Query → ┬─► Vector Search ──► Candidates ─┐
        │                                  │
        └─► Keyword Search ─► Candidates ─┴─► Fusion ─► Results

from typing import List, Dict, Tuple
from collections import defaultdict

def reciprocal_rank_fusion(
    result_lists: List[List[Tuple[str, float]]],
    k: int = 60,
    weights: List[float] = None
) -> List[Tuple[str, float]]:
    """
    Combine multiple ranked lists using RRF.

    Args:
        result_lists: List of (doc_id, score) tuples per search method
        k: RRF constant (higher = more weight to l

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</details>

### langchain-architecture

> Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

<details>
<summary>View skill definition</summary>

# LangChain & LangGraph Architecture

Master modern LangChain 1.x and LangGraph for building sophisticated LLM applications with agents, state management, memory, and tool integration.

## When to Use This Skill

- Building autonomous AI agents with tool access
- Implementing complex multi-step LLM workflows
- Managing conversation memory and state
- Integrating LLMs with external data sources and APIs
- Creating modular, reusable LLM application components
- Implementing document processing pipelines
- Building production-grade LLM applications

## Package Structure (LangChain 1.x)

## Core Concepts

### 1. LangGraph Agents

LangGraph is the standard for building agents in 2026. It provides:

**Key Features:**

- **StateGraph**: Explicit state management with typed state
- **Durable Execution**: Agents persist through failures
- **Human-in-the-Loop**: Inspect and modify state at any point
- **Memory**: Short-term and long-term memory across sessions
- **Checkpointing**: Save and resume agent state

**Agent Patterns:**

- 

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</details>

### llm-evaluation

> Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

<details>
<summary>View skill definition</summary>

# LLM Evaluation

Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.

## When to Use This Skill

- Measuring LLM application performance systematically
- Comparing different models or prompts
- Detecting performance regressions before deployment
- Validating improvements from prompt changes
- Building confidence in production systems
- Establishing baselines and tracking progress over time
- Debugging unexpected model behavior

## Core Evaluation Types

### 1. Automated Metrics

Fast, repeatable, scalable evaluation using computed scores.

**Text Generation:**

- **BLEU**: N-gram overlap (translation)
- **ROUGE**: Recall-oriented (summarization)
- **METEOR**: Semantic similarity
- **BERTScore**: Embedding-based similarity
- **Perplexity**: Language model confidence

**Classification:**

- **Accuracy**: Percentage correct
- **Precision/Recall/F1**: Class-specific performance
- **Confusion Matrix**: Error patterns
- **AUC-ROC**: Ranking quality

**Retrieval (RAG):**

- **MRR**: Mean Reciprocal Rank
- **NDCG**: Normalized Discounted Cumulative Gain
- **Precision@K**: Relevant in top K
- **Recall@K**: Coverage in top K

### 2. Human Evaluation

Manual assessment for quality aspects difficult to automate.

**Dimensions:**

- **Accuracy**: Factual correctness
- **Coherence**: Logical flow
- **Relevance**: Answers the question
- **Fluency**: Natural language quality
- **Safety**: No harmful content
- **Helpful

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</details>

### prompt-engineering-patterns

> Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

<details>
<summary>View skill definition</summary>

# Prompt Engineering Patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

## When to Use This Skill

- Designing complex prompts for production LLM applications
- Optimizing prompt performance and consistency
- Implementing structured reasoning patterns (chain-of-thought, tree-of-thought)
- Building few-shot learning systems with dynamic example selection
- Creating reusable prompt templates with variable interpolation
- Debugging and refining prompts that produce inconsistent outputs
- Implementing system prompts for specialized AI assistants
- Using structured outputs (JSON mode) for reliable parsing

## Core Capabilities

### 1. Few-Shot Learning

- Example selection strategies (semantic similarity, diversity sampling)
- Balancing example count with context window constraints
- Constructing effective demonstrations with input-output pairs
- Dynamic example retrieval from knowledge bases
- Handling edge cases through strategic example selection

### 2. Chain-of-Thought Prompting

- Step-by-step reasoning elicitation
- Zero-shot CoT with "Let's think step by step"
- Few-shot CoT with reasoning traces
- Self-consistency techniques (sampling multiple reasoning paths)
- Verification and validation steps

### 3. Structured Outputs

- JSON mode for reliable parsing
- Pydantic schema enforcement
- Type-safe response handling
- Error handling for malformed outputs

### 4. Prompt Optimization

- Iterative refinement 

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</details>

## Source

[View on GitHub](https://github.com/wshobson/agents)