TuYv/ccpm - 第 110 页

feishu-bitable

飞书多维表格（Bitable）的创建、查询、编辑和管理工具。包含 27 种字段类型支持、高级筛选、批量操作和视图管理。 **当以下情况时使用此 Skill**： (1) 需要创建或管理飞书多维表格 App (2) 需要在多维表格中新增、查询、修改、删除记录（行数据） (3) 需要管理字段（列）、视图、数据表 (4) 用户提到"多维表格"、"bitable"、"数据表"、"记录"、"字段" (5) 需要批量导入数据或批量更新多维表格

feishu-calendar

飞书日历与日程管理工具集。包含日历管理、日程管理、参会人管理、忙闲查询。

feishu-channel-rules

Lark/Feishu channel output rules. Always active in Lark conversations.

原文语言：英语

feishu-create-doc

创建飞书云文档。从 Lark-flavored Markdown 内容创建新的飞书云文档，支持指定创建位置（文件夹/知识库/知识空间）。

feishu-fetch-doc

获取飞书云文档内容。返回文档的 Markdown 内容，支持处理文档中的图片、文件和画板（需配合 feishu_doc_media 工具）。

feishu-im-read

飞书 IM 消息读取工具使用指南，覆盖会话消息获取、话题回复读取、跨会话消息搜索、图片/文件资源下载。 **当以下情况时使用此 Skill**: (1) 需要获取群聊或单聊的历史消息 (2) 需要读取话题（thread）内的回复消息 (3) 需要跨会话搜索消息（按关键词、发送者、时间等条件） (4) 消息中包含图片、文件、音频、视频，需要下载 (5) 用户提到"聊天记录"、"消息"、"群里说了什么"、"话题回复"、"搜索消息"、"图片"、"文件下载" (6) 需要按时间范围过滤消息、分页获取更多消息

feishu-task

飞书任务管理工具,用于创建、查询、更新任务和清单。 **当以下情况时使用此 Skill**: (1) 需要创建、查询、更新、删除任务 (2) 需要创建、管理任务清单 (3) 需要查看任务列表或清单内的任务 (4) 用户提到"任务"、"待办"、"to-do"、"清单"、"task" (5) 需要设置任务负责人、关注人、截止时间

feishu-troubleshoot

飞书插件问题排查工具。包含常见问题 FAQ 和深度诊断命令（/feishu_doctor）。 常见问题可随时查阅。诊断命令用于排查复杂问题（多次授权仍失败、自动授权无法解决等）， 会检查账户配置、API 连通性、应用权限、用户授权状态，并生成详细的诊断报告和解决方案。

feishu-update-doc

更新飞书云文档。支持 7 种更新模式：追加、覆盖、定位替换、全文替换、前/后插入、删除。

classifying-tax-transactions

Content skill for classifying business bank transactions into US federal Schedule C (Form 1040) line items for sole proprietors and single-member LLCs disregarded for federal tax. Tax year 2025. Federal only. Supplies the Tier 1 deterministic vendor pattern…

原文语言：英语

tax-workflow-base

Workflow-only base skill for regulated-domain classification. Defines the three-state contract (clean / conservative-default-with-flag / refuse), the conservative-defaults principle, the citation discipline, the structured-question form, and the…

原文语言：英语

csv-workbench

Analyze CSV files in /mnt/data and return concise numeric summaries.

原文语言：英语

openai-knowledge

Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI…

原文语言：英语

test-coverage-improver

Improve test coverage in the OpenAI Agents Python repository: run `make coverage`, inspect coverage artifacts, identify low-coverage files, propose high-impact tests, and confirm with the user before writing tests.

原文语言：英语

rosa-aws-context

Cross-check AWS-facing code and docs against official ROSA and AWS references before changing behavior.

原文语言：英语

rosa-command-authoring

Add or edit Cobra commands in openshift/rosa while keeping command wiring thin and package logic aligned with repo structure.

原文语言：英语

rosa-docs-and-structure-tests

Keep CLI docs, structure tests, and user-facing guidance in sync when commands or workflow docs change.

原文语言：英语

rosa-verification-gates

Choose the right local verification steps before claiming a ROSA change is complete.

原文语言：英语

autoresearch

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction.…

原文语言：英语

evolving-ai-agents

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent…

原文语言：英语

huggingface-accelerate

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace…

原文语言：英语

autogpt-agents

Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.

原文语言：英语

awq-quantization

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for…

原文语言：英语

axolotl

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

原文语言：英语

evaluating-code-models

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard…

原文语言：英语

quantizing-models-bitsandbytes

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with…

原文语言：英语

constitutional-ai

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers…

原文语言：英语

crewai-multi-agent

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring…

原文语言：英语

deepspeed

Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention

原文语言：英语

optimizing-attention-flash

Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports…

原文语言：英语

gguf-quantization

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

原文语言：英语

gptq

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates…

原文语言：英语

grpo-rl-training

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

原文语言：英语

hqq-quantization

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

原文语言：英语

huggingface-tokenizers

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with…

原文语言：英语

lambda-labs-gpu-cloud

Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.

原文语言：英语

implementing-llms-litgpt

Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA.…

原文语言：英语

llama-cpp

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on…

原文语言：英语

llama-factory

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

原文语言：英语

llamaguard

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo…

原文语言：英语