context-engineering

What this skill does

# Context Engineering

Context engineering is the discipline of curating and maintaining the optimal set of tokens during LLM inference. Unlike prompt engineering (crafting individual prompts), context engineering focuses on what information enters the context window and when.

## Table of Contents

- [Core Principles](#core-principles)
- [Context Management Strategies](#context-management-strategies)
- [System Prompt Design](#system-prompt-design)
- [Tool Design for Context Efficiency](#tool-design-for-context-efficiency)
- [Long-Horizon Task Patterns](#long-horizon-task-patterns)
- [Implementation Patterns](#implementation-patterns)
- [Best Practices](#best-practices)
- [References](#references)

## Core Principles

### Context as a Finite Resource

LLMs have limited "attention budgets." As context length increases, models experience **context rot**—decreased ability to accurately recall information. The goal is finding the smallest possible set of high-signal tokens that maximize desired outcomes.

```
Effective Context = Relevant Information / Total Tokens
```

**Key insight**: More context isn't better. The right context is better.

### The Context Pollution Problem

Every token added to context has costs:
- Increased latency and compute
- Diluted attention to important information
- Higher risk of hallucination from conflicting data
- Reduced model performance on retrieval tasks

## Context Management Strategies

### 1. Context Trimming

Drop older conversation turns, keeping only the last N turns.

| Aspect | Details |
|--------|---------|
| **Mechanism** | Sliding window over conversation history |
| **Pros** | Deterministic, zero latency, preserves recent context verbatim |
| **Cons** | Abrupt loss of long-range context, "amnesia" effect |
| **Best for** | Independent tasks, short interactions, predictable workflows |

```python
def trim_context(messages: list, keep_last_n: int = 10) -> list:
    """Keep system message + last N turns."""
    system_msgs = [m for m in messages if m["role"] == "system"]
    other_msgs = [m for m in messages if m["role"] != "system"]
    return system_msgs + other_msgs[-keep_last_n:]
```

### 2. Context Summarization

Compress prior messages into structured summaries.

| Aspect | Details |
|--------|---------|
| **Mechanism** | LLM generates summary of older context |
| **Pros** | Retains long-range memory, smoother UX, scalable |
| **Cons** | Summarization bias risk, added latency, potential compounding errors |
| **Best for** | Complex multi-step tasks, long-horizon interactions |

```python
SUMMARIZATION_PROMPT = """Summarize the conversation so far, preserving:
1. Key decisions made
2. Important context established
3. Current task state and goals
4. Any constraints or preferences expressed

Be concise but complete. Output as structured markdown."""

async def summarize_context(messages: list, model) -> str:
    """Generate a summary of conversation history."""
    conversation_text = format_messages_for_summary(messages)
    response = await model.generate(
        system=SUMMARIZATION_PROMPT,
        user=conversation_text
    )
    return response.content
```

### 3. Hybrid Approach

Combine trimming and summarization for optimal balance.

```python
class HybridContextManager:
    def __init__(
        self,
        keep_recent: int = 5,      # Recent turns to keep verbatim
        summary_threshold: int = 20, # When to trigger summarization
    ):
        self.keep_recent = keep_recent
        self.summary_threshold = summary_threshold
        self.running_summary = ""

    def process(self, messages: list) -> list:
        if len(messages) < self.summary_threshold:
            return messages

        # Summarize older messages
        old_messages = messages[:-self.keep_recent]
        self.running_summary = summarize(old_messages, self.running_summary)

        # Return summary + recent messages
        return [
            {"role": "system", "content": f"Previous context:\n{self.running_summary}"},
            *messages[-self.keep_recent:]
        ]
```

### 4. Session Memory

Persist reusable facts, preferences, and task state outside the context window. Load only the relevant slice for the current turn.

| Aspect | Details |
|--------|---------|
| **Mechanism** | External store keyed by user, session, task, or resource |
| **Pros** | Recovers long-range context without carrying all history |
| **Cons** | Requires retrieval, freshness, and deletion policies |
| **Best for** | Agents, project work, personalization, long-running workflows |

Separate durable memory from ephemeral scratchpads. Durable memory should contain stable facts and explicit decisions, not every intermediate thought.

## System Prompt Design

### Principles for Context-Efficient Prompts

1. **Clear and direct language**: Avoid ambiguity that requires clarification turns
2. **Structured sections**: Organize by purpose (role, capabilities, constraints)
3. **Minimal yet comprehensive**: Include only what affects behavior
4. **Self-contained instructions**: Reduce need for context retrieval

### Example Structure

```markdown
# Role
You are [specific role] that [primary function].

# Capabilities
- [Capability 1 with scope]
- [Capability 2 with scope]

# Constraints
- [Hard constraint]
- [Preference]

# Output Format
[Specific format requirements]
```

## Tool Design for Context Efficiency

### Just-in-Time Context Loading

Instead of front-loading all possible context, load information dynamically as needed.

```python
# Anti-pattern: Loading everything upfront
context = load_all_user_data()  # Large, mostly unused
context += load_all_documents()  # Even larger

# Better: Just-in-time retrieval
tools = [
    Tool(
        name="get_user_preference",
        description="Get specific user preference by key",
        # Only fetches what's needed when asked
    ),
    Tool(
        name="search_documents",
        description="Search documents by query",
        # Returns relevant subset
    ),
]
```

### Tool Design Principles

1. **Self-contained**: Each tool returns complete, usable information
2. **Scoped**: Tools do one thing well
3. **Descriptive**: Names and descriptions guide LLM toward correct usage
4. **Error-robust**: Return informative errors that don't pollute context

```python
# Well-designed tool
def search_codebase(query: str, max_results: int = 5) -> str:
    """Search codebase for relevant code snippets.

    Args:
        query: Natural language description of what to find
        max_results: Maximum snippets to return (default 5)

    Returns:
        Formatted code snippets with file paths and line numbers,
        or 'No results found' if nothing matches.
    """
    results = perform_search(query, limit=max_results)
    if not results:
        return "No results found for query."
    return format_results(results)  # Concise, structured output
```

## Long-Horizon Task Patterns

### Pattern 1: Compaction

Periodically compress conversation history to reclaim context space.

```python
async def compaction_loop(agent, messages, task):
    while not task.complete:
        # Process next step
        response = await agent.run(messages)
        messages.append(response)

        # Compact when approaching limit
        if estimate_tokens(messages) > TOKEN_LIMIT * 0.8:
            summary = await summarize_context(messages[:-3])
            messages = [
                {"role": "system", "content": agent.system_prompt},
                {"role": "assistant", "content": f"Summary of progress:\n{summary}"},
                *messages[-3:]  # Keep recent context
            ]

    return messages
```

### Pattern 2: Structured Note-Taking

Agent maintains external notes, retrieving as needed.

```python
class NoteTakingAgent:
    def __init__(self):
        self.notes = {}  # Key-value store outside context

    async def run(self, messages):
        tools = [
            Tool("save_note", self.save_note, "Save infor