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
- List the files/docs the agent needs to read to complete THIS specific task.
- For each item, ask: "Can the agent complete the task without this?" If yes, don't include it.
- 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
- Long conversation history → summarize to key decisions and current state.
- Large files → extract only the relevant functions/sections.
- Entire docs → extract only the relevant sections.
- 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
- 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
- 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
- Don't carry over context from a completed task to a new task.
- Start each distinct task with a fresh, minimal context.
- 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](../rag-and-memory/SKILL.md)
- [multi-agent-orchestration skill](../multi-agent-orchestration/SKILL.md)