[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFM1JTA5QHNKKSWAYADCYM":3,"skill-same:01M6AFM1JTA5QHNKKSWAYADCYM":122},{"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":23,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6AFM1JTA5QHNKKSWAYADCYM","pytorch-lightning",null,"Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU\u002FTPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33418,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpytorch-lightning\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"pytorch","pytorch lightning","lightning","DeepSpeed","Scalable","Callbacks","DDP","FSDP","neural network","deep","organize","configure","data","network","Distributed Training","implement","gpu","logging","data pipelines","framework","code","training","deep learning","distributed","tpu","2026-11-10 10:12:30",false,"---\nname: pytorch-lightning\ndescription: \"Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU\u002FTPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.\"\n---\n\n# PyTorch Lightning\n\n## Overview\n\nPyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs\u002FTPUs.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Building, training, or deploying neural networks using PyTorch Lightning\n- Organizing PyTorch code into LightningModules\n- Configuring Trainers for multi-GPU\u002FTPU training\n- Implementing data pipelines with LightningDataModules\n- Working with callbacks, logging, and distributed training strategies (DDP, FSDP, DeepSpeed)\n- Structuring deep learning projects professionally\n\n## Core Capabilities\n\n### 1. LightningModule - Model Definition\n\nOrganize PyTorch models into six logical sections:\n\n1. **Initialization** - `__init__()` and `setup()`\n2. **Training Loop** - `training_step(batch, batch_idx)`\n3. **Validation Loop** - `validation_step(batch, batch_idx)`\n4. **Test Loop** - `test_step(batch, batch_idx)`\n5. **Prediction** - `predict_step(batch, batch_idx)`\n6. **Optimizer Configuration** - `configure_optimizers()`\n\n**Quick template reference:** See `scripts\u002Ftemplate_lightning_module.py` for a complete boilerplate.\n\n**Detailed documentation:** Read `references\u002Flightning_module.md` for comprehensive method documentation, hooks, properties, and best practices.\n\n### 2. Trainer - Training Automation\n\nThe Trainer automates the training loop, device management, gradient operations, and callbacks. Key features:\n\n- Multi-GPU\u002FTPU support with strategy selection (DDP, FSDP, DeepSpeed)\n- Automatic mixed precision training\n- Gradient accumulation and clipping\n- Checkpointing and early stopping\n- Progress bars and logging\n\n**Quick setup reference:** See `scripts\u002Fquick_trainer_setup.py` for common Trainer configurations.\n\n**Detailed documentation:** Read `references\u002Ftrainer.md` for all parameters, methods, and configuration options.\n\n### 3. LightningDataModule - Data Pipeline Organization\n\nEncapsulate all data processing steps in a reusable class:\n\n1. `prepare_data()` - Download and process data (single-process)\n2. `setup()` - Create datasets and apply transforms (per-GPU)\n3. `train_dataloader()` - Return training DataLoader\n4. `val_dataloader()` - Return validation DataLoader\n5. `test_dataloader()` - Return test DataLoader\n\n**Quick template reference:** See `scripts\u002Ftemplate_datamodule.py` for a complete boilerplate.\n\n**Detailed documentation:** Read `references\u002Fdata_module.md` for method details and usage patterns.\n\n### 4. Callbacks - Extensible Training Logic\n\nAdd custom functionality at specific training hooks without modifying your LightningModule. Built-in callbacks include:\n\n- **ModelCheckpoint** - Save best\u002Flatest models\n- **EarlyStopping** - Stop when metrics plateau\n- **LearningRateMonitor** - Track LR scheduler changes\n- **BatchSizeFinder** - Auto-determine optimal batch size\n\n**Detailed documentation:** Read `references\u002Fcallbacks.md` for built-in callbacks and custom callback creation.\n\n### 5. Logging - Experiment Tracking\n\nIntegrate with multiple logging platforms:\n\n- TensorBoard (default)\n- Weights & Biases (WandbLogger)\n- MLflow (MLFlowLogger)\n- Neptune (NeptuneLogger)\n- Comet (CometLogger)\n- CSV (CSVLogger)\n\nLog metrics using `self.log(\"metric_name\", value)` in any LightningModule method.\n\n**Detailed documentation:** Read `references\u002Flogging.md` for logger setup and configuration.\n\n### 6. Distributed Training - Scale to Multiple Devices\n\nChoose the right strategy based on model size:\n\n- **DDP** - For models \u003C500M parameters (ResNet, smaller transformers)\n- **FSDP** - For models 500M+ parameters (large transformers, recommended for Lightning users)\n- **DeepSpeed** - For cutting-edge features and fine-grained control\n\nConfigure with: `Trainer(strategy=\"ddp\", accelerator=\"gpu\", devices=4)`\n\n**Detailed documentation:** Read `references\u002Fdistributed_training.md` for strategy comparison and configuration.\n\n### 7. Best Practices\n\n- Device agnostic code - Use `self.device` instead of `.cuda()`\n- Hyperparameter saving - Use `self.save_hyperparameters()` in `__init__()`\n- Metric logging - Use `self.log()` for automatic aggregation across devices\n- Reproducibility - Use `seed_everything()` and `Trainer(deterministic=True)`\n- Debugging - Use `Trainer(fast_dev_run=True)` to test with 1 batch\n\n**Detailed documentation:** Read `references\u002Fbest_practices.md` for common patterns and pitfalls.\n\n## Quick Workflow\n\n1. **Define model:**\n   ```python\n   class MyModel(L.LightningModule):\n       def __init__(self):\n           super().__init__()\n           self.save_hyperparameters()\n           self.model = YourNetwork()\n\n       def training_step(self, batch, batch_idx):\n           x, y = batch\n           loss = F.cross_entropy(self.model(x), y)\n           self.log(\"train_loss\", loss)\n           return loss\n\n       def configure_optimizers(self):\n           return torch.optim.Adam(self.parameters())\n   ```\n\n2. **Prepare data:**\n   ```python\n   # Option 1: Direct DataLoaders\n   train_loader = DataLoader(train_dataset, batch_size=32)\n\n   # Option 2: LightningDataModule (recommended for reusability)\n   dm = MyDataModule(batch_size=32)\n   ```\n\n3. **Train:**\n   ```python\n   trainer = L.Trainer(max_epochs=10, accelerator=\"gpu\", devices=2)\n   trainer.fit(model, train_loader)  # or trainer.fit(model, datamodule=dm)\n   ```\n\n## Resources\n\n### scripts\u002F\nExecutable Python templates for common PyTorch Lightning patterns:\n\n- `template_lightning_module.py` - Complete LightningModule boilerplate\n- `template_datamodule.py` - Complete LightningDataModule boilerplate\n- `quick_trainer_setup.py` - Common Trainer configuration examples\n\n### references\u002F\nDetailed documentation for each PyTorch Lightning component:\n\n- `lightning_module.md` - Comprehensive LightningModule guide (methods, hooks, properties)\n- `trainer.md` - Trainer configuration and parameters\n- `data_module.md` - LightningDataModule patterns and methods\n- `callbacks.md` - Built-in and custom callbacks\n- `logging.md` - Logger integrations and usage\n- `distributed_training.md` - DDP, FSDP, DeepSpeed comparison and setup\n- `best_practices.md` - Common patterns, tips, and pitfalls\n","# PyTorch Lightning\n\n## Overview\n\nPyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs\u002FTPUs.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Building, training, or deploying neural networks using PyTorch Lightning\n- Organizing PyTorch code into LightningModules\n- Configuring Trainers for multi-GPU\u002FTPU training\n- Implementing data pipelines with LightningDataModules\n- Working with callbacks, logging, and distributed training strategies (DDP, FSDP, DeepSpeed)\n- Structuring deep learning projects professionally\n\n## Core Capabilities\n\n### 1. LightningModule - Model Definition\n\nOrganize PyTorch models into six logical sections:\n\n1. **Initialization** - `__init__()` and `setup()`\n2. **Training Loop** - `training_step(batch, batch_idx)`\n3. **Validation Loop** - `validation_step(batch, batch_idx)`\n4. **Test Loop** - `test_step(batch, batch_idx)`\n5. **Prediction** - `predict_step(batch, batch_idx)`\n6. **Optimizer Configuration** - `configure_optimizers()`\n\n**Quick template reference:** See `scripts\u002Ftemplate_lightning_module.py` for a complete boilerplate.\n\n**Detailed documentation:** Read `references\u002Flightning_module.md` for comprehensive method documentation, hooks, properties, and best practices.\n\n### 2. Trainer - Training Automation\n\nThe Trainer automates the training loop, device management, gradient operations, and callbacks. Key features:\n\n- Multi-GPU\u002FTPU support with strategy selection (DDP, FSDP, DeepSpeed)\n- Automatic mixed precision training\n- Gradient accumulation and clipping\n- Checkpointing and early stopping\n- Progress bars and logging\n\n**Quick setup reference:** See `scripts\u002Fquick_trainer_setup.py` for common Trainer configurations.\n\n**Detailed documentation:** Read `references\u002Ftrainer.md` for all parameters, methods, and configuration options.\n\n### 3. LightningDataModule - Data Pipeline Organization\n\nEncapsulate all data processing steps in a reusable class:\n\n1. `prepare_data()` - Download and process data (single-process)\n2. `setup()` - Create datasets and apply transforms (per-GPU)\n3. `train_dataloader()` - Return training DataLoader\n4. `val_dataloader()` - Return validation DataLoader\n5. `test_dataloader()` - Return test DataLoader\n\n**Quick template reference:** See `scripts\u002Ftemplate_datamodule.py` for a complete boilerplate.\n\n**Detailed documentation:** Read `references\u002Fdata_module.md` for method details and usage patterns.\n\n### 4. Callbacks - Extensible Training Logic\n\nAdd custom functionality at specific training hooks without modifying your LightningModule. Built-in callbacks include:\n\n- **ModelCheckpoint** - Save best\u002Flatest models\n- **EarlyStopping** - Stop when metrics plateau\n- **LearningRateMonitor** - Track LR scheduler changes\n- **BatchSizeFinder** - Auto-determine optimal batch size\n\n**Detailed documentation:** Read `references\u002Fcallbacks.md` for built-in callbacks and custom callback creation.\n\n### 5. Logging - Experiment Tracking\n\nIntegrate with multiple logging platforms:\n\n- TensorBoard (default)\n- Weights & Biases (WandbLogger)\n- MLflow (MLFlowLogger)\n- Neptune (NeptuneLogger)\n- Comet (CometLogger)\n- CSV (CSVLogger)\n\nLog metrics using `self.log(\"metric_name\", value)` in any LightningModule method.\n\n**Detailed documentation:** Read `references\u002Flogging.md` for logger setup and configuration.\n\n### 6. Distributed Training - Scale to Multiple Devices\n\nChoose the right strategy based on model size:\n\n- **DDP** - For models \u003C500M parameters (ResNet, smaller transformers)\n- **FSDP** - For models 500M+ parameters (large transformers, recommended for Lightning users)\n- **DeepSpeed** - For cutting-edge features and fine-grained control\n\nConfigure with: `Trainer(strategy=\"ddp\", accelerator=\"gpu\", devices=4)`\n\n**Detailed documentation:** Read `references\u002Fdistributed_training.md` for strategy comparison and configuration.\n\n### 7. Best Practices\n\n- Device agnostic code - Use `self.device` instead of `.cuda()`\n- Hyperparameter saving - Use `self.save_hyperparameters()` in `__init__()`\n- Metric logging - Use `self.log()` for automatic aggregation across devices\n- Reproducibility - Use `seed_everything()` and `Trainer(deterministic=True)`\n- Debugging - Use `Trainer(fast_dev_run=True)` to test with 1 batch\n\n**Detailed documentation:** Read `references\u002Fbest_practices.md` for common patterns and pitfalls.\n\n## Quick Workflow\n\n1. **Define model:**\n   ```python\n   class MyModel(L.LightningModule):\n       def __init__(self):\n           super().__init__()\n           self.save_hyperparameters()\n           self.model = YourNetwork()\n\n       def training_step(self, batch, batch_idx):\n           x, y = batch\n           loss = F.cross_entropy(self.model(x), y)\n           self.log(\"train_loss\", loss)\n           return loss\n\n       def configure_optimizers(self):\n           return torch.optim.Adam(self.parameters())\n   ```\n\n2. **Prepare data:**\n   ```python\n   # Option 1: Direct DataLoaders\n   train_loader = DataLoader(train_dataset, batch_size=32)\n\n   # Option 2: LightningDataModule (recommended for reusability)\n   dm = MyDataModule(batch_size=32)\n   ```\n\n3. **Train:**\n   ```python\n   trainer = L.Trainer(max_epochs=10, accelerator=\"gpu\", devices=2)\n   trainer.fit(model, train_loader)  # or trainer.fit(model, datamodule=dm)\n   ```\n\n## Resources\n\n### scripts\u002F\nExecutable Python templates for common PyTorch Lightning patterns:\n\n- `template_lightning_module.py` - Complete LightningModule boilerplate\n- `template_datamodule.py` - Complete LightningDataModule boilerplate\n- `quick_trainer_setup.py` - Common Trainer configuration examples\n\n### references\u002F\nDetailed documentation for each PyTorch Lightning component:\n\n- `lightning_module.md` - Comprehensive LightningModule guide (methods, hooks, properties)\n- `trainer.md` - Trainer configuration and parameters\n- `data_module.md` - LightningDataModule patterns and methods\n- `callbacks.md` - Built-in and custom callbacks\n- `logging.md` - Logger integrations and usage\n- `distributed_training.md` - DDP, FSDP, DeepSpeed comparison and setup\n- `best_practices.md` - Common patterns, tips, and pitfalls\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpytorch-lightning",3926,"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","2026-11-12 14:45:56",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add davila7\u002Fclaude-code-templates --skill pytorch-lightning",{"tool":70,"command":71},"bunx","bunx skills add 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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. 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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",[157],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[159,160,161,162,163,164,165,166,167,168,169,140,44,170,171,172,173,174,175,176],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","todo","work","plan","focus","signals","audit","dependency",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":192,"publishedAt":49},"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",[183,186,187],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":188,"categoryName":189,"taxonomyType":190,"taxonomyTypeDesc":191},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[193,194,195,196,197,198,199,136,200,201,202,203,204,205,206,139,207,208,140,209],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":211,"name":212,"nameZh":6,"description":213,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":214,"licenseFileUrl":215,"sourceHtmlUrl":216,"categories":217,"keywords":221,"publishedAt":49},"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",[218],{"categoryNo":219,"categoryName":220,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,136,244],"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":246,"name":247,"nameZh":6,"description":248,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":214,"licenseFileUrl":249,"sourceHtmlUrl":250,"categories":251,"keywords":256,"publishedAt":49},"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",[252,255],{"categoryNo":253,"categoryName":254,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":219,"categoryName":220,"taxonomyType":21,"taxonomyTypeDesc":22},[257,258,259,260,261,223,262,232,233,263,264,265,266,136,267,142,268],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":270,"name":271,"nameZh":6,"description":272,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":214,"licenseFileUrl":273,"sourceHtmlUrl":274,"categories":275,"keywords":283,"publishedAt":49},"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",[276,279,280],{"categoryNo":277,"categoryName":278,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":281,"categoryName":282,"taxonomyType":190,"taxonomyTypeDesc":191},"01JBSEEDCAT000000000000036","内容创作者",[284,285,286,287,288,289,36,290,225,291,235,44,292,293,294,295,296,205,297,140,298,299,300,301,302,148,303],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]