Skip to content

/context-loading

Load minimum necessary context into agent context windows. Prevents token bloat, reduces cost, and improves focus. Only load what the current task needs.

shell
$ npx -y skills add DevelopersGlobal/ai-agent-skills --skill context-loading --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
  • You can call itInvoke it directly when you want it.
  • Slash command/context-loading
How auto-invocation works

Context preview

The summary Claude sees to decide when to auto-load this skill.

Load minimum necessary context into agent context windows. Prevents token bloat, reduces cost, and improves focus. Only load what the current task needs.

SKILL.md

context-loading.SKILL.md
name: context-loading
description: Load minimum necessary context into agent context windows. Prevents token bloat, reduces cost, and improves focus. Only load what the current task needs.
category: plan
applies-to: [claude, gemini, cursor, copilot, any]
version: 1.0.0

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

4. Long conversation history → summarize to key decisions and current state. 5. Large files → extract only the relevant functions/sections. 6. Entire docs → extract only the relevant sections. 7. 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

8. 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

9. 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

10. Don't carry over context from a completed task to a new task. 11. Start each distinct task with a fresh, minimal context. 12. 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)
Read more
Read it on GitHub ↗
Ships withai-agent-skills

AI agent skills for production grade applications

Get the whole plugin, auto-invoked
Stats
64
Stars
0
Views
10
Forks
Maintained
Maintenance
Python
Language
MIT
License
3mo ago
Last commit
3mo ago
Created

Repo: DevelopersGlobal/ai-agent-skills