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context-summarization

Trim project context to essential task_goal, constraints, and artifacts.

Updated Mar 22, 2026
Search Tags:#workflow#ai-agent#token-optimization#context-summarization#context-trimming#task-goal#raw_context
One-click install
npx skills add https://github.com/Burburton/amazing_agent_specialist --skill context-summarization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: context-summarization
Source: https://github.com/Burburton/amazing_agent_specialist/tree/main/templates/pack/full/.opencode/skills/common/context-summarization
Command: npx skills add https://github.com/Burburton/amazing_agent_specialist --skill context-summarization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Contexts in large projects can become unwieldy, causing AI agents to process unnecessary data and drift from the current task.

Core Features & Use Cases

  • Reduces the active context to the minimum set of information required for the current role and task.
  • Keeps essential items like task_goal, constraints, and key artifacts while discarding noise from history and non-essential files.
  • Use Case: when a task spans many files or milestones, generate a focused summary to feed downstream agents.

Quick Start

Provide raw_context, current_role, and task_goal to generate a concise, task-focused context payload.

Frequently Asked Questions about context-summarization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce token bloat when an AI agent processes large project contexts?▼

Context summarization trims project context to the minimum set of information required for a specific task. It preserves task goals, constraints, and relevant artifacts while discarding irrelevant history and non-essential files to prevent AI agents from drifting.

What is the best way to trim raw context for downstream AI agents?▼

The best way to trim raw context is to supply the raw_context, current_role, and task_goal parameters. This generates a concise, task-focused context payload that retains only essential items for downstream AI agents.

Why does my AI agent drift from the current task when processing multiple files?▼

AI agents drift from the current task because unwieldy contexts in large projects force them to process unnecessary data. Trimming the context to the minimum necessary information prevents this drift and improves decision quality.

When do I need context trimming for an AI agent workflow?▼

You need context trimming for an AI agent workflow when a task spans many files or milestones. It generates a focused summary to feed downstream agents, ensuring they only process the minimum required data.

Does context summarization work for tasks spanning multiple milestones?▼

Yes, context summarization works for tasks spanning multiple milestones by generating a focused summary to feed downstream agents. It actively filters out noise from project history and non-essential files while keeping constraints intact.

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