developersglobal/ai-agent-skills/context-loading

Overview

More context is not better context. Irrelevant context dilutes attention, increases cost, and slows inference. This skill enforces disciplined context loading: only the files, docs, and history that the current task requires.

When to Use

• Before starting any complex agent task
• When designing system prompts for production agents
• When context windows are filling up

Process

Step 1: Identify Required Context

1. List the files/docs the agent needs to read to complete THIS specific task.
2. For each item, ask: "Can the agent complete the task without this?" If yes, don't include it.
3. Prioritize: system prompt → task definition → directly relevant code → supporting references.

Verify: Every item in context is directly necessary for the current task.

Step 2: Summarize, Don't Dump

1. Long conversation history → summarize to key decisions and current state.
2. Large files → extract only the relevant functions/sections.
3. Entire docs → extract only the relevant sections.
4. Previous agent output → extract only the conclusions and next steps.

Verify: No item in context exceeds what's needed from that source.

Step 3: Set Context Budgets

1. Define token allocation for each context section:

• System prompt: ≤ 2,000 tokens
• Task definition: ≤ 500 tokens
• Code context: ≤ 4,000 tokens
• Conversation history (summarized): ≤ 1,000 tokens

1. Stay well within model context limits (leave 30% buffer for output).

Verify: Total prompt fits within 70% of model context limit.

Step 4: Refresh Context for New Tasks

1. Don't carry over context from a completed task to a new task.
2. Start each distinct task with a fresh, minimal context.
3. Re-introduce only what the new task genuinely needs.

Verification

• [ ] Context items limited to task-required items only
• [ ] Long content summarized before inclusion
• [ ] Token budget defined and respected
• [ ] Context window at ≤70% capacity

References

• rag-and-memory skill
• multi-agent-orchestration skill