[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EKCSREQ3Y3AZXS7BRK57YV":3,"skill-same:01M6EKCSREQ3Y3AZXS7BRK57YV":102},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":50,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":53,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M6EKCSREQ3Y3AZXS7BRK57YV","stable-baselines3",null,"Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.","levalencia\u002Fagent-god-mode","levalencia","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6962857?v=4","01M6EB0FZZ4BPQPC4HET4QY6NM","Luis Valencia ","01M6EB0G4HWHPWW0H9X7QKSAHP",3,"MIT","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fstable-baselines3\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"stable baselines3","parallel","multi","prototyping","high-performance","systems","reinforcement","scikit-learn","rl","ready","use","performance","learning","api","pufferlib","PPO","vectorized","reinforcement learning","learn","training","2026-11-10 10:12:30",false,"---\nname: stable-baselines3\ndescription: Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.\nlicense: MIT license\nmetadata:\n    skill-author: K-Dense Inc.\n---\n\n# Stable Baselines3\n\n## Overview\n\nStable Baselines3 (SB3) is a PyTorch-based library providing reliable implementations of reinforcement learning algorithms. This skill provides comprehensive guidance for training RL agents, creating custom environments, implementing callbacks, and optimizing training workflows using SB3's unified API.\n\n## Core Capabilities\n\n### 1. Training RL Agents\n\n**Basic Training Pattern:**\n\n```python\nimport gymnasium as gym\nfrom stable_baselines3 import PPO\n\n# Create environment\nenv = gym.make(\"CartPole-v1\")\n\n# Initialize agent\nmodel = PPO(\"MlpPolicy\", env, verbose=1)\n\n# Train the agent\nmodel.learn(total_timesteps=10000)\n\n# Save the model\nmodel.save(\"ppo_cartpole\")\n\n# Load the model (without prior instantiation)\nmodel = PPO.load(\"ppo_cartpole\", env=env)\n```\n\n**Important Notes:**\n- `total_timesteps` is a lower bound; actual training may exceed this due to batch collection\n- Use `model.load()` as a static method, not on an existing instance\n- The replay buffer is NOT saved with the model to save space\n\n**Algorithm Selection:**\nUse `references\u002Falgorithms.md` for detailed algorithm characteristics and selection guidance. Quick reference:\n- **PPO\u002FA2C**: General-purpose, supports all action space types, good for multiprocessing\n- **SAC\u002FTD3**: Continuous control, off-policy, sample-efficient\n- **DQN**: Discrete actions, off-policy\n- **HER**: Goal-conditioned tasks\n\nSee `scripts\u002Ftrain_rl_agent.py` for a complete training template with best practices.\n\n### 2. Custom Environments\n\n**Requirements:**\nCustom environments must inherit from `gymnasium.Env` and implement:\n- `__init__()`: Define action_space and observation_space\n- `reset(seed, options)`: Return initial observation and info dict\n- `step(action)`: Return observation, reward, terminated, truncated, info\n- `render()`: Visualization (optional)\n- `close()`: Cleanup resources\n\n**Key Constraints:**\n- Image observations must be `np.uint8` in range [0, 255]\n- Use channel-first format when possible (channels, height, width)\n- SB3 normalizes images automatically by dividing by 255\n- Set `normalize_images=False` in policy_kwargs if pre-normalized\n- SB3 does NOT support `Discrete` or `MultiDiscrete` spaces with `start!=0`\n\n**Validation:**\n```python\nfrom stable_baselines3.common.env_checker import check_env\n\ncheck_env(env, warn=True)\n```\n\nSee `scripts\u002Fcustom_env_template.py` for a complete custom environment template and `references\u002Fcustom_environments.md` for comprehensive guidance.\n\n### 3. Vectorized Environments\n\n**Purpose:**\nVectorized environments run multiple environment instances in parallel, accelerating training and enabling certain wrappers (frame-stacking, normalization).\n\n**Types:**\n- **DummyVecEnv**: Sequential execution on current process (for lightweight environments)\n- **SubprocVecEnv**: Parallel execution across processes (for compute-heavy environments)\n\n**Quick Setup:**\n```python\nfrom stable_baselines3.common.env_util import make_vec_env\n\n# Create 4 parallel environments\nenv = make_vec_env(\"CartPole-v1\", n_envs=4, vec_env_cls=SubprocVecEnv)\n\nmodel = PPO(\"MlpPolicy\", env, verbose=1)\nmodel.learn(total_timesteps=25000)\n```\n\n**Off-Policy Optimization:**\nWhen using multiple environments with off-policy algorithms (SAC, TD3, DQN), set `gradient_steps=-1` to perform one gradient update per environment step, balancing wall-clock time and sample efficiency.\n\n**API Differences:**\n- `reset()` returns only observations (info available in `vec_env.reset_infos`)\n- `step()` returns 4-tuple: `(obs, rewards, dones, infos)` not 5-tuple\n- Environments auto-reset after episodes\n- Terminal observations available via `infos[env_idx][\"terminal_observation\"]`\n\nSee `references\u002Fvectorized_envs.md` for detailed information on wrappers and advanced usage.\n\n### 4. Callbacks for Monitoring and Control\n\n**Purpose:**\nCallbacks enable monitoring metrics, saving checkpoints, implementing early stopping, and custom training logic without modifying core algorithms.\n\n**Common Callbacks:**\n- **EvalCallback**: Evaluate periodically and save best model\n- **CheckpointCallback**: Save model checkpoints at intervals\n- **StopTrainingOnRewardThreshold**: Stop when target reward reached\n- **ProgressBarCallback**: Display training progress with timing\n\n**Custom Callback Structure:**\n```python\nfrom stable_baselines3.common.callbacks import BaseCallback\n\nclass CustomCallback(BaseCallback):\n    def _on_training_start(self):\n        # Called before first rollout\n        pass\n\n    def _on_step(self):\n        # Called after each environment step\n        # Return False to stop training\n        return True\n\n    def _on_rollout_end(self):\n        # Called at end of rollout\n        pass\n```\n\n**Available Attributes:**\n- `self.model`: The RL algorithm instance\n- `self.num_timesteps`: Total environment steps\n- `self.training_env`: The training environment\n\n**Chaining Callbacks:**\n```python\nfrom stable_baselines3.common.callbacks import CallbackList\n\ncallback = CallbackList([eval_callback, checkpoint_callback, custom_callback])\nmodel.learn(total_timesteps=10000, callback=callback)\n```\n\nSee `references\u002Fcallbacks.md` for comprehensive callback documentation.\n\n### 5. Model Persistence and Inspection\n\n**Saving and Loading:**\n```python\n# Save model\nmodel.save(\"model_name\")\n\n# Save normalization statistics (if using VecNormalize)\nvec_env.save(\"vec_normalize.pkl\")\n\n# Load model\nmodel = PPO.load(\"model_name\", env=env)\n\n# Load normalization statistics\nvec_env = VecNormalize.load(\"vec_normalize.pkl\", vec_env)\n```\n\n**Parameter Access:**\n```python\n# Get parameters\nparams = model.get_parameters()\n\n# Set parameters\nmodel.set_parameters(params)\n\n# Access PyTorch state dict\nstate_dict = model.policy.state_dict()\n```\n\n### 6. Evaluation and Recording\n\n**Evaluation:**\n```python\nfrom stable_baselines3.common.evaluation import evaluate_policy\n\nmean_reward, std_reward = evaluate_policy(\n    model,\n    env,\n    n_eval_episodes=10,\n    deterministic=True\n)\n```\n\n**Video Recording:**\n```python\nfrom stable_baselines3.common.vec_env import VecVideoRecorder\n\n# Wrap environment with video recorder\nenv = VecVideoRecorder(\n    env,\n    \"videos\u002F\",\n    record_video_trigger=lambda x: x % 2000 == 0,\n    video_length=200\n)\n```\n\nSee `scripts\u002Fevaluate_agent.py` for a complete evaluation and recording template.\n\n### 7. Advanced Features\n\n**Learning Rate Schedules:**\n```python\ndef linear_schedule(initial_value):\n    def func(progress_remaining):\n        # progress_remaining goes from 1 to 0\n        return progress_remaining * initial_value\n    return func\n\nmodel = PPO(\"MlpPolicy\", env, learning_rate=linear_schedule(0.001))\n```\n\n**Multi-Input Policies (Dict Observations):**\n```python\nmodel = PPO(\"MultiInputPolicy\", env, verbose=1)\n```\nUse when observations are dictionaries (e.g., combining images with sensor data).\n\n**Hindsight Experience Replay:**\n```python\nfrom stable_baselines3 import SAC, HerReplayBuffer\n\nmodel = SAC(\n    \"MultiInputPolicy\",\n    env,\n    replay_buffer_class=HerReplayBuffer,\n    replay_buffer_kwargs=dict(\n        n_sampled_goal=4,\n        goal_selection_strategy=\"future\",\n    ),\n)\n```\n\n**TensorBoard Integration:**\n```python\nmodel = PPO(\"MlpPolicy\", env, tensorboard_log=\".\u002Ftensorboard\u002F\")\nmodel.learn(total_timesteps=10000)\n```\n\n## Workflow Guidance\n\n**Starting a New RL Project:**\n\n1. **Define the problem**: Identify observation space, action space, and reward structure\n2. **Choose algorithm**: Use `references\u002Falgorithms.md` for selection guidance\n3. **Create\u002Fadapt environment**: Use `scripts\u002Fcustom_env_template.py` if needed\n4. **Validate environment**: Always run `check_env()` before training\n5. **Set up training**: Use `scripts\u002Ftrain_rl_agent.py` as starting template\n6. **Add monitoring**: Implement callbacks for evaluation and checkpointing\n7. **Optimize performance**: Consider vectorized environments for speed\n8. **Evaluate and iterate**: Use `scripts\u002Fevaluate_agent.py` for assessment\n\n**Common Issues:**\n\n- **Memory errors**: Reduce `buffer_size` for off-policy algorithms or use fewer parallel environments\n- **Slow training**: Consider SubprocVecEnv for parallel environments\n- **Unstable training**: Try different algorithms, tune hyperparameters, or check reward scaling\n- **Import errors**: Ensure `stable_baselines3` is installed: `uv pip install stable-baselines3[extra]`\n\n## Resources\n\n### scripts\u002F\n- `train_rl_agent.py`: Complete training script template with best practices\n- `evaluate_agent.py`: Agent evaluation and video recording template\n- `custom_env_template.py`: Custom Gym environment template\n\n### references\u002F\n- `algorithms.md`: Detailed algorithm comparison and selection guide\n- `custom_environments.md`: Comprehensive custom environment creation guide\n- `callbacks.md`: Complete callback system reference\n- `vectorized_envs.md`: Vectorized environment usage and wrappers\n\n## Installation\n\n```bash\n# Basic installation\nuv pip install stable-baselines3\n\n# With extra dependencies (Tensorboard, etc.)\nuv pip install stable-baselines3[extra]\n```\n\n","# Stable Baselines3\n\n## Overview\n\nStable Baselines3 (SB3) is a PyTorch-based library providing reliable implementations of reinforcement learning algorithms. This skill provides comprehensive guidance for training RL agents, creating custom environments, implementing callbacks, and optimizing training workflows using SB3's unified API.\n\n## Core Capabilities\n\n### 1. Training RL Agents\n\n**Basic Training Pattern:**\n\n```python\nimport gymnasium as gym\nfrom stable_baselines3 import PPO\n\n# Create environment\nenv = gym.make(\"CartPole-v1\")\n\n# Initialize agent\nmodel = PPO(\"MlpPolicy\", env, verbose=1)\n\n# Train the agent\nmodel.learn(total_timesteps=10000)\n\n# Save the model\nmodel.save(\"ppo_cartpole\")\n\n# Load the model (without prior instantiation)\nmodel = PPO.load(\"ppo_cartpole\", env=env)\n```\n\n**Important Notes:**\n- `total_timesteps` is a lower bound; actual training may exceed this due to batch collection\n- Use `model.load()` as a static method, not on an existing instance\n- The replay buffer is NOT saved with the model to save space\n\n**Algorithm Selection:**\nUse `references\u002Falgorithms.md` for detailed algorithm characteristics and selection guidance. Quick reference:\n- **PPO\u002FA2C**: General-purpose, supports all action space types, good for multiprocessing\n- **SAC\u002FTD3**: Continuous control, off-policy, sample-efficient\n- **DQN**: Discrete actions, off-policy\n- **HER**: Goal-conditioned tasks\n\nSee `scripts\u002Ftrain_rl_agent.py` for a complete training template with best practices.\n\n### 2. Custom Environments\n\n**Requirements:**\nCustom environments must inherit from `gymnasium.Env` and implement:\n- `__init__()`: Define action_space and observation_space\n- `reset(seed, options)`: Return initial observation and info dict\n- `step(action)`: Return observation, reward, terminated, truncated, info\n- `render()`: Visualization (optional)\n- `close()`: Cleanup resources\n\n**Key Constraints:**\n- Image observations must be `np.uint8` in range [0, 255]\n- Use channel-first format when possible (channels, height, width)\n- SB3 normalizes images automatically by dividing by 255\n- Set `normalize_images=False` in policy_kwargs if pre-normalized\n- SB3 does NOT support `Discrete` or `MultiDiscrete` spaces with `start!=0`\n\n**Validation:**\n```python\nfrom stable_baselines3.common.env_checker import check_env\n\ncheck_env(env, warn=True)\n```\n\nSee `scripts\u002Fcustom_env_template.py` for a complete custom environment template and `references\u002Fcustom_environments.md` for comprehensive guidance.\n\n### 3. Vectorized Environments\n\n**Purpose:**\nVectorized environments run multiple environment instances in parallel, accelerating training and enabling certain wrappers (frame-stacking, normalization).\n\n**Types:**\n- **DummyVecEnv**: Sequential execution on current process (for lightweight environments)\n- **SubprocVecEnv**: Parallel execution across processes (for compute-heavy environments)\n\n**Quick Setup:**\n```python\nfrom stable_baselines3.common.env_util import make_vec_env\n\n# Create 4 parallel environments\nenv = make_vec_env(\"CartPole-v1\", n_envs=4, vec_env_cls=SubprocVecEnv)\n\nmodel = PPO(\"MlpPolicy\", env, verbose=1)\nmodel.learn(total_timesteps=25000)\n```\n\n**Off-Policy Optimization:**\nWhen using multiple environments with off-policy algorithms (SAC, TD3, DQN), set `gradient_steps=-1` to perform one gradient update per environment step, balancing wall-clock time and sample efficiency.\n\n**API Differences:**\n- `reset()` returns only observations (info available in `vec_env.reset_infos`)\n- `step()` returns 4-tuple: `(obs, rewards, dones, infos)` not 5-tuple\n- Environments auto-reset after episodes\n- Terminal observations available via `infos[env_idx][\"terminal_observation\"]`\n\nSee `references\u002Fvectorized_envs.md` for detailed information on wrappers and advanced usage.\n\n### 4. Callbacks for Monitoring and Control\n\n**Purpose:**\nCallbacks enable monitoring metrics, saving checkpoints, implementing early stopping, and custom training logic without modifying core algorithms.\n\n**Common Callbacks:**\n- **EvalCallback**: Evaluate periodically and save best model\n- **CheckpointCallback**: Save model checkpoints at intervals\n- **StopTrainingOnRewardThreshold**: Stop when target reward reached\n- **ProgressBarCallback**: Display training progress with timing\n\n**Custom Callback Structure:**\n```python\nfrom stable_baselines3.common.callbacks import BaseCallback\n\nclass CustomCallback(BaseCallback):\n    def _on_training_start(self):\n        # Called before first rollout\n        pass\n\n    def _on_step(self):\n        # Called after each environment step\n        # Return False to stop training\n        return True\n\n    def _on_rollout_end(self):\n        # Called at end of rollout\n        pass\n```\n\n**Available Attributes:**\n- `self.model`: The RL algorithm instance\n- `self.num_timesteps`: Total environment steps\n- `self.training_env`: The training environment\n\n**Chaining Callbacks:**\n```python\nfrom stable_baselines3.common.callbacks import CallbackList\n\ncallback = CallbackList([eval_callback, checkpoint_callback, custom_callback])\nmodel.learn(total_timesteps=10000, callback=callback)\n```\n\nSee `references\u002Fcallbacks.md` for comprehensive callback documentation.\n\n### 5. Model Persistence and Inspection\n\n**Saving and Loading:**\n```python\n# Save model\nmodel.save(\"model_name\")\n\n# Save normalization statistics (if using VecNormalize)\nvec_env.save(\"vec_normalize.pkl\")\n\n# Load model\nmodel = PPO.load(\"model_name\", env=env)\n\n# Load normalization statistics\nvec_env = VecNormalize.load(\"vec_normalize.pkl\", vec_env)\n```\n\n**Parameter Access:**\n```python\n# Get parameters\nparams = model.get_parameters()\n\n# Set parameters\nmodel.set_parameters(params)\n\n# Access PyTorch state dict\nstate_dict = model.policy.state_dict()\n```\n\n### 6. Evaluation and Recording\n\n**Evaluation:**\n```python\nfrom stable_baselines3.common.evaluation import evaluate_policy\n\nmean_reward, std_reward = evaluate_policy(\n    model,\n    env,\n    n_eval_episodes=10,\n    deterministic=True\n)\n```\n\n**Video Recording:**\n```python\nfrom stable_baselines3.common.vec_env import VecVideoRecorder\n\n# Wrap environment with video recorder\nenv = VecVideoRecorder(\n    env,\n    \"videos\u002F\",\n    record_video_trigger=lambda x: x % 2000 == 0,\n    video_length=200\n)\n```\n\nSee `scripts\u002Fevaluate_agent.py` for a complete evaluation and recording template.\n\n### 7. Advanced Features\n\n**Learning Rate Schedules:**\n```python\ndef linear_schedule(initial_value):\n    def func(progress_remaining):\n        # progress_remaining goes from 1 to 0\n        return progress_remaining * initial_value\n    return func\n\nmodel = PPO(\"MlpPolicy\", env, learning_rate=linear_schedule(0.001))\n```\n\n**Multi-Input Policies (Dict Observations):**\n```python\nmodel = PPO(\"MultiInputPolicy\", env, verbose=1)\n```\nUse when observations are dictionaries (e.g., combining images with sensor data).\n\n**Hindsight Experience Replay:**\n```python\nfrom stable_baselines3 import SAC, HerReplayBuffer\n\nmodel = SAC(\n    \"MultiInputPolicy\",\n    env,\n    replay_buffer_class=HerReplayBuffer,\n    replay_buffer_kwargs=dict(\n        n_sampled_goal=4,\n        goal_selection_strategy=\"future\",\n    ),\n)\n```\n\n**TensorBoard Integration:**\n```python\nmodel = PPO(\"MlpPolicy\", env, tensorboard_log=\".\u002Ftensorboard\u002F\")\nmodel.learn(total_timesteps=10000)\n```\n\n## Workflow Guidance\n\n**Starting a New RL Project:**\n\n1. **Define the problem**: Identify observation space, action space, and reward structure\n2. **Choose algorithm**: Use `references\u002Falgorithms.md` for selection guidance\n3. **Create\u002Fadapt environment**: Use `scripts\u002Fcustom_env_template.py` if needed\n4. **Validate environment**: Always run `check_env()` before training\n5. **Set up training**: Use `scripts\u002Ftrain_rl_agent.py` as starting template\n6. **Add monitoring**: Implement callbacks for evaluation and checkpointing\n7. **Optimize performance**: Consider vectorized environments for speed\n8. **Evaluate and iterate**: Use `scripts\u002Fevaluate_agent.py` for assessment\n\n**Common Issues:**\n\n- **Memory errors**: Reduce `buffer_size` for off-policy algorithms or use fewer parallel environments\n- **Slow training**: Consider SubprocVecEnv for parallel environments\n- **Unstable training**: Try different algorithms, tune hyperparameters, or check reward scaling\n- **Import errors**: Ensure `stable_baselines3` is installed: `uv pip install stable-baselines3[extra]`\n\n## Resources\n\n### scripts\u002F\n- `train_rl_agent.py`: Complete training script template with best practices\n- `evaluate_agent.py`: Agent evaluation and video recording template\n- `custom_env_template.py`: Custom Gym environment template\n\n### references\u002F\n- `algorithms.md`: Detailed algorithm comparison and selection guide\n- `custom_environments.md`: Comprehensive custom environment creation guide\n- `callbacks.md`: Complete callback system reference\n- `vectorized_envs.md`: Vectorized environment usage and wrappers\n\n## Installation\n\n```bash\n# Basic installation\nuv pip install stable-baselines3\n\n# With extra dependencies (Tensorboard, etc.)\nuv pip install stable-baselines3[extra]\n```\n\n",{"name":5,"license":44,"metadata":45,"description":7},"MIT license",{"skill-author":46},"K-Dense Inc.","main","fa638b97a18ff6d9f26cf0eaf3dfa123531bcc87","organized-skills\u002Fstable-baselines3",1,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode","https:\u002F\u002Fgithub.com\u002Flevalencia","https:\u002F\u002Fmedium.com\u002F@luisevalencia","Microsoft MVP - 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Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzarr-python\u002FSKILL.md",[],[110,111,112,113,114,115,116,117,20,118,119,120,121,122,123],"python","zarr python","storage","integration","s3","gcs","cloud","dask","pipelines","arrays","numpy","scientific computing","scale","scientific",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":143,"publishedAt":39},"01M6EM8AR6ZFKZ9YVW8TTG850Z","zabbix","Zabbix monitoring system automation via API and Python. Use when: (1) Managing hosts, templates, items, triggers, or host groups, (2) Automating monitoring configuration, (3) Sending data via Zabbix trapper\u002Fsender, (4) Querying historical data or events, (5) Bulk operations on Zabbix objects, (6) Maintenance window management, (7) User\u002Fpermission management","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzabbix-api-skill\u002FSKILL.md",[130,135,138],{"categoryNo":131,"categoryName":132,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":136,"categoryName":137,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":139,"categoryName":140,"taxonomyType":141,"taxonomyTypeDesc":142},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[126,110,144,145,146,147,148,32,149,150,29,151,152,153,154,155,156,157,158,159],"automation","window management","monitoring","data","templates","system","operations","maintenance","objects","permission","bulk operations","triggers","host","events","via","bulk",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":39},"01M6EM6Q9JG846ECQY9A210A26","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fx-twitter-growth\u002FSKILL.md",[],[167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186],"twitter","x twitter growth","growth","competitive intelligence","search","analyzing","plan","marketing","web search","competitors","analyze","write","optimize","social","intelligence","viral","growth engine","mechanics","grow","competitive",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":193,"publishedAt":39},"01M6EM3FQNPYNKPC4CV9TCVNGD","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fwhat-if-oracle\u002FSKILL.md",[],[194,195,196,197,198,199,200,201,202,203,204,205,29,206,207,208,209,210,211,21],"oracle","what if oracle","test","planning","ux","futures","risk","analysis","explore","decision","branch","question","exploration","options","stress-test","strategies","trigger","case",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":221,"publishedAt":39},"01M6EM08C06RGDYCJ86HAPK0FF","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvercel-react-best-practices\u002FSKILL.md",[218],{"categoryNo":219,"categoryName":220,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000009","前端开发",[222,223,224,225,226,227,30,228,229,230,231,232,233,147,234,235,236,237,238,155],"react","vercel react best practices","best practices","react best practices","vercel","writing","components","react components","guidelines","tasks","code","Optimization","Next.js","refactoring","performance optimization","next","pages",{"skillNo":240,"name":241,"nameZh":6,"description":242,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":243,"categories":244,"keywords":255,"publishedAt":39},"01M6EM00S13326QM08DTM94J6Z","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvenue-templates\u002FSKILL.md",[245,248,251,252],{"categoryNo":246,"categoryName":247,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":249,"categoryName":250,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":136,"categoryName":137,"taxonomyType":133,"taxonomyTypeDesc":134},{"categoryNo":253,"categoryName":254,"taxonomyType":141,"taxonomyTypeDesc":142},"01JBSEEDCAT000000000000036","内容创作者",[148,256,257,230,258,259,260,261,262,263,264,178,265,266,267,268,269,270,123,271],"venue templates","research","NeurIPS","ICML","LaTeX","science","Bash","access","read","edit","proposals","Academic","requirements","formatting","journal","chi"]