open-agent-kit: Skill for Claude Code

Context Engineering

Design effective prompts and optimize the full context window for AI models and agents to produce consistently high-quality output.

Quick Start

Recipe 1: Improve a vague prompt

Before: Review this code and give feedback.

After:

<task>Review the provided code for correctness, performance, and maintainability.</task>
<output-format>
For each issue: file/line, severity (critical|warning|suggestion), problem (one sentence), fix (concrete code change).
If no issues, state "No issues found" and list one strength.
</output-format>
<code>{{code_to_review}}</code>

Why it works: Specifies evaluation criteria, defines output structure, separates instructions from data with XML tags.

Recipe 2: Design a system prompt

Use the altitude concept -- not so high it's useless ("be helpful"), not so low it's brittle ("always use 4 spaces").

1. Define the role in one sentence: what the agent IS and IS NOT
2. Set altitude -- right level: "Follow the project's existing naming conventions. When none exists, prefer descriptive names over short ones."
3. Add constraints using RFC 2119 language (MUST, SHOULD, MAY)
4. Include 1-2 canonical examples of ideal output
5. Define non-goals to prevent scope creep

See references/system-prompt-design.md for full anatomy and templates.

Recipe 3: Structure agent context with the 4 strategies

When your agent produces inconsistent or degraded output, apply the four strategies:

Strategy	Action	Example
Write	Craft persistent instructions	System prompt with altitude-appropriate rules
Select	Choose what enters context	Use oak_search to retrieve only relevant code
Compress	Reduce tokens, preserve signal	Summarize prior conversation turns, delegate to sub-agents
Isolate	Move information out of context	Store reference docs externally, load just-in-time

1. Start with Write: define a clear system prompt
2. Apply Select: give the agent only the tools and context it needs
3. Add Compress: implement compaction for long sessions
4. Use Isolate: move large reference material behind retrieval