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Explains LLM context fundamentals: system prompts, tool definitions, anatomy, and progressive disclosure. For designing agents, debugging behavior, optimizing tokens, and onboarding.
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Context is the complete state available to a language model at inference time. It includes everything the model can attend to when generating responses: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Understanding context fundamentals is prerequisite to effective context engineering.
Context is the complete state available to a language model at inference time. It includes everything the model can attend to when generating responses: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Understanding context fundamentals is prerequisite to effective context engineering.
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Context comprises several distinct components, each with different characteristics and constraints. The attention mechanism creates a finite budget that constrains effective context usage. Progressive disclosure manages this constraint by loading information only as needed. The engineering discipline is curating the smallest high-signal token set that achieves desired outcomes.
System Prompts System prompts establish the agent's core identity, constraints, and behavioral guidelines. They are loaded once at session start and typically persist throughout the conversation. System prompts should be extremely clear and use simple, direct language at the right altitude for the agent.
The right altitude balances two failure modes. At one extreme, engineers hardcode complex brittle logic that creates fragility and maintenance burden. At the other extreme, engineers provide vague high-level guidance that fails to give concrete signals for desired outputs or falsely assumes shared context. The optimal altitude strikes a balance: specific enough to guide behavior effectively, yet flexible enough to provide strong heuristics.
Organize prompts into distinct sections using XML tagging or Markdown headers to delineate background information, instructions, tool guidance, and output description. The exact formatting matters less as models become more capable, but structural clarity remains valuable.
Tool Definitions Tool definitions specify the actions an agent can take. Each tool includes a name, description, parameters, and return format. Tool definitions live near the front of context after serialization, typically before or after the system prompt.
Tool descriptions collectively steer agent behavior. Poor descriptions force agents to guess; optimized descriptions include usage context, examples, and defaults. The consolidation principle states that if a human engineer cannot definitively say which tool should be used in a given situation, an agent cannot be expected to do better.
Retrieved Documents Retrieved documents provide domain-specific knowledge, reference materials, or task-relevant information. Agents use retrieval augmented generation to pull relevant documents into context at runtime rather than pre-loading all possible information.
The just-in-time approach maintains lightweight identifiers (file paths, stored queries, web links) and uses these references to load data into context dynamically. This mirrors human cognition: we generally do not memorize entire corpuses of information but rather use external organization and indexing systems to retrieve relevant information on demand.
Message History Message history contains the conversation between the user and agent, including previous queries, responses, and reasoning. For long-running tasks, message history can grow to dominate context usage.
Message history serves as scratchpad memory where agents track progress, maintain task state, and preserve reasoning across turns. Effective management of message history is critical for long-horizon task completion.
Tool Outputs Tool outputs are the results of agent actions: file contents, search results, command execution output, API responses, and similar data. Tool outputs comprise the majority of tokens in typical agent trajectories, with research showing observations (tool outputs) can reach 83.9% of total context usage.
Tool outputs consume context whether they are relevant to current decisions or not. This creates pressure for strategies like observation masking, compaction, and selective tool result retention.
The Attention Budget Constraint Language models process tokens through attention mechanisms that create pairwise relationships between all tokens in context. For n tokens, this creates n² relationships that must be computed and stored. As context length increases, the model's ability to capture these relationships gets stretched thin.
Models develop attention patterns from training data distributions where shorter sequences predominate. This means models have less experience with and fewer specialized parameters for context-wide dependencies. The result is an "attention budget" that depletes as context grows.
Position Encoding and Context Extension Position encoding interpolation allows models to handle longer sequences by adapting them to originally trained smaller contexts. However, this adaptation introduces degradation in token position understanding. Models remain highly capable at longer contexts but show reduced precision for information retrieval and long-range reasoning compared to performance on shorter contexts.
The Progressive Disclosure Principle Progressive disclosure manages context efficiently by loading information only as needed. At startup, agents load only skill names and descriptions—sufficient to know when a skill might be relevant. Full content loads only when a skill is activated for specific tasks.
This approach keeps agents fast while giving them access to more context on demand. The principle applies at multiple levels: skill selection, document loading, and even tool result retrieval.
The assumption that larger context windows solve memory problems has been empirically debunked. Context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes.
Several factors create pressure for context efficiency. Processing cost grows disproportionately with context length—not just double the cost for double the tokens, but exponentially more in time and computing resources. Model performance degrades beyond certain context lengths even when the window technically supports more tokens. Long inputs remain expensive even with prefix caching.
The guiding principle is informativity over exhaustiveness. Include what matters for the decision at hand, exclude what does not, and design systems that can access additional information on demand.
Context must be treated as a finite resource with diminishing marginal returns. Like humans with limited working memory, language models have an attention budget drawn on when parsing large volumes of context.
Every new token introduced depletes this budget by some amount. This creates the need for careful curation of available tokens. The engineering problem is optimizing utility against inherent constraints.
Context engineering is iterative and the curation phase happens each time you decide what to pass to the model. It is not a one-time prompt writing exercise but an ongoing discipline of context management.
Agents with filesystem access can use progressive disclosure naturally. Store reference materials, documentation, and data externally. Load files only when needed using standard filesystem operations. This pattern avoids stuffing context with information that may not be relevant.
The file system itself provides structure that agents can navigate. File sizes suggest complexity; naming conventions hint at purpose; timestamps serve as proxies for relevance. Metadata of file references provides a mechanism to efficiently refine behavior.
The most effective agents employ hybrid strategies. Pre-load some context for speed (like CLAUDE.md files or project rules), but enable autonomous exploration for additional context as needed. The decision boundary depends on task characteristics and context dynamics.
For contexts with less dynamic content, pre-loading more upfront makes sense. For rapidly changing or highly specific information, just-in-time loading avoids stale context.
Design with explicit context budgets in mind. Know the effective context limit for your model and task. Monitor context usage during development. Implement compaction triggers at appropriate thresholds. Design systems assuming context will degrade rather than hoping it will not.
Effective context budgeting requires understanding not just raw token counts but also attention distribution patterns. The middle of context receives less attention than the beginning and end. Place critical information at attention-favored positions.
Example 1: Organizing System Prompts
<BACKGROUND_INFORMATION>
You are a Python expert helping a development team.
Current project: Data processing pipeline in Python 3.9+
</BACKGROUND_INFORMATION>
<INSTRUCTIONS>
- Write clean, idiomatic Python code
- Include type hints for function signatures
- Add docstrings for public functions
- Follow PEP 8 style guidelines
</INSTRUCTIONS>
<TOOL_GUIDANCE>
Use bash for shell operations, python for code tasks.
File operations should use pathlib for cross-platform compatibility.
</TOOL_GUIDANCE>
<OUTPUT_DESCRIPTION>
Provide code blocks with syntax highlighting.
Explain non-obvious decisions in comments.
</OUTPUT_DESCRIPTION>
Example 2: Progressive Document Loading
# Instead of loading all documentation at once:
# Step 1: Load summary
docs/api_summary.md # Lightweight overview
# Step 2: Load specific section as needed
docs/api/endpoints.md # Only when API calls needed
docs/api/authentication.md # Only when auth context needed
This skill provides foundational context that all other skills build upon. It should be studied first before exploring:
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Created: 2025-12-20 Last Updated: 2025-12-20 Author: Agent Skills for Context Engineering Contributors Version: 1.0.0
npx claudepluginhub sickn33/antigravity-awesome-skills --plugin antigravity-awesome-skillsExplains the components of LLM context (system prompts, tool definitions, message history) and principles like attention budgets and progressive disclosure. Useful when designing agent systems, debugging unexpected behavior, or reducing token costs.
Explains context engineering fundamentals: attention mechanics, U-shaped curve, context anatomy, and quality-versus-quantity tradeoffs. For conceptual understanding and onboarding.
Designs LLM context windows: allocates token budgets, orders information for attention, selects relevant data, and applies RAG/summarization strategies.