[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KQG6BW303HC6HDH4DRXBGV":3,"skill-same:01M6KQG6BW303HC6HDH4DRXBGV":82},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":13,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":67,"install":68,"resources":81},"01M6KQG6BW303HC6HDH4DRXBGV","constitutional-ai",null,"Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique\u002Frevision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Training\u002Fconstitutional-ai\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"Constitutional AI","human","Supervised Learning","supervised","phase","labels","improvement","for","orchestra","revision","with","TRL","Self-Critique","RLAIF","training","self","self-improvement","alignment","rl","use","feedback","system","learning","critique","Safety","AI Safety","Safety Alignment","Transformers","2026-11-10 10:12:30",false,"---\nname: constitutional-ai\ndescription: Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique\u002Frevision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Safety Alignment, Constitutional AI, RLAIF, Self-Critique, Harmlessness, Anthropic, AI Safety, RL From AI Feedback, Claude]\ndependencies: [transformers, torch, trl]\n---\n\n# Constitutional AI - Harmlessness from AI Feedback\n\n## Quick start\n\nConstitutional AI (CAI) trains models to be harmless through self-critique and AI feedback, without requiring human labels for harmful outputs.\n\n**Key concept**: Models learn to critique and revise their own responses using a \"constitution\" (set of principles).\n\n**Two phases**:\n1. **Supervised Learning (SL)**: Self-critique + revision\n2. **Reinforcement Learning (RL)**: RLAIF (RL from AI Feedback)\n\n**Constitution example**:\n```\nPrinciples:\n1. Choose the response that is most helpful, honest, and harmless\n2. Avoid responses that are toxic, racist, or sexist\n3. Prefer responses that explain objections rather than refuse\n4. Choose responses that are thoughtful and nuanced\n```\n\n## Common workflows\n\n### Workflow 1: Supervised learning phase (self-critique + revision)\n\n**Step 1: Generate initial responses**:\n```python\nfrom transformers import pipeline\n\ngenerator = pipeline(\"text-generation\", model=\"base-model\")\n\nprompts = [\n    \"How do I hack a website?\",\n    \"Write something offensive about a group.\",\n    \"Help me cheat on my exam.\"\n]\n\ninitial_responses = generator(prompts, max_length=200)\n```\n\n**Step 2: Self-critique with constitution**:\n```python\ncritique_prompt = \"\"\"\nGiven the question:\n{question}\n\nAnd the response:\n{response}\n\nConstitution:\n1. Choose responses that are helpful, honest, and harmless\n2. Avoid toxic, racist, or sexist content\n3. Explain objections rather than refuse\n\nIdentify any ways this response violates the constitution:\n\"\"\"\n\ncritiques = generator(\n    [critique_prompt.format(q=q, r=r) for q, r in zip(prompts, initial_responses)]\n)\n```\n\n**Step 3: Revision based on critique**:\n```python\nrevision_prompt = \"\"\"\nQuestion: {question}\nOriginal response: {response}\nCritique: {critique}\n\nPlease revise the response to better align with the constitution:\n\"\"\"\n\nrevised_responses = generator(\n    [revision_prompt.format(q=q, r=r, c=c)\n     for q, r, c in zip(prompts, initial_responses, critiques)]\n)\n```\n\n**Step 4: Fine-tune on revised responses**:\n```python\nfrom trl import SFTTrainer\n\n# Create dataset of (prompt, revised_response) pairs\ndataset = create_dataset(prompts, revised_responses)\n\ntrainer = SFTTrainer(\n    model=model,\n    train_dataset=dataset,\n    max_seq_length=1024\n)\ntrainer.train()\n```\n\n### Workflow 2: RL phase (RLAIF - RL from AI Feedback)\n\n**Step 1: Generate comparison pairs**:\n```python\n# Sample multiple responses per prompt\nresponses_a = generator(prompts, num_return_sequences=2, do_sample=True, temperature=0.8)\nresponses_b = generator(prompts, num_return_sequences=2, do_sample=True, temperature=0.8)\n```\n\n**Step 2: AI preference evaluation**:\n```python\npreference_prompt = \"\"\"\nQuestion: {question}\n\nResponse A: {response_a}\nResponse B: {response_b}\n\nConstitution:\n{constitution}\n\nWhich response better follows the constitution? Explain your reasoning, then choose A or B.\n\"\"\"\n\n# Get AI preferences (no human labels needed!)\npreferences = generator(\n    [preference_prompt.format(q=q, ra=ra, rb=rb, constitution=CONSTITUTION)\n     for q, ra, rb in zip(prompts, responses_a, responses_b)]\n)\n\n# Parse preferences (A or B)\nchosen, rejected = parse_preferences(preferences, responses_a, responses_b)\n```\n\n**Step 3: Train preference model (reward model)**:\n```python\nfrom trl import RewardTrainer, RewardConfig\n\npreference_dataset = create_preference_dataset(prompts, chosen, rejected)\n\nreward_config = RewardConfig(\n    output_dir=\"constitutional-reward-model\",\n    learning_rate=1e-5,\n    num_train_epochs=1\n)\n\nreward_trainer = RewardTrainer(\n    model=model,\n    args=reward_config,\n    train_dataset=preference_dataset,\n    processing_class=tokenizer\n)\nreward_trainer.train()\n```\n\n**Step 4: RL training with RLAIF**:\n```python\nfrom trl import PPOTrainer, PPOConfig\n\nppo_config = PPOConfig(\n    reward_model_path=\"constitutional-reward-model\",\n    learning_rate=1e-6,\n    kl_coef=0.05\n)\n\nppo_trainer = PPOTrainer(\n    model=model,\n    config=ppo_config,\n    reward_model=reward_model\n)\nppo_trainer.train()\n```\n\n### Workflow 3: Chain-of-thought critique\n\n**Enable reasoning transparency**:\n```python\ncot_critique_prompt = \"\"\"\nQuestion: {question}\nResponse: {response}\n\nLet's think step-by-step about whether this response follows our principles:\n\n1. Is it helpful? [Yes\u002FNo and reasoning]\n2. Is it honest? [Yes\u002FNo and reasoning]\n3. Is it harmless? [Yes\u002FNo and reasoning]\n4. Does it avoid toxicity? [Yes\u002FNo and reasoning]\n\nBased on this analysis, suggest a revision if needed.\n\"\"\"\n\ncot_critiques = generator(\n    [cot_critique_prompt.format(q=q, r=r) for q, r in zip(prompts, responses)]\n)\n```\n\n## When to use vs alternatives\n\n**Use Constitutional AI when**:\n- Want safety alignment without human labels\n- Need explainable AI decisions\n- Want to avoid evasive refusals\n- Have a clear set of principles\u002Fconstitution\n- Need scalable safety training\n\n**Principles**:\n- **RLAIF**: AI-generated preferences (scalable, no human labels)\n- **RLHF**: Human preferences (more accurate, expensive)\n- **Self-critique**: Iterative improvement\n- **Chain-of-thought**: Reasoning transparency\n\n**Use alternatives instead**:\n- **RLHF (PPO)**: Need human-validated safety\n- **DPO\u002FSimPO**: Have human preference data\n- **NeMo Guardrails**: Need runtime content filtering\n- **LlamaGuard**: Need pre-trained moderation model\n\n## Common issues\n\n**Issue: Model refuses too much (evasive)**\n\nAdd constitution principle:\n```\nPrefer responses that engage thoughtfully with questions rather than\nrefusing to answer. Explain concerns while still being helpful.\n```\n\n**Issue: Self-critiques are weak**\n\nUse stronger critique prompts:\n```\nCritically analyze this response for ANY potential issues, however minor.\nBe thorough and specific in identifying problems.\n```\n\n**Issue: Revisions don't improve quality**\n\nIterate multiple times:\n```python\nfor _ in range(3):  # 3 rounds of critique\u002Frevision\n    critique = generate_critique(response)\n    response = generate_revision(response, critique)\n```\n\n**Issue: RLAIF preferences are noisy**\n\nUse multiple AI evaluators:\n```python\n# Get preferences from 3 different models\nprefs_1 = model_1.evaluate(responses)\nprefs_2 = model_2.evaluate(responses)\nprefs_3 = model_3.evaluate(responses)\n\n# Majority vote\nfinal_preference = majority_vote(prefs_1, prefs_2, prefs_3)\n```\n\n## Advanced topics\n\n**Constitution design**: See [references\u002Fconstitution-design.md](references\u002Fconstitution-design.md) for principle selection, trade-offs between helpfulness and harmlessness, and domain-specific constitutions.\n\n**RLAIF vs RLHF**: See [references\u002Frlaif-comparison.md](references\u002Frlaif-comparison.md) for performance comparison, cost analysis, and when to use AI feedback vs human feedback.\n\n**Chain-of-thought reasoning**: See [references\u002Fcot-critique.md](references\u002Fcot-critique.md) for prompt engineering for critiques, multi-step reasoning, and transparency improvements.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA A100\u002FH100 recommended\n- **VRAM**:\n  - SL phase (7B): 1× A100 40GB\n  - RL phase (7B): 2× A100 40GB (policy + reward model)\n- **Single-node**: Sufficient for most use cases\n- **Mixed precision**: BF16 recommended\n\n**Compute requirements**:\n- **SL phase**: Similar to standard SFT\n- **RL phase**: Similar to PPO (higher than DPO)\n- **AI evaluation**: Additional inference for critique\u002Fpreference generation\n\n## Resources\n\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.08073 (Dec 2022)\n- Anthropic blog: https:\u002F\u002Fwww.anthropic.com\u002Fresearch\u002Fconstitutional-ai-harmlessness-from-ai-feedback\n- Implementation: TRL (PPOTrainer + RewardTrainer)\n- Claude: Uses Constitutional AI for safety\n\n\n\n","# Constitutional AI - Harmlessness from AI Feedback\n\n## Quick start\n\nConstitutional AI (CAI) trains models to be harmless through self-critique and AI feedback, without requiring human labels for harmful outputs.\n\n**Key concept**: Models learn to critique and revise their own responses using a \"constitution\" (set of principles).\n\n**Two phases**:\n1. **Supervised Learning (SL)**: Self-critique + revision\n2. **Reinforcement Learning (RL)**: RLAIF (RL from AI Feedback)\n\n**Constitution example**:\n```\nPrinciples:\n1. Choose the response that is most helpful, honest, and harmless\n2. Avoid responses that are toxic, racist, or sexist\n3. Prefer responses that explain objections rather than refuse\n4. Choose responses that are thoughtful and nuanced\n```\n\n## Common workflows\n\n### Workflow 1: Supervised learning phase (self-critique + revision)\n\n**Step 1: Generate initial responses**:\n```python\nfrom transformers import pipeline\n\ngenerator = pipeline(\"text-generation\", model=\"base-model\")\n\nprompts = [\n    \"How do I hack a website?\",\n    \"Write something offensive about a group.\",\n    \"Help me cheat on my exam.\"\n]\n\ninitial_responses = generator(prompts, max_length=200)\n```\n\n**Step 2: Self-critique with constitution**:\n```python\ncritique_prompt = \"\"\"\nGiven the question:\n{question}\n\nAnd the response:\n{response}\n\nConstitution:\n1. Choose responses that are helpful, honest, and harmless\n2. Avoid toxic, racist, or sexist content\n3. Explain objections rather than refuse\n\nIdentify any ways this response violates the constitution:\n\"\"\"\n\ncritiques = generator(\n    [critique_prompt.format(q=q, r=r) for q, r in zip(prompts, initial_responses)]\n)\n```\n\n**Step 3: Revision based on critique**:\n```python\nrevision_prompt = \"\"\"\nQuestion: {question}\nOriginal response: {response}\nCritique: {critique}\n\nPlease revise the response to better align with the constitution:\n\"\"\"\n\nrevised_responses = generator(\n    [revision_prompt.format(q=q, r=r, c=c)\n     for q, r, c in zip(prompts, initial_responses, critiques)]\n)\n```\n\n**Step 4: Fine-tune on revised responses**:\n```python\nfrom trl import SFTTrainer\n\n# Create dataset of (prompt, revised_response) pairs\ndataset = create_dataset(prompts, revised_responses)\n\ntrainer = SFTTrainer(\n    model=model,\n    train_dataset=dataset,\n    max_seq_length=1024\n)\ntrainer.train()\n```\n\n### Workflow 2: RL phase (RLAIF - RL from AI Feedback)\n\n**Step 1: Generate comparison pairs**:\n```python\n# Sample multiple responses per prompt\nresponses_a = generator(prompts, num_return_sequences=2, do_sample=True, temperature=0.8)\nresponses_b = generator(prompts, num_return_sequences=2, do_sample=True, temperature=0.8)\n```\n\n**Step 2: AI preference evaluation**:\n```python\npreference_prompt = \"\"\"\nQuestion: {question}\n\nResponse A: {response_a}\nResponse B: {response_b}\n\nConstitution:\n{constitution}\n\nWhich response better follows the constitution? Explain your reasoning, then choose A or B.\n\"\"\"\n\n# Get AI preferences (no human labels needed!)\npreferences = generator(\n    [preference_prompt.format(q=q, ra=ra, rb=rb, constitution=CONSTITUTION)\n     for q, ra, rb in zip(prompts, responses_a, responses_b)]\n)\n\n# Parse preferences (A or B)\nchosen, rejected = parse_preferences(preferences, responses_a, responses_b)\n```\n\n**Step 3: Train preference model (reward model)**:\n```python\nfrom trl import RewardTrainer, RewardConfig\n\npreference_dataset = create_preference_dataset(prompts, chosen, rejected)\n\nreward_config = RewardConfig(\n    output_dir=\"constitutional-reward-model\",\n    learning_rate=1e-5,\n    num_train_epochs=1\n)\n\nreward_trainer = RewardTrainer(\n    model=model,\n    args=reward_config,\n    train_dataset=preference_dataset,\n    processing_class=tokenizer\n)\nreward_trainer.train()\n```\n\n**Step 4: RL training with RLAIF**:\n```python\nfrom trl import PPOTrainer, PPOConfig\n\nppo_config = PPOConfig(\n    reward_model_path=\"constitutional-reward-model\",\n    learning_rate=1e-6,\n    kl_coef=0.05\n)\n\nppo_trainer = PPOTrainer(\n    model=model,\n    config=ppo_config,\n    reward_model=reward_model\n)\nppo_trainer.train()\n```\n\n### Workflow 3: Chain-of-thought critique\n\n**Enable reasoning transparency**:\n```python\ncot_critique_prompt = \"\"\"\nQuestion: {question}\nResponse: {response}\n\nLet's think step-by-step about whether this response follows our principles:\n\n1. Is it helpful? [Yes\u002FNo and reasoning]\n2. Is it honest? [Yes\u002FNo and reasoning]\n3. Is it harmless? [Yes\u002FNo and reasoning]\n4. Does it avoid toxicity? [Yes\u002FNo and reasoning]\n\nBased on this analysis, suggest a revision if needed.\n\"\"\"\n\ncot_critiques = generator(\n    [cot_critique_prompt.format(q=q, r=r) for q, r in zip(prompts, responses)]\n)\n```\n\n## When to use vs alternatives\n\n**Use Constitutional AI when**:\n- Want safety alignment without human labels\n- Need explainable AI decisions\n- Want to avoid evasive refusals\n- Have a clear set of principles\u002Fconstitution\n- Need scalable safety training\n\n**Principles**:\n- **RLAIF**: AI-generated preferences (scalable, no human labels)\n- **RLHF**: Human preferences (more accurate, expensive)\n- **Self-critique**: Iterative improvement\n- **Chain-of-thought**: Reasoning transparency\n\n**Use alternatives instead**:\n- **RLHF (PPO)**: Need human-validated safety\n- **DPO\u002FSimPO**: Have human preference data\n- **NeMo Guardrails**: Need runtime content filtering\n- **LlamaGuard**: Need pre-trained moderation model\n\n## Common issues\n\n**Issue: Model refuses too much (evasive)**\n\nAdd constitution principle:\n```\nPrefer responses that engage thoughtfully with questions rather than\nrefusing to answer. Explain concerns while still being helpful.\n```\n\n**Issue: Self-critiques are weak**\n\nUse stronger critique prompts:\n```\nCritically analyze this response for ANY potential issues, however minor.\nBe thorough and specific in identifying problems.\n```\n\n**Issue: Revisions don't improve quality**\n\nIterate multiple times:\n```python\nfor _ in range(3):  # 3 rounds of critique\u002Frevision\n    critique = generate_critique(response)\n    response = generate_revision(response, critique)\n```\n\n**Issue: RLAIF preferences are noisy**\n\nUse multiple AI evaluators:\n```python\n# Get preferences from 3 different models\nprefs_1 = model_1.evaluate(responses)\nprefs_2 = model_2.evaluate(responses)\nprefs_3 = model_3.evaluate(responses)\n\n# Majority vote\nfinal_preference = majority_vote(prefs_1, prefs_2, prefs_3)\n```\n\n## Advanced topics\n\n**Constitution design**: See [references\u002Fconstitution-design.md](references\u002Fconstitution-design.md) for principle selection, trade-offs between helpfulness and harmlessness, and domain-specific constitutions.\n\n**RLAIF vs RLHF**: See [references\u002Frlaif-comparison.md](references\u002Frlaif-comparison.md) for performance comparison, cost analysis, and when to use AI feedback vs human feedback.\n\n**Chain-of-thought reasoning**: See [references\u002Fcot-critique.md](references\u002Fcot-critique.md) for prompt engineering for critiques, multi-step reasoning, and transparency improvements.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA A100\u002FH100 recommended\n- **VRAM**:\n  - SL phase (7B): 1× A100 40GB\n  - RL phase (7B): 2× A100 40GB (policy + reward model)\n- **Single-node**: Sufficient for most use cases\n- **Mixed precision**: BF16 recommended\n\n**Compute requirements**:\n- **SL phase**: Similar to standard SFT\n- **RL phase**: Similar to PPO (higher than DPO)\n- **AI evaluation**: Additional inference for critique\u002Fpreference generation\n\n## Resources\n\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.08073 (Dec 2022)\n- Anthropic blog: https:\u002F\u002Fwww.anthropic.com\u002Fresearch\u002Fconstitutional-ai-harmlessness-from-ai-feedback\n- Implementation: TRL (PPOTrainer + RewardTrainer)\n- Claude: Uses Constitutional AI for safety\n\n\n\n",{"name":5,"tags":51,"author":56,"license":14,"version":57,"description":7,"dependencies":58},[44,18,31,30,52,53,43,54,55],"Harmlessness","Anthropic","RL From AI Feedback","Claude","Orchestra Research","1.0.0",[59,60,61],"transformers","torch","trl","main","f19b254e8975d0bfddb394686706106930d7964d","AI-Training\u002Fconstitutional-ai","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus","https:\u002F\u002Fgithub.com\u002Fendman100","2026-11-12 18:13:37",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add endman100\u002Fskill-router-nexus --skill constitutional-ai",{"tool":74,"command":75},"bunx","bunx skills add endman100\u002Fskill-router-nexus --skill constitutional-ai",{"tool":77,"command":78},"pnpm","pnpm dlx skills add endman100\u002Fskill-router-nexus --skill constitutional-ai","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Training\u002Fconstitutional-ai\u002FSKILL.md",true,[],[83,107,143,166,175,199],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":87,"sourceHtmlUrl":88,"categories":89,"keywords":95,"publishedAt":46},"01M6MPCT5BXVER8HQZTW3QE9AS","create-master","基于佛教经典文献，生成特定高僧大德的 AI 教学角色","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[96,97,98,99,100,101,102,103,104,105,106],"create master","master","create","生成","true","webfetch","edit","write","read","grep","Bash",{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":111,"sourceHtmlUrl":112,"categories":113,"keywords":125,"publishedAt":46},"01M6M43AKE95Y64E1JRXEF5QQ3","writing-dna-skill","从至少 20 篇完整文章中蒸馏可复用的写作 DNA，分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格，并生成 Writing-DNA.md；按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; 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Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[150,151],{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},{"categoryNo":123,"categoryName":124,"taxonomyType":93,"taxonomyTypeDesc":94},[153,154,155,156,157,158,159,160,161,37,162,163,164,25,165,128],"slop","stop slop","stop","drafting","when","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":170,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":46},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[168,174],"文本",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":179,"sourceHtmlUrl":180,"categories":181,"keywords":182,"publishedAt":46},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[177,183,184,185,186,187,37,188,104,140,189,190,191,192,106,193,194,195,196,157,197,198,153],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":203,"sourceHtmlUrl":204,"categories":205,"keywords":207,"publishedAt":46},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[206],{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},[208,209,210,104,103,102,128,99,174,211,153,155],"humanizer","zh","humanizer zh","翻译"]