[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AF7M7PTH00RTH9S2B2NNYX":3,"skill-same:01M6AF7M7PTH00RTH9S2B2NNYX":81},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":48,"repositoryHtmlUrl":49,"repositoryHomepageUrl":50,"ownerHtmlUrl":51,"ownerBlogUrl":52,"ownerDescription":53,"updatedAt":54,"install":55,"resources":68},"01M6AF7M7PTH00RTH9S2B2NNYX","nowait",null,"Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on \"optimize reasoning\", \"reduce thinking tokens\", \"efficient inference\", \"suppress reflection tokens\", or when working with verbose CoT outputs.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnowait\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"triggers","when","outputs","accuracy","reduce","for","usage","cot","Efficient Inference","chain-of-thought","chain","llms","tokens","token","deepseek","optimize","thinking","reflection","use","reasoning","inference","2026-11-10 10:12:30",false,"---\nname: nowait\ndescription: Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on \"optimize reasoning\", \"reduce thinking tokens\", \"efficient inference\", \"suppress reflection tokens\", or when working with verbose CoT outputs.\n---\n\n# NOWAIT Reasoning Optimizer\n\nImplements the NOWAIT technique from the paper \"Wait, We Don't Need to 'Wait'! Removing Thinking Tokens Improves Reasoning Efficiency\" (Wang et al., 2025).\n\n## Overview\n\nNOWAIT is a training-free inference-time intervention that suppresses self-reflection tokens (e.g., \"Wait\", \"Hmm\", \"Alternatively\") during generation, reducing chain-of-thought (CoT) trajectory length by **27-51%** without compromising model utility.\n\n## When to Use\n\n- Deploying R1-style reasoning models with limited compute\n- Reducing inference latency for production systems\n- Optimizing token costs for reasoning tasks\n- Working with verbose CoT outputs that need streamlining\n\n## Supported Models\n\n| Model Series | Type | Token Reduction |\n|--------------|------|-----------------|\n| QwQ-32B | RL-based | 16-31% |\n| Phi4-Reasoning-Plus | RL-based | 23-28% |\n| Qwen3-32B | RL-based | 13-16% |\n| Kimi-VL-A3B | Multimodal | 40-60% |\n| QvQ-72B-Preview | Multimodal | 20-30% |\n\n**Important**: NOWAIT works best with RL-based models. Distilled models (Qwen3-4B\u002F8B\u002F14B) show degraded performance when reflection tokens are suppressed.\n\n## Quick Start\n\n### 1. Basic Implementation\n\n```python\nfrom scripts.nowait_processor import NOWAITLogitProcessor\n\n# Initialize processor for your model's tokenizer\nprocessor = NOWAITLogitProcessor(tokenizer)\n\n# Use during generation\noutputs = model.generate(\n    inputs,\n    logits_processor=[processor],\n    max_new_tokens=32768\n)\n```\n\n### 2. Keywords Suppressed\n\nSee `references\u002Fkeywords.md` for the complete list. Core keywords:\n\n```\nwait, alternatively, hmm, but, however, check, \ndouble-check, maybe, verify, again, oh, ah\n```\n\n## How It Works\n\n1. **Initialize Keywords**: Identify reflection keywords from empirical analysis\n2. **Expand to Token Variants**: Map keywords to all token variants in vocabulary (e.g., \"wait\" → \" wait\", \"Wait\", \" Wait\", \".wait\", \"WAIT\")\n3. **Suppress During Inference**: Set logits of reflection tokens to large negative values during decoding\n\n```\nLogits (Before)         Logits (After)\nWait     0.8     →     Wait     -inf\nFirst    0.6     →     First    0.6\nHmm      0.5     →     Hmm      -inf\nLet      0.4     →     Let      0.4\n```\n\n## Key Findings\n\n### Why It Works\n\n- NOWAIT doesn't eliminate self-reflection entirely—it guides models to skip **unnecessary** \"waiting\" reasoning\n- Models still perform essential verification at key decision points\n- Results in more linear, straightforward reasoning paths\n\n### RL vs Distilled Models\n\n| Model Type | NOWAIT Effect | Recommendation |\n|------------|---------------|----------------|\n| RL-based (QwQ, Phi4, Qwen3-32B) | Stable accuracy, significant token reduction | ✅ Recommended |\n| Distilled (Qwen3-4B\u002F8B\u002F14B) | Accuracy degradation on hard tasks | ⚠️ Use with caution |\n\nDistilled models rely heavily on CoT structure from training data—removing reflection tokens disrupts their reasoning patterns.\n\n## Integration Examples\n\n### HuggingFace Transformers\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom scripts.nowait_processor import NOWAITLogitProcessor\n\nmodel = AutoModelForCausalLM.from_pretrained(\"Qwen\u002FQwQ-32B\")\ntokenizer = AutoTokenizer.from_pretrained(\"Qwen\u002FQwQ-32B\")\n\nprocessor = NOWAITLogitProcessor(tokenizer)\n\nresponse = model.generate(\n    tokenizer(prompt, return_tensors=\"pt\").input_ids,\n    logits_processor=[processor],\n    max_new_tokens=32768,\n    do_sample=True,\n    temperature=0.7\n)\n```\n\n### vLLM\n\n```python\nfrom vllm import LLM, SamplingParams\nfrom scripts.nowait_processor import get_nowait_bad_words_ids\n\nllm = LLM(model=\"Qwen\u002FQwQ-32B\")\nbad_words_ids = get_nowait_bad_words_ids(llm.get_tokenizer())\n\nsampling_params = SamplingParams(\n    max_tokens=32768,\n    bad_words_ids=bad_words_ids\n)\n```\n\n## Expected Results\n\n| Task Type | Original Tokens | NOWAIT Tokens | Reduction |\n|-----------|-----------------|---------------|-----------|\n| Math (AIME) | 15,000 | 10,500 | 30% |\n| Visual QA (MMMU) | 2,900 | 1,450 | 50% |\n| Video QA (MMVU) | 1,700 | 1,250 | 27% |\n\n## Limitations\n\n- Less effective on very simple problems where CoT overhead is already minimal\n- Distilled models may suffer accuracy loss on challenging tasks\n- Some domains may require model-specific keyword tuning\n\n## References\n\n- Paper: arXiv:2506.08343v2\n- Complete keyword list: `references\u002Fkeywords.md`\n- Implementation: `scripts\u002Fnowait_processor.py`","# NOWAIT Reasoning Optimizer\n\nImplements the NOWAIT technique from the paper \"Wait, We Don't Need to 'Wait'! Removing Thinking Tokens Improves Reasoning Efficiency\" (Wang et al., 2025).\n\n## Overview\n\nNOWAIT is a training-free inference-time intervention that suppresses self-reflection tokens (e.g., \"Wait\", \"Hmm\", \"Alternatively\") during generation, reducing chain-of-thought (CoT) trajectory length by **27-51%** without compromising model utility.\n\n## When to Use\n\n- Deploying R1-style reasoning models with limited compute\n- Reducing inference latency for production systems\n- Optimizing token costs for reasoning tasks\n- Working with verbose CoT outputs that need streamlining\n\n## Supported Models\n\n| Model Series | Type | Token Reduction |\n|--------------|------|-----------------|\n| QwQ-32B | RL-based | 16-31% |\n| Phi4-Reasoning-Plus | RL-based | 23-28% |\n| Qwen3-32B | RL-based | 13-16% |\n| Kimi-VL-A3B | Multimodal | 40-60% |\n| QvQ-72B-Preview | Multimodal | 20-30% |\n\n**Important**: NOWAIT works best with RL-based models. Distilled models (Qwen3-4B\u002F8B\u002F14B) show degraded performance when reflection tokens are suppressed.\n\n## Quick Start\n\n### 1. Basic Implementation\n\n```python\nfrom scripts.nowait_processor import NOWAITLogitProcessor\n\n# Initialize processor for your model's tokenizer\nprocessor = NOWAITLogitProcessor(tokenizer)\n\n# Use during generation\noutputs = model.generate(\n    inputs,\n    logits_processor=[processor],\n    max_new_tokens=32768\n)\n```\n\n### 2. Keywords Suppressed\n\nSee `references\u002Fkeywords.md` for the complete list. Core keywords:\n\n```\nwait, alternatively, hmm, but, however, check, \ndouble-check, maybe, verify, again, oh, ah\n```\n\n## How It Works\n\n1. **Initialize Keywords**: Identify reflection keywords from empirical analysis\n2. **Expand to Token Variants**: Map keywords to all token variants in vocabulary (e.g., \"wait\" → \" wait\", \"Wait\", \" Wait\", \".wait\", \"WAIT\")\n3. **Suppress During Inference**: Set logits of reflection tokens to large negative values during decoding\n\n```\nLogits (Before)         Logits (After)\nWait     0.8     →     Wait     -inf\nFirst    0.6     →     First    0.6\nHmm      0.5     →     Hmm      -inf\nLet      0.4     →     Let      0.4\n```\n\n## Key Findings\n\n### Why It Works\n\n- NOWAIT doesn't eliminate self-reflection entirely—it guides models to skip **unnecessary** \"waiting\" reasoning\n- Models still perform essential verification at key decision points\n- Results in more linear, straightforward reasoning paths\n\n### RL vs Distilled Models\n\n| Model Type | NOWAIT Effect | Recommendation |\n|------------|---------------|----------------|\n| RL-based (QwQ, Phi4, Qwen3-32B) | Stable accuracy, significant token reduction | ✅ Recommended |\n| Distilled (Qwen3-4B\u002F8B\u002F14B) | Accuracy degradation on hard tasks | ⚠️ Use with caution |\n\nDistilled models rely heavily on CoT structure from training data—removing reflection tokens disrupts their reasoning patterns.\n\n## Integration Examples\n\n### HuggingFace Transformers\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom scripts.nowait_processor import NOWAITLogitProcessor\n\nmodel = AutoModelForCausalLM.from_pretrained(\"Qwen\u002FQwQ-32B\")\ntokenizer = AutoTokenizer.from_pretrained(\"Qwen\u002FQwQ-32B\")\n\nprocessor = NOWAITLogitProcessor(tokenizer)\n\nresponse = model.generate(\n    tokenizer(prompt, return_tensors=\"pt\").input_ids,\n    logits_processor=[processor],\n    max_new_tokens=32768,\n    do_sample=True,\n    temperature=0.7\n)\n```\n\n### vLLM\n\n```python\nfrom vllm import LLM, SamplingParams\nfrom scripts.nowait_processor import get_nowait_bad_words_ids\n\nllm = LLM(model=\"Qwen\u002FQwQ-32B\")\nbad_words_ids = get_nowait_bad_words_ids(llm.get_tokenizer())\n\nsampling_params = SamplingParams(\n    max_tokens=32768,\n    bad_words_ids=bad_words_ids\n)\n```\n\n## Expected Results\n\n| Task Type | Original Tokens | NOWAIT Tokens | Reduction |\n|-----------|-----------------|---------------|-----------|\n| Math (AIME) | 15,000 | 10,500 | 30% |\n| Visual QA (MMMU) | 2,900 | 1,450 | 50% |\n| Video QA (MMVU) | 1,700 | 1,250 | 27% |\n\n## Limitations\n\n- Less effective on very simple problems where CoT overhead is already minimal\n- Distilled models may suffer accuracy loss on challenging tasks\n- Some domains may require model-specific keyword tuning\n\n## References\n\n- Paper: arXiv:2506.08343v2\n- Complete keyword list: `references\u002Fkeywords.md`\n- Implementation: `scripts\u002Fnowait_processor.py`",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnowait",3911,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add davila7\u002Fclaude-code-templates --skill nowait",{"tool":61,"command":62},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill nowait",{"tool":64,"command":65},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill nowait","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnowait\u002FSKILL.md",true,[69,75],{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"refrences\u002Fkeywords.md","REFERENCE","参考文档",4115,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnowait\u002Frefrences\u002Fkeywords.md",{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"scripts\u002Fnowait_processor.py","SCRIPT","脚本",10186,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnowait\u002Fscripts\u002Fnowait_processor.py",[82,110,137,170,205,229],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":93,"publishedAt":40},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[88],{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[94,95,96,97,98,24,99,100,37,20,23,101,102,103,104,105,106,107,108,109],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":117,"publishedAt":40},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[116],{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},[118,119,120,121,122,123,124,125,126,127,128,20,129,130,131,132,133,134,135,136],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":152,"publishedAt":40},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[143,146,147],{"categoryNo":144,"categoryName":145,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},{"categoryNo":148,"categoryName":149,"taxonomyType":150,"taxonomyTypeDesc":151},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[153,154,155,156,157,158,159,24,160,161,162,163,164,165,166,37,167,168,20,169],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":174,"licenseFileUrl":175,"sourceHtmlUrl":176,"categories":177,"keywords":181,"publishedAt":40},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[178],{"categoryNo":179,"categoryName":180,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000009","前端开发",[182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,24,204],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":174,"licenseFileUrl":209,"sourceHtmlUrl":210,"categories":211,"keywords":216,"publishedAt":40},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[212,215],{"categoryNo":213,"categoryName":214,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":179,"categoryName":180,"taxonomyType":91,"taxonomyTypeDesc":92},[217,218,219,220,221,183,222,192,193,223,224,225,226,24,227,101,228],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":174,"licenseFileUrl":233,"sourceHtmlUrl":234,"categories":235,"keywords":243,"publishedAt":40},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[236,239,240],{"categoryNo":237,"categoryName":238,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},{"categoryNo":241,"categoryName":242,"taxonomyType":150,"taxonomyTypeDesc":151},"01JBSEEDCAT000000000000036","内容创作者",[244,245,246,247,248,38,249,250,185,251,195,129,252,253,254,255,256,165,257,20,258,259,260,261,262,107,263],"discernment nudge","nudge","interpretation","help","run","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]