Agent Skills

by notion

Search the user's Notion workspace using the Notion MCP server. Use for finding pages, databases, and content by keywords or natural-language queries.

tasks-explain-diff

by notion

Generate a rich Notion document explaining code changes. Creates comprehensive documentation with background, intuition, code walkthrough, and verification steps.

tasks-plan

by notion

Create an implementation plan from a Notion task or specification. Breaks down requirements into actionable steps with estimates and dependencies.

tasks-setup

by notion

Set up a Notion task board for tracking tasks. Guides users through using a template or connecting an existing board.

spec-to-implementation

by notion

Turn product or tech specs into concrete Notion tasks. Breaks down spec pages into detailed implementation plans with clear tasks, acceptance criteria, and progress tracking.

find

by notion

Quickly find pages or databases in Notion by title keywords. Returns precise matches rather than comprehensive results.

knowledge-capture

by notion

Transform conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context with proper organization and linking.

tasks-build

by notion

Build a task from a Notion page URL. Fetches task details, marks it in progress, implements the work, and updates status in Notion.

create-page

by notion

Create a new Notion page, optionally under a specific parent. Automatically structures content based on page type (meeting notes, project pages, etc.).

database-query

by notion

Query a Notion database by name or ID and return structured, readable results with optional filters and sorting.

pinecone-assistant

🔥🔥🔥✓ VerifiedAccount requiredNeeds API keys

by pinecone-io

Create, manage, and chat with Pinecone Assistants for document Q&A with citations. Handles all assistant operations - create, upload, sync, chat, context retrieval, and list. Recognizes natural language like "create an assistant from my docs", "ask my assistant about X", or "upload my docs to Pinecone".

pinecone-full-text-search

by pinecone-io

Create, ingest into, and query a Pinecone full-text-search (FTS) index using the preview API (2026-01.alpha, public preview). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct score_by clauses (text / query_string / dense_vector / sparse_vector), or compose with text-match filters ($match_phrase / $match_all / $match_any). Ships `scripts/ingest.py` for safe bulk ingestion (batch_upsert + error inspection + rea

pinecone-query

🔥🔥🔥✓ VerifiedAccount requiredNeeds API keys

by pinecone-io

Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.

pinecone-cli

by pinecone-io

Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation, and full control over Pinecone resources.

pinecone-docs

🔥🔥✓ VerifiedFreeQuick setup

by pinecone-io

Curated documentation reference for developers building with Pinecone. Contains links to official docs organized by topic and data format references. Use when writing Pinecone code, looking up API parameters, or needing the correct format for vectors or records.

pinecone-help

🔥🔥✓ VerifiedFreeQuick setup

by pinecone-io

Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.

pinecone-mcp

by pinecone-io

Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available, how to use them, or what parameters they accept.

pinecone-quickstart

🔥🔥🔥✓ VerifiedAccount requiredNeeds API keys

by pinecone-io

Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided tour of Pinecone's tools.

pinecone-n8n

by pinecone-io

Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.

huggingface-tokenizers

by firecrawl

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 transformers. Use when you need high-performance tokenization or custom tokenizer training.

constitutional-ai

by firecrawl

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 Claude's safety system.

gptq

by firecrawl

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 with transformers and PEFT for QLoRA fine-tuning.

langchain

by firecrawl

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

llamaguard

by firecrawl

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 Guardrails.

pytorch-lightning

by firecrawl

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

ray-data

by firecrawl

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

sglang

by firecrawl

Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.

tensorrt-llm

by firecrawl

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

blip-2-vision-language

by firecrawl

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.

🔥🔥🔥FreeQuick setup

fine-tuning-with-trl