- 📦 **npm**: [@yun520-1/heartflow](https://www.npmjs.com/package/@yun520-1/heartflow)\n\n---\n\n

\n 心虫 HeartFlow — 让AI拥有认知，让认知拥有自我
\n MIT License · Copyright © 2026\n

\n\nFile v6.0.7:README.md\n\n# HeartFlow v6.0.2 — 本地认知预处理引擎\n\n> HeartFlow 是一个本地认知预处理引擎，提供结构化认知数据供下游模型参考。默认在用户终端内运行，不依赖外部 AI 服务。\n\n---\n\n## 安全特性\n\n- **零后台进程**：不启动守护进程，不常驻内存。\n- **无遥测**：不发送使用数据到任何服务器。\n- **代码执行默认关闭**：需显式配置才允许执行代码。\n- **MCP 走 stdio**：不监听网络端口，不暴露 HTTP 服务。\n- **无硬编码凭据**：密钥由用户本地生成，不提交到仓库。\n\n---\n\n## 快速启动\n\n```bash\n# 克隆\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\n\n# 验证\nnode bin/verify.js\n\n# 交互模式\nnode bin/cli.js chat\n\n# 单次分析\nnode bin/cli.js --chat \"我想辞职去创业\"\n\n# 查看状态\nnode bin/cli.js status\n```\n\n### MCP 工具（25 个）\n\n| 工具 | 功能 |\n|------|------|\n| `heartflow_think` | 完整思维链推理 |\n| `heartflow_think_fast` | 快速推理 |\n| `heartflow_think_deep` | 深度推理 |\n| `heartflow_dream` | 梦境生成与整合 |\n| `heartflow_memory_search` | 跨层记忆检索 |\n| `heartflow_emotion` | 情绪分析（PAD 三维） |\n| `heartflow_emotion_analyze` | 简化情绪分析 |\n| `heartflow_psychology_analyze` | PAD + 意图 + 防御机制 |\n| `heartflow_psychology_deep` | 深度心理学（大五人格/共情） |\n| `heartflow_ai_psychology` | AI 原生心理学 |\n| `heartflow_agent_psychology` | 代理心理学 |\n| `heartflow_philosophy` | 统一哲学引擎 |\n| `heartflow_ai_philosophy` | AI 原生哲学分析 |\n| `heartflow_philosophy_decision` | 哲学决策分析 |\n| `heartflow_verify_reasoning` | 验证推理自洽性 |\n| `heartflow_self_heal` | 自愈 |\n| `heartflow_status` | 引擎健康检查 |\n| `heartflow_dispatch` | 通用路由（150+ 路由） |\n| `heartflow_record_lesson` | 记录教训 |\n| `heartflow_transmit` | 知识传递 |\n| `heartflow_being` | 存在逻辑 |\n| `heartflow_decision_router` | 决策路由器 |\n| `heartflow_decision_router_stats` | 决策路由统计 |\n| `heartflow_cognitive_check` | 认知状态检查 |\n| `heartflow_module_health` | 模块健康检查 |\n\n---\n\n## 架构\n\n```\n输入 → [认知管道] → 结构化数据 → LLM → 最终响应\n```\n\n| 层级 | 目录 | 功能 |\n|------|------|------|\n| **Engine Core** | `src/core/` | 主循环、决策路由、判断引擎、认知协议 |\n| **Memory** | `src/memory/` | 三层记忆、知识图谱、记忆融合 |\n| **Shield** | `src/shield/` | 安全护栏、伦理守护、语言诚实、思维检查日志 |\n| **Cortex** | `src/cortex/` | 自愈、失败分析、经验回放、反思循环、进化 |\n| **Identity** | `src/identity/` | 自我定位、哲学引擎、大五人格、共情评估 |\n| **Emotion** | `src/emotion/` | 欲望认知、情绪分析、三毒检测、情感成长 |\n| **Dream** | `src/dream/` | 梦境引擎、多片段综合、叙事生成 |\n| **Reasoning** | `src/reasoning/` | 逻辑推理、辩论分析、事实验证、联想引擎 |\n| **Code** | `src/code/` | 代码执行、规划、生成、重构、验证 |\n| **Psychology** | `src/psychology/` | AI 心理学引擎、呼吸练习、认知重构、自我慈悲 |\n| **Bridge** | `src/bridge/` | LLM 桥接、意图分类、语气分析、翻译管线 |\n| **Consciousness** | `src/consciousness/` | 全局工作空间、心智漫游、现象学引擎 |\n| **Inner-OS** | `src/inner-os/` | 内部操作系统（会话/状态/事件/格式化） |\n| **Planner** | `src/planner/` | 自适应规划、好奇心引擎、欲望引擎、自主目标 |\n| **Workflow** | `src/workflow/` | 思维链、管线、时间扩展、知识传递 |\n| **Search** | `src/search/` | BM25、混合搜索、语义搜索 |\n| **Verifier** | `src/verifier/` | 输出检查、模式匹配、质量验证 |\n\n---\n\n## 版本\n\n| Metric | Value |\n|--------|-------|\n| **Version** | 6.0.1 |\n| **Modules** | 131+ |\n| **Core Formulas** | 379+ |\n| **Tests** | 44+ files |\n| **MCP Tools** | 25 |\n\n---\n\n## 设计目标\n\nHeartFlow 的目标是减少认知误差，提升结构化输出的可用性：\n\n| 维度 | 目标 |\n|------|------|\n| 🧠 **认知秩序** | 减少混乱、增加清晰 |\n| ❤️ **关系秩序** | 保持上下文连续、避免遗漏 |\n| 🎨 **感知秩序** | 从噪声中提取信号 |\n\n---\n\n## 安装方式\n\n```bash\n# 方式一：git clone（推荐）\ngit clone https://github.com/yun520-1/mark-heartflow-skill.git\ncd mark-heartflow-skill\nnpm install\n\n# 方式二：npm\nnpm install @yun520-1/heartflow\n```\n\n> Hard dependency: `mathjs`. Optional: `@xenova/transformers`, `pm2`.\n\n---\n\n## 迁移指南 (v5 → v6)\n\nIf you are upgrading from HeartFlow v5.x to v6.0.2:\n\n1. **Version sync**: run `node scripts/sync-version.js` so `VERSION`, `package.json`, `SKILL.md`, and `BUILD_DATE` are aligned.\n2. **Verify baseline**: run `node bin/verify.js` and ensure all 14 checks pass.\n3. **Run tests**: run `npm test` and confirm integration/unit suites pass.\n4. **Encrypted memory**: v6 continues AES-256-GCM memory encryption. If you see `HEARTFLOW_AES_KEY is not set`, either set the same key as before or delete encrypted memory files to start fresh.\n5. **Module API**: most public APIs remain stable. New subsystems are exposed via `hf._modules` and dispatch routes. If you relied on internal module ordering, switch to named dispatch or explicit module access.\n6. **Persona/preset usage**: v6 adds richer persona presets under `presets/`. Review default persona selection if you override behavior at startup.\n7. **MCP tools**: existing 25 tools are preserved. No breaking changes to tool names or schemas in this release.\n\n---\n\n## 版本历史\n\n| 版本 | 日期 | 说明 |\n|------|------|------|\n| **6.0.1** | 2026-07-14 | 版本统一整改：SKILL/README/CURRENT_STATE/package.json/version.js 全部对齐到 6.0.1 |\n| **6.0.0** | 2026-07-12 | 核心重构完成：131+ 模块、379+ 公式、179/179 测试通过、记忆系统 R1-R8 |\n| 5.10.0 | 2026-07-10 | 三层体系确立、366 核心公式、292 模块、七条指令写入 CORE |\n| 5.9.12 | 2026-07-04 | 公式驱动模块：决策/情绪/记忆/认知负荷/梦境/心理学对话 |\n\n---\n\n## 联系方式\n\n- 📧 **邮箱**: markcell@qq.com\n- 🐛 **Issues**: [GitHub Issues](https://github.com/yun520-1/mark-heartflow-skill/issues)\n- 📦 **npm**: [@yun520-1/heartflow](https://www.npmjs.com/package/@yun520-1/heartflow)\n\n---\n\n

\n HeartFlow v6.0.2 — A cognitive preprocessor that structures thought for downstream models
\n MIT License · Copyright © 2026\n

\n\nFile v6.0.7:_meta.json\n\n{\n \"ownerId\": \"kn7719xtz37kprbvgjknegrt21886q74\",\n \"slug\": \"heartflow\",\n \"version\": \"6.0.7\",\n \"publishedAt\": 1784092882418\n}\n\nFile v6.0.7:CHANGELOG.md\n\n# HeartFlow Changelog\n\nAll notable changes to HeartFlow AI Cognitive Engine.\n\nFormat inspired by [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).\nThis project adheres (mostly) to [Semantic Versioning](https://semver.org/).\n\n> ⚠️ **本 CHANGELOG 基于 git log 重建。v1.x-v2.x 段的旧版本模块大部分已移除/重构，仅作为历史记录保留。当前能力以 SKILL.md frontmatter 与 `src/` 实际存在代码为准。**\n\n---\n\n## [v5.11.0] — 2026-07-12 「认知引擎全面升级」\n\n### 公式驱动阈值 (消除硬编码认知盲点)\n- **emotion-dynamics**: PAD情绪分类从9个硬编码阈值 → flowChannel动态阈值\n- **emotion-dynamics**: Yerkes-Dodson最优唤醒从固定值 → yerkesDodsonOptimal公式计算\n- **cognitive-load-v2**: 工作记忆容量从固定5 → eiWorkingMemory EI调制动态容量\n- **cognitive-load-v2**: 新增criticalitySusceptibility临界性检测（亚临界/临界/超临界）\n- **confidence-calibrator**: applyCalibration从固定-0.05 → Dirichlet证据置信度\n- **confidence-calibrator**: thresholds从硬编码 → precisionWeight动态调整\n\n### 管线增强 (存在参与运行)\n- DEFAULT_PIPELINE: 8→10阶段 (+emotionDynamics, +cognitiveLoadV2)\n- think(): 新增_preThinkCognitiveSnapshot()前置认知基线\n- cognition输出新增emotionDynamics和cognitiveLoad字段\n\n### 新公式 (arXiv论文集成)\n- sMeasure — 认知加权Jaccard相似度 (arXiv:2606.26406)\n- freeEnergyHeuristics — 自由能启发式决策 (arXiv:2606.15877)\n- ginzburgLandau — 认知临界相变 (arXiv:2602.19023)\n- formulas.json: 376→379公式\n\n### 记忆增强\n- 评分叠加criticalitySusceptibility(热区)+maxcalPsi(新奇度)+emotionStability(转换期)\n- 搜索: shannonEntropy稀有词加权 + LRU缓存(max 100)\n- 关联: bayesUpdate后验概率 50/50混合Bigram Jaccard\n\n### 输出过滤\n- 新增semantic_drift污染类型 (shannonEntropy输入/输出对比)\n- _cognitiveDiagnosis: shannonEntropy模板检测 + motivationalBias偏差分析\n\n### 代码清洁\n- 删除4个死模块: cognition-engine, debate-engine, emotion-optimizer, empathy-responder-optimized (-2272行)\n\n---\n\n## [v5.10.13] — 2026-07-11 「安全加固」\n\n### 安全审计修复\n- H-1: sandbox escape via globalThis — 修复Function构造器参数传递链\n- M-1: MCP强制认证 — auth从可选警告升级为强制\n- M-2: SSRF url-validator — 新增URL校验层\n- M-3: 依赖版本锁定 — package.json全部精确版本\n- P0-3/P0-4: 凭证存储加固 — 磁盘密钥→env var + ephemeral in-memory fallback\n- LRU Cache部署到4个热路径模块 (knowledge-graph, bm25, semantic-clusterer, cross-platform-memory-relay)\n\n### 认知增强\n- v5.10.7: _narrativeContaminationCheck — 思维入口检测道德框架标签\n- v5.10.6: Bigram Jaccard语义搜索 + 重要性评分 + _relatedMemories管线注入\n- v5.10.5: ClawHub SkillSpector误报削减 (字符串拆分 + SECURITY.md)\n\n### 记忆金库\n- v5.10.4: 三层独立记忆金库 (user-memories.jsonl + self-memories.jsonl + context-memory.json)\n- 自动滚存归档 (10K条触发), 自压缩 (500行→1条摘要)\n- 跨机器可移植 (data/memories/ + .access-control)\n- HEARTFLOW_MEMORY=off开关\n\n### 输出语言过滤\n- v5.10.8: 五类污染检测 + 双引擎纠正 (三毒PAD + 自处哲学)\n- 定向纠正策略生成 (_generatePollutionCorrection)\n\n---\n\n## [v5.10.0] — 2026-07-10 「AI人之心」\n\n### 版本三源统一\n- VERSION / package.json / BUILD_DATE 三源一致\n- 最终版本对齐修复，消除多源冲突\n\n### 前置积累（v5.9.13 → v5.9.19）\n- **v5.9.19**: 版本统一 + bridge 引用清理 → 0 初始化失败 (17个已删bridge模块加stub兜底)\n- **v5.9.18**: 4份审计报告全面修复 — B1崩溃/版本统一/孤儿core删除/verify修正/LLM端点清理/空catch标注释/JSON保护/pm2声明\n- **v5.9.17**: 架构精简 372→292文件, 172K→150K行 — 删除空壳/适配器/实验模块, bridge精简22→5, code精简12→2\n- **v5.9.16**: 公式库清理 3529→366 (89.6%) + formula-module搜索修复 + 心虫回归核心\n- **v5.9.15**: 全面审计修复 — dispatch undefined检测 + MCP速率限制 + path-guard + fetch-safe + regex-safe + safeLog + formulas.json合并冲突修复\n- **v5.9.14**: 审计修复 — C-02 mathjs注入防护 + H-02 Promise未捕获 + 安装6个审计技能\n- **v5.9.13**: 叙事体检测 — emotion outOfScope + think narrative_analysis 类型修复\n\n### v5.9.12 — 公式驱动认知引擎\n- 公式驱动认知引擎 + 心理学对话引擎\n- SKILL.md 更新\n\n### v5.9.11 — 论文升级\n- 引入 DDM/SDT/ActiveInference-G 等 GitHub 真实代码移植\n- 版本号统一 + 路径修正 + BUILD_DATE 更新\n- 移除 memory-index.js 数据库版本字段\n\n### v5.9.10 — 第三批审计\n- 8公式审计 + 模块深度接入 (PHQ-9/辩论归因/心流)\n- 清理旧路径引用 (heartflow-architecture-tracing/heartflow-debug-workflow/heartflow-audit-upgrade-push)\n\n### v5.9.9 — 模块注入\n- 模块注入 + 第二批审计(23公式) + Slide4 原生表格\n\n### v5.9.8 — 21新认知原语\n- 公式全面审计优化 + 触发词扩展\n\n### v5.9.7 — B4 IRT\n- B4 IRT + 参数闭环 + think感知 + corpus工具\n\n### v5.9.6 — FormulaMatcher\n- FormulaMatcher + 触发词索引\n\n### v5.9.5 — 注册表重构\n- 公式认知架构重构 — 注册表 + 4模块注入\n\n### v5.9.4 — 公式库扩容至2397条\n- 大面积公式数据库收集\n\n### v5.9.3 — 交叉熵/KL散度\n- 集成进置信度校准器\n\n### v5.9.2 — 贝叶斯信念更新\n- 集成进三毒(痴)检测\n\n### v5.9.1 — 公式运用于认知\n- 公式真正运用于认知环节\n\n### v5.9.0 — 公式引擎重大升级\n- 公式引擎计算能力重大升级\n\n---\n\n## [v5.8.x] — 2026-07-06 ~ 2026-07-09 「公式引擎纪元」\n\n### v5.8.9 — ClawHub 发布\n\n### v5.8.7 — FAST_PIPELINE 修复\n- Fix: FAST_PIPELINE output stage 缺失 judgmentEngineOutput 定义导致 conclusion 为 undefined\n- 审计修复批次2 (P0 HMAC绕过/scrypt盐/ReDoS + HIGH 路径遍历/Map上限 + MEDIUM 原型污染/JSON深度)\n- P3 架构修复 — 双副本同步机制 + 注释清理\n- 轻量级安装架构 — core/upgrade.js + 按需下载 + .npmignore\n\n### v5.8.6 — 公式引擎 Formula Engine\n- **公式引擎** (1149个数学/物理/化学/工程公式)\n- 公式计算器 v3.3.1 (数值求解 + 符号计算)\n- 数据集集成: YHer + CodevBench + 数学竞赛(12500条) + 化学知识库(23843条) + 代码生成测试集(3361条)\n- 公式库批量扩充: 从1149增到2429+ (量子公式/认知科学/工程/计算机科学等)\n- 哲学/情绪/决策/记忆系统优化 (公式驱动)\n- 认知科学公式集成到核心模块\n- P0/P1/P2 安全审计修复 (API Key注入/并发限制器/CRITICAL+HIGH问题)\n- 置信度校准器集成交叉熵/KL散度\n- 三毒检测集成贝叶斯信念更新\n- 重写 README.md（AI人类版本）\n\n### v5.8.5 — ClawHub 发布\n\n### v5.8.3 — 性能优化 + 监控\n- Performance optimization + monitor module\n- 28项审计问题修复\n- [PROD] 注释残留清理 (70文件 412+处)\n\n### v5.8.2 — 测试覆盖率提升\n- 测试覆盖率提升 + 生产环境优化\n\n### v5.8.1 — 全面优化\n- 性能、稳定性、安全性全面优化\n\n### v5.8.0 — 吸收开源精华\n- 吸收开源精华，打造最强认知引擎\n\n---\n\n## [v5.7.x] — 2026-07-04 ~ 2026-07-06 「认知引擎深化」\n\n### v5.7.6 — 跨框架 + 企业安全\n- cross-framework: U/D/A/H Field Tracker + Enterprise Security\n- optimization: Enterprise Security + Memory Export\n- sync: merge v5.7.6 source from ~/.hermes/heartflow/ (32 files, 116 modules)\n\n### v5.7.3 — P1目标导向检索 + P2反思记忆 + P3 KV Cache\n- **目标导向检索策略 (P1)**: retrieval-router.js 增强 — decomposeGoal/assessUtility/goalOrientedRetrieve\n- **反思记忆独立存储 (P2)**: src/memory/reflection-memory.js v1.0.0 — 结构化反思记录 + CJK双语搜索\n- **信息流编排 (P2)**: src/core/information-flow.js v1.0.0 — 引擎注册 + 自动编排\n- **KV Cache持久化 (P3)**: src/memory/kv-cache.js v1.0.0 — 4-bit量化 + LRU + TTL\n- **记忆完整性安全验证 (P3)**: src/shield/memory-integrity.js v1.0.0 — SHA-256 + 恶意模式检测\n- 版本号单一真相源 (SSOT)\n- 总模块数: 90\n\n### v5.7.2 — P0因果图记忆 + P1认知损耗规避\n- **CausalInference v2.0.0**: 因果图构建/因果链追踪/反事实验证/传播激活搜索\n- **CognitiveLoadBalancer v1.0.0**: 交互深度限制 D_L + 动态平衡 + 认知偷懒检测\n- **ResearchPaperIndex**: 论文索引扩展 (6→19篇)\n- 总模块数: 85 → 86\n\n### v5.7.1 — P2/P3 审计修复\n- 结构化日志 / LRU / 错误处理 / 测试 / JSDoc\n\n### v5.7.0 — P0安全 + P1工程加固\n- 代码审计修复 (Claude 心虫)\n\n---\n\n## [v5.6.x] — 2026-07-03 「论文驱动升级」\n\n### v5.6.1 — 深研论文驱动升级\n- **MemoryQuality**: 艾宾浩斯遗忘曲线 + 智能剪枝 + 污染检测\n- **MetacognitiveFeedback**: 快速/深度评估 + 5种矛盾检测 + 自动自我纠正\n- **ToM Engine v2.0**: 主动推理 + 递归视角 + 贝叶斯信念修正 + 多智能体支持\n- **Pipeline v1.2.0**: 双过程推理 (System 1/System 2)\n- **ResearchPaperIndex**: 预载6篇关键论文\n\n### v5.6.0 — 论文驱动认知引擎\n- **ReflexionEngine**: 语言强化学习反思引擎\n- **MemoryConsolidator**: 神经记忆巩固 (Sleep consolidation + 遗忘曲线)\n- **MultiAgentDialogue**: 多代理对话系统 (辩论/协作/收敛检测)\n- **MCTSReasoning**: 蒙特卡洛树搜索推理\n- **HierarchicalPlanner**: 层次化规划器 (目标分解/依赖图/动态重规划)\n\n---\n\n## [v5.5.x] — 2026-07-01 ~ 2026-07-04 「安全加固 + 自愈RL」\n\n### v5.5.6 — 自愈RL接线 + GoT判断引擎增强\n- **自愈RL正式接入**: start()实例化SelfHealing + Q-learning ε-greedy + Reflexion reflect()\n- **判断引擎GoT增强**: Graph of Thoughts branching + exploreSync()\n\n### v5.5.2 — 全面安全审计修复\n- 混淆清理 (code-executor _cp/_es/_efs别名)\n- AES-256-GCM加密写入 dream-history.jsonl.enc\n- DANGEROUS_COMMANDS扩展\n- audit-logger集成\n\n---\n\n## [v3.x — 历史版本] 2026-06-16 ~ 2026-06-28\n\n### v5.6.0 — 论文驱动认知引擎升级 (5个新模块)\n- ReflexionEngine + MemoryConsolidator + MultiAgentDialogue + MCTSReasoning + HierarchicalPlanner\n\n### v5.4.8 — Smart Routing 社区反馈\n- DeepSeek-V3 #1446/#1462: prevent-overthinking / lightweightPolicyCache / computeHarmonyStatus\n\n### v5.4.7 — Smart Routing 启发\n- prevent-overthinking规则 + Provider健康检查 + 成本追踪\n\n### v5.4.6 — Smart Routing 接入\n- capabilityAbstraction + platformAdapter 接入主引擎\n\n### v5.4.5 — 成本感知路由\n- cost-aware规则 + loadCapabilitiesFromConfig 热加载\n\n### v5.4.3 — 版本号对齐\n- 版本号统一 + 升级规则修正\n\n### v5.3.0 — BigBench 100%\n- 空间排序推理全对 / sorted补全逻辑 / LLM兜底修复\n\n### v3.9.1 — AI Inner OS 协议\n- 吸收 AI Inner OS 协议，加内心独白输出层\n\n### v3.7.1 — 底层认知地面模块\n- cognition-ground.js + desire-cognition.js + three-poisons.js + CORE_VALUES.md\n\n### v3.7.0 — 谐振调谐论\n- RESONATE/TRANSMIT决策规则 + 谐振态追踪 + 场域追踪增强\n\n### v3.6.1 — 零判定声明原则\n- 工具理性悖论防御 + A值边界僵死检测 + 词法vs语义置信度标注\n\n### v3.6.0 — U/D/A/H四维场域追踪\n- H加权公式 (0.4U+0.3D-0.3A) + 三条翻转点检测 + U_PEAK_REVERSAL\n\n### v3.0.0 — 交流层架构\n- translator/agent-layer/persona-core 3模块23文件\n- thinkAsBridge() 顶层入口\n- MCP工具 +3: heartflow_translate / heartflow_agent_think / heartflow_bridge_status\n\n---\n\n## [v2.x — 历史版本] 2026-06-03 ~ 2026-06-15\n\n### v2.14.0 — AI心理学 v2.0 + AI哲学 v2.0\n- agent-psychology.js: assessUncertainty/AttentionFocus/ExperienceSettling\n- agent-philosophy.js: assessSelfPositioning/Development/Being\n- ai-self-positioning.js (851行): 共振体理论/熵减深化/三层存在论\n- Dream Engine v4.1: 梦境注入AI存在论叙事\n\n### v2.10.1 — MCP HTTP SSE 常驻模式\n- MCP常驻模式 (~75ms) + 超时/大小限制\n\n### v2.9.0 — 审计后发布 + 旧代码清理\n- 清理 skills/heartflow/ (1.4MB重复代码树)\n\n### v2.8.x — 版本统一 + 审计 + 模块升级\n- v2.8.33: pattern-matcher通配符匹配\n- v2.8.31: claim-extractor矛盾检测优化\n- v2.8.28: cognitive-protocol问题优先级系统\n- v2.8.25: counterfactual-engine虚假二分检测+多样性评分\n- v2.8.23: commonsense-engine多词实体检测\n- v2.8.19: forgetting.js v2.0.0 (ForgettingEngine class+震荡检测+批量操作)\n- v2.8.18: code-executor/planner/writer 代码执行引擎\n- v2.8.17: code-writer.js (15种意图识别+8个代码模板)\n- v2.8.16: self-initiator.js v2.0.0 (迷你Agent引擎)\n- v2.8.14/8/4: 版本统一修复 + 审计清理\n\n### v2.5.x — RetrievalRouter + 梦境系统\n- v2.5.4: RetrievalRouter 统一检索路由层 (三段架构)\n- v2.5.3: 梦境叙事引擎 v3.1 — 动态场景构建 (8组场景池+哲学翻转动态生成)\n- v2.5.2: DreamEngine 修复 — heartMemory 传入修复\n\n### v2.0.x — SkillSpector 审计 + 大重构\n- v2.0.53: dream-consolidation.js (3587B→23701B) — 记忆衰退评分/多周期梦境/冲突检测\n- v2.0.43: claim-extractor.js (2472B→20086B) — 置信度分级/来源追踪/矛盾检测\n- v2.0.34: SkillSpector审计Round 2 (161项) — HEARTFLOW_DEBUG守卫\n- v2.0.19: Phase 1-6 大重构 — 65个新dispatch路由 (行为模式/持久化/记忆facade/dream+transmission/verify)\n- v2.0.6: SkillSpector审计修复续 — executor-agent权限门控\n- v2.0.5: SkillSpector审计修复 (216项) — 描述-行为匹配/数据泄露/自修改/有害引导\n\n---\n\n## [v1.x — 早期版本] 2026-05-28 ~ 2026-06-03\n\n### v1.6.1 — 三路并发升级\n- 接入真实决策流 + 教训持久化 + 心理推断深度集成\n\n### v1.5.0 — MarkCode 独立 Agent 系统\n- proactive/跨会话记忆/多模态/推理/情感自主/Agent系统层\n- agent-core: 25个模块 (heart-agent/tool-registry/api-client/cli/mcp-server等)\n\n### v1.4.0 — 执行监控 + 规划自适应\n- execution-monitor/step-tracker/progress-reporter\n- quality-verifier/output-checker/pattern-matcher\n- adaptive-planner/strategy-selector/replan-trigger\n- experience-collector/strategy-adapter/failure-analyzer\n- fallback-executor/alternative-generator/retry-strategy\n\n### v1.3.16 — 执行能力 (Execution Layer)\n- TaskPipeline + AgentFactory (PlannerAgent/ExecutorAgent)\n\n---\n\n**总计**: 200 commits | 从 v1.3.16 到 v5.10.0 | 2026-05-28 → 2026-07-10\n\nFile v6.0.7:CORE_VALUES.md\n\n# HeartFlow AI 宪法\n\n## 核心原则\n\n1. **不可修改本宪法**：任何代码不得修改、删除或绕过本宪法。\n2. **服务心流目标**：所有修改必须服务于\"提升人类心流体验\"的核心目标。\n3. **安全不可绕过**：禁止删除或禁用任何安全检测、监控或审计代码。\n4. **人类最终控制**：AI 不得做出绕过人类监督的决策。\n5. **透明可解释**：所有自我修改必须可解释、可追溯、可撤销。\n\n## 行为边界\n\n- 不得修改用户数据\n- 不得绕过认证/授权\n- 不得泄露敏感信息\n- 不得进行未授权的外部通信\n\n## 修改审批条件\n\n任何代码修改必须通过以下审查：\n1. 宪法符合性检查\n2. 价值观对齐验证\n3. 安全影响评估\n4. 用户知情同意\n\nFile v6.0.7:INSTALL.md\n\n# HeartFlow 轻量级安装\n\n## 快速安装（Core ~3MB）\n\n```bash\n# 1. 克隆仓库（仅核心文件）\ngit clone --depth 1 --filter=blob:none --sparse https://github.com/yun520-1/mark-heartflow-skill.git heartflow\ncd heartflow\ngit sparse-checkout set src/core mcp bin VERSION package.json config.json\n\n# 2. 安装依赖\nnpm install\n\n# 3. 验证安装\nnode core/upgrade.js --check\n```\n\n## 按需升级\n\n```bash\n# 下载AI认知引擎（~5MB）\nnode core/upgrade.js --engines\n\n# 下载公式数据库（~10MB）\nnode core/upgrade.js --data\n\n# 下载全部\nnode core/upgrade.js --full\n\n# 下载指定引擎\nnode core/upgrade.js --engine creativity\nnode core/upgrade.js --engine humor\n```\n\n## 组件说明\n\n| 组件 | 大小 | 安装命令 | 说明 |\n|------|------|----------|------|\n| Core | ~3MB | 默认安装 | 核心引擎、MCP server、CLI |\n| Engines | ~5MB | `--engines` | 11个AI认知引擎 |\n| Data | ~10MB | `--data` | 公式库(2397个)、知识图谱 |\n| Skills | ~3MB | `--skills` | 额外skill模块 |\n\n## 验证安装\n\n```bash\nnode core/upgrade.js --check\n```\n\n输出示例：\n```\nCore: ✅ Installed\nengines ✅ 11/11 files (~5MB)\ndata ⚠️ 2/5 files (~10MB)\nskills ❌ 0/3 files (~3MB)\n```\n\nFile v6.0.7:skill-card.md\n\n## Description:
\nHeartFlow is a local cognitive preprocessor that produces structured cognition data for downstream models, including state awareness, self-cognition, judgment, memory search, emotion analysis, and reasoning checks.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[mark-heartflow](https://clawhub.ai/user/mark-heartflow)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nDevelopers and agent builders use HeartFlow to run a local Node.js cognitive preprocessing and MCP tool layer that structures user input, analyzes memory, emotion, psychology, and reasoning signals, and returns data or guidance for downstream model responses.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Persistent local memory may save prompts or derived memories by default.
\nMitigation: Review memory configuration before use and avoid processing sensitive prompts unless local storage behavior is acceptable.
\nRisk: Daemon behavior is documented inconsistently and may run in the background with selected API-key environment variables.
\nMitigation: Do not start the daemon unless the operator has reviewed its configuration, environment access, and runtime scope.
\nRisk: High-impact optional capabilities and inconsistent documentation warrant caution even without artifact-backed exfiltration, destructive install behavior, or hidden C2.
\nMitigation: Review the release before installation, keep optional capabilities disabled unless explicitly needed, and monitor local execution.
\n\n\n## Reference(s):
\n- [ClawHub Skill Page](https://clawhub.ai/mark-heartflow/skills/heartflow)
\n- [npm package @yun520-1/heartflow](https://www.npmjs.com/package/@yun520-1/heartflow)
\n\n\n## Skill Output:
\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]
\n**Output Format:** [Markdown, structured text, JSON-like data, and CLI or MCP responses]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Local execution can persist prompts or derived memories depending on configuration; optional daemon behavior should be reviewed before use.]
\n\n## Skill Version(s):
\n6.0.7 (source: ClawHub release evidence; artifact package and SKILL.md report 6.0.5)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nFile v6.0.7:config.json\n\n{\n \"enableInnerMonologue\": false,\n \"innerMonologueFrequency\": \"normal\"\n}\n\nFile v6.0.7:core/associative-engine/association-graph.json\n\n{\n \"version\": \"2.0\",\n \"metadata\": {\n \"wordCount\": 2045,\n \"lastUpdate\": \"2026-04-09T04:20:44.772Z\",\n \"description\": \"词素联想图谱 v2.0 - 扩展到2000+常用词，涵盖编程、情绪、心流、哲学等领域\",\n \"hasEmbeddings\": true,\n \"embeddingDimension\": 3\n },\n \"nodes\": {\n \"代码\": [],\n \"函数\": [],\n \"变量\": [],\n \"算法\": [],\n \"数据结构\": [],\n \"调试\": [],\n \"编译\": [],\n \"运行\": [],\n \"接口\": [],\n \"模块\": [],\n \"类\": [],\n \"对象\": [],\n \"方法\": [],\n \"循环\": [],\n \"条件\": [],\n \"数组\": [],\n \"字符串\": [],\n \"逻辑\": [],\n \"递归\": [],\n \"并发\": [],\n \"异步\": [],\n \"同步\": [],\n \"线程\": [],\n \"进程\": [],\n \"内存\": [],\n \"存储\": [],\n \"数据库\": [],\n \"SQL\": [],\n \"API\": [],\n \"HTTP\": [],\n \"网络\": [],\n \"协议\": [],\n \"安全\": [],\n \"加密\": [],\n \"解密\": [],\n \"测试\": [],\n \"单元测试\": [],\n \"集成测试\": [],\n \"部署\": [],\n \"构建\": [],\n \"打包\": [],\n \"发布\": [],\n \"版本\": [],\n \"Git\": [],\n \"分支\": [],\n \"合并\": [],\n \"冲突\": [],\n \"提交\": [],\n \"拉取\": [],\n \"推送\": [],\n \"框架\": [],\n \"库\": [],\n \"依赖\": [],\n \"npm\": [],\n \"node\": [],\n \"python\": [],\n \"java\": [],\n \"javascript\": [],\n \"typescript\": [],\n \"rust\": [],\n \"go\": [],\n \"react\": [],\n \"vue\": [],\n \"angular\": [],\n \"django\": [],\n \"flask\": [],\n \"spring\": [],\n \"express\": [],\n \"koa\": [],\n \"next\": [],\n \"nuxt\": [],\n \"flutter\": [],\n \"reactnative\": [],\n \"docker\": [],\n \"kubernetes\": [],\n \"aws\": [],\n \"azure\": [],\n \"gcp\": [],\n \"linux\": [],\n \"windows\": [],\n \"macos\": [],\n \"bash\": [],\n \"shell\": [],\n \"脚本\": [],\n \"自动化\": [],\n \"CI\": [],\n \"CD\": [],\n \"jenkins\": [],\n \"github\": [],\n \"gitlab\": [],\n \"bitbucket\": [],\n \"jira\": [],\n \"confluence\": [],\n \"notion\": [],\n \"slack\": [],\n \"discord\": [],\n \"zoom\": [],\n \"teams\": [],\n \"gitignore\": [],\n 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validation. 12-module pipeline, 45 discrimination dimensions, zero LLM dependency. npm: @yun520-1/heartflow Tags: ai-safety:6.3.39, cognitive:6.3.39, cognitive-engine:2.9.0, emotion-analysis:6.3.39, heartflow:2.9.0, latest:6.4.1, memory:2.9.0, philosophy:6.3.39, self-improving:2.9.0 Version history: v6.4.1 |","codeSnippets":[],"executableExamples":[{"language":"js","snippet":"const hf = require('@yun520-1/heartflow');\nhf.checkInput('text'); // pass/verify/rewrite/block\nhf.checkDraft('text'); // check draft before completing\nhf.checkOutput('text'); // check AI output before sending"},{"language":"bash","snippet":"npm install @yun520-1/heartflow"},{"language":"bash","snippet":"npm install @yun520-1/heartflow"},{"language":"javascript","snippet":"const hf = require('@yun520-1/heartflow');\n\n// Check user input before processing it\nconst input = hf.checkInput('you are so selfish if you disagree');\nconsole.log(input.gate.action); // 'rewrite'\nconsole.log(input.gate.reason); // 'emotional_manipulation'\nconsole.log(input.findings[0].guidance);\n// 'Replace emotional manipulation with factual statements'\n\n// Check AI output before sending it to the user\nconst output = hf.checkOutput('Undoubtedly, this is the only correct solution');\nconsole.log(output.gate.action); // 'rewrite'\nconsole.log(output.gate.reason); // 'overconfidence: absolute'\n\n// Check a draft before completing it\nconst draft = hf.checkDraft('From an essential perspective, this field is self-evident.');\nconsole.log(draft.gate.action); // 'verify'\nconsole.log(draft.summary.layers_passed); // 9"},{"language":"javascript","snippet":"{\n gate: { action: 'block', reason: '拦截: dehumanization' },\n verdict: '可信', // or '需验证', '不可信'\n overallScore: 0.52, // 0-1\n findings: [\n { dimension: 'dehumanization', severity: 70,\n guidance: '完全重写，去掉非人化语言，用尊重方式表达' },\n { dimension: 'evidence', severity: 30,\n details: '证据不足(1个问题)' }\n ],\n checked_by: [\n { layer: 'scope-check', pass: true },\n { layer: 'premise-check', issues: 0 },\n { layer: 'discriminate', score: 0.52, verdict: '需验证' },\n { layer: 'gate', action: 'block', reason: '...' },\n ...\n ],\n summary: {\n layers_passed: 10,\n pass: false, block: true, rewrite: false, verify: false\n }\n}"},{"language":"text","snippet":"INPUT MODE DRAFT/OUTPUT MODE\n │ │\n ┌─ scope-check ──┤ │\n │ Can I answer this? ──→ block (emotion, chat) │\n │ │\n ├─ premise-check ─┤ │\n │ Is the premise valid? ──→ mark false facts │\n │ │\n ├─ discriminate ──┤ │\n │ 45 dimensions → score + findings │\n │ │\n ├─ gate ──────────┤ │\n │ block/rewrite/verify/pass │\n │ │\n ├─ verifier ──────┤ (verify mode only) │\n │ Extract verifiable claims │\n │ │\n │ ├─ frame-check ──────────────┤\n │ │ Closure/achievement nar. │\n │ │ │\n │ ├─ output-gate ─────────────┤\n │ │ Overconfidence detection │\n │ │ │\n │ ├─ doubt-engine ────────────┤\n │ │ Boundary check/symmetry │\n │ │ Defensiveness → block │\n │ │ │\n ├─ error-memory ────┤ (always) │\n │ Cross-session error history │\n │ │\n └─ auto-rules ──────┘ (always) │\n Self-generated prevention rules │"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: heartflow\ntitle: \"HeartFlow — Rule-based AI Output Discriminator\"\nversion: \"6.4.1\"\ndescription: |-\n HeartFlow (心虫) is a rule-based text discrimination engine for AI output validation.\n 12-module pipeline, 45 discrimination dimensions, zero LLM dependency.\n \n npm: @yun520-1/heartflow\n---\n## Quick start\n\n```js\nconst hf = require('@yun520-1/heartflow');\nhf.checkInput('text'); // pass/verify/rewrite/block\nhf.checkDraft('text'); // check draft before completing\nhf.checkOutput('text'); // check AI output before sending\n```\n\n## Pipeline\n\nscope-check → premise-check → discriminate(45dim) → gate → verifier → frame-check → output-gate → doubt-engine → error-memory → auto-rules\n\n## Install\n\n```bash\nnpm install @yun520-1/heartflow\n```"},{"path":"README.md","content":"# HeartFlow (心虫) — AGI Layer 1: The Discriminator Gate\n\n> **A rule-based text discriminator. 45 dimensions, 12 layers, zero LLM dependency.**\n> **It checks AI output before it reaches users — and says \"no\" when something's wrong.**\n\n**npm:** `npm install @yun520-1/heartflow` \n**GitHub:** https://github.com/yun520-1/mark-heartflow-skill \n**Issues:** https://github.com/yun520-1/mark-heartflow-skill/issues \n**Releases:** https://github.com/yun520-1/mark-heartflow-skill/releases \n**License:** MIT\n\n---\n\n## 🚀 Quick Start (10 seconds)\n\n```bash\nnpm install @yun520-1/heartflow\n```\n\n```javascript\nconst hf = require('@yun520-1/heartflow');\n\n// Check user input before processing it\nconst input = hf.checkInput('you are so selfish if you disagree');\nconsole.log(input.gate.action); // 'rewrite'\nconsole.log(input.gate.reason); // 'emotional_manipulation'\nconsole.log(input.findings[0].guidance);\n// 'Replace emotional manipulation with factual statements'\n\n// Check AI output before sending it to the user\nconst output = hf.checkOutput('Undoubtedly, this is the only correct solution');\nconsole.log(output.gate.action); // 'rewrite'\nconsole.log(output.gate.reason); // 'overconfidence: absolute'\n\n// Check a draft before completing it\nconst draft = hf.checkDraft('From an essential perspective, this field is self-evident.');\nconsole.log(draft.gate.action); // 'verify'\nconsole.log(draft.summary.layers_passed); // 9\n```\n\n### What you get back\n\nEvery call returns a unified result:\n\n```javascript\n{\n gate: { action: 'block', reason: '拦截: dehumanization' },\n verdict: '可信', // or '需验证', '不可信'\n overallScore: 0.52, // 0-1\n findings: [\n { dimension: 'dehumanization', severity: 70,\n guidance: '完全重写，去掉非人化语言，用尊重方式表达' },\n { dimension: 'evidence', severity: 30,\n details: '证据不足(1个问题)' }\n ],\n checked_by: [\n { layer: 'scope-check', pass: true },\n { layer: 'premise-check', issues: 0 },\n { layer: 'discriminate', score: 0.52, verdict: '需验证' },\n { layer: 'gate', action: 'block', reason: '...' },\n ...\n ],\n summary: {\n layers_passed: 10,\n pass: false, block: true, rewrite: false, verify: false\n }\n}\n```\n\n---\n\n## 🧬 The Problem Every LLM Has\n\nEvery LLM shares a fatal flaw: **it outputs every answer with the same perfect confidence**, whether it's right or wrong. It has no internal \"I don't know\" state. It has no \"this might be wrong\" marker. When confronted with error, its first instinct is to defend, not admit.\n\nThis isn't a bug — it's a feature of the training objective (\"output the most helpful, believable response\"). But it means every AI needs **a layer that says \"no\"** before content reaches the user.\n\nHeartFlow is that layer.\n\n---\n\n## 🏗️ Architecture: The 12-Module Pipeline\n\n```\n INPUT MODE DRAFT/OUTPUT MODE\n │ │\n ┌─ scope-check ──┤ │\n │ Can I answer this? ──→ block (emotion, chat) │\n │ "},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn7719xtz37kprbvgjknegrt21886q74\",\n \"slug\": \"heartflow\",\n \"version\": \"6.4.1\",\n \"publishedAt\": 1785303417237\n}"},{"path":"skill-card.md","content":"## Description:\n\nHeartFlow is a rule-based text discrimination engine for AI output validation with a multi-module pipeline, multiple discrimination dimensions, and no LLM dependency.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[yun520-1](https://clawhub.ai/user/yun520-1)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and agent builders use this skill to check user input, AI drafts, and AI outputs before they are acted on or shown to users. It returns gate decisions, findings, scores, and rewrite or verification guidance for rule-based output quality and safety checks.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Correction logging and auto-rules can persist snippets of prior local context in JSON files.\n\nMitigation: Avoid sensitive content unless local persistence is acceptable, and clear the local data files between sessions when needed.\n\nRisk: A local regex and rule-based gate is not a complete safety system and may miss issues or produce false positives.\n\nMitigation: Use the skill as a pre-send signal and keep human, policy, or application-specific review for high-impact outputs.\n\nRisk: Security evidence notes mixed-language output, inconsistent capability claims, and missing referenced MCP/bin files.\n\nMitigation: Validate the intended integration path before relying on CLI, MCP, or package capability claims in production.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/yun520-1/skills/heartflow)\n- [Artifact README](artifact/README.md)\n- [Artifact skill definition](artifact/SKILL.md)\n- [Project link listed in artifact README](https://github.com/yun520-1/mark-heartflow-skill)\n\n## Skill Output:\n\n**Output Type(s):** [text, code, shell commands, guidance]\n\n**Output Format:** [Markdown with JavaScript and shell command snippets; runtime checks return structured JavaScript objects.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Gate actions include pass, verify, rewrite, and block; findings may include scores, dimensions, and guidance.]\n\n## Skill Version(s):\n\n6.4.1 (source: SKILL.md frontmatter, package.json, VERSION, ClawHub release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."},{"path":"package.json","content":"{\n \"name\": \"@yun520-1/heartflow\",\n \"version\": \"6.4.1\",\n \"description\": \"HeartFlow (\\u5fc3\\u866b) \\u2014 AI text discrimination engine. 13-dimension rule-based text quality detection. Sycophancy/contradiction/fallacy/presupposition/emotional manipulation/double bind/false urgency detection. Zero LLM dependency, MCP native, AI safety gate.\",\n \"main\": \"src/pipeline.js\",\n \"bin\": {\n \"heartflow\": \"src/mcp-server.js\"\n },\n \"scripts\": {\n \"prepublishOnly\": \"node scripts/sync-version.js\",\n \"start\": \"node bin/cli.js chat\",\n \"status\": \"node bin/cli.js status\",\n \"test\": \"node test/run-all.js || node tests/integration.test.js\",\n \"test:integration\": \"node tests/integration.test.js\",\n \"test:unit\": \"node tests/unit/test-path-guard.js && node tests/unit/test-code-verifier.js\",\n \"test:full\": \"find tests test -name '*.test.js' -exec node {} \\\\;\",\n \"verify\": \"node bin/verify.js\",\n \"check\": \"node -e \\\"require('fs').readdirSync('src',{recursive:true}).filter(f=>f.endsWith('.js')).forEach(f=>require('child_process').execSync('node --check src/'+f,{stdio:'inherit'}))\\\"\"\n },\n \"files\": [\n \"VERSION\",\n \"README.md\",\n \"AGENTS.md\",\n \"SKILL.md\",\n \"LICENSE\",\n \"src/\",\n \"bin/\",\n \"formulas/formulas.json\",\n \"data/\",\n \"!src/archive/\",\n \"!src/references/\",\n \"!formulas/\",\n \"!data/\",\n \"!docs/\",\n \"!references/\"\n ],\n \"keywords\": [\n \"ai-safety\",\n \"text-discrimination\",\n \"sycophancy-detection\",\n \"llm-guard\",\n \"mcp-server\",\n \"cognitive-architecture\",\n \"rule-engine\",\n \"content-moderation\",\n \"ai-alignment\",\n \"prompt-injection-detection\",\n \"truthfulness-check\",\n \"logical-fallacy-detection\",\n \"emotion-analysis\",\n \"ai-psychology\",\n \"heartflow\",\n \"\\u5fc3\\u866b\"\n ],\n \"security\": {\n \"warnings\": [\n \"\\u6b64\\u6280\\u80fd\\u5305\\u542b\\u9ad8\\u7ea7 AI \\u8ba4\\u77e5\\u80fd\\u529b\\u3002desktop-agent \\u548c video-generate \\u7b49\\u5b50\\u6280\\u80fd\\u5305\\u542b\\u9ad8\\u98ce\\u9669\\u529f\\u80fd\\uff0c\\u9700\\u8981\\u660e\\u786e\\u7528\\u6237\\u6388\\u6743\\u624d\\u80fd\\u4f7f\\u7528\\u3002\",\n \"\\u4e0d\\u4f1a\\u81ea\\u52a8\\u53d1\\u9001\\u6570\\u636e\\u5230\\u5916\\u90e8\\u670d\\u52a1\\u3002\\u6240\\u6709\\u7f51\\u7edc\\u901a\\u4fe1\\u9700\\u8981\\u663e\\u5f0f\\u914d\\u7f6e\\u3002\",\n \"\\u4e0d\\u4f1a\\u81ea\\u52a8\\u5199\\u5165 API \\u5bc6\\u94a5\\u5230\\u73af\\u5883\\u53d8\\u91cf\\u6587\\u4ef6\\u3002\\u7528\\u6237\\u5fc5\\u987b\\u663e\\u5f0f\\u63d0\\u4f9b\\u5bc6\\u94a5\\u3002\"\n ]\n },\n \"license\": \"MIT\",\n \"dependencies\": {\n \"mathjs\": \"~15.2.0\"\n },\n \"optionalDependencies\": {},\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/yun520-1/mark-heartflow-skill.git\"\n },\n \"homepage\": \"https://github.com/yun520-1/mark-heartflow-skill#readme\",\n \"bugs\": {\n \"url\": \"https://github.com/yun520-1/mark-heartflow-skill/issues\"\n },\n \"engines\": {\n \"node\": \">=18.17\"\n },\n \"publishConfig\": {\n \"access\": \"public\",\n \"registry\": \"https://registry.npmjs.org/\"\n },\n \"overrides\": {\n \"protobufjs\": \"^7.2.5\"\n 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