Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/goondocks-co/open-agent-kit/context-engineeringnpx skills add goondocks-co/open-agent-kit --skill context-engineeringgit clone --depth 1 https://github.com/goondocks-co/open-agent-kitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00097 | $0.03616 |
| Opus 5 | $0.00048 | $0.01808 |
| Sonnet 5 | $0.00019 | $0.00723 |
| Haiku 4.5 | $0.00010 | $0.00362 |
Grade A, and why
context-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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").
- Define the role in one sentence: what the agent IS and IS NOT
- Set altitude -- right level: "Follow the project's existing naming conventions. When none exists, prefer descriptive names over short ones."
- Add constraints using RFC 2119 language (MUST, SHOULD, MAY)
- Include 1-2 canonical examples of ideal output
- 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
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 312 lines · 97 tokens per session scan A 6e1bb1d89f6c
context-engineering is a skill published in the GitHub repository goondocks-co/open-agent-kit (10 stars, last pushed 1mo ago), licensed MIT. It adds 97 tokens to every session and 3,616 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
promptkit
PromptKit composition engine. Use this skill when the user wants to assemble a task-specific prompt, write a requirements doc, investigate a bug, review code, plan an implementation, author agent instructions, create a Copilot prompt file, build an agentic workflow, or perform any engineering task that PromptKit has a…
bootstrap
Alias for the /promptkit skill. Use this when the user wants to assemble a task-specific prompt using PromptKit, or says "bootstrap" to start the PromptKit composition engine.
prompt-writing
Create, refine, and optimize high-quality YAML prompts for AI assistants. Use when working with prompt templates, system prompts, agent prompts, or any prompt engineering tasks. Provides structure guidelines, template patterns, and quality standards for YAML-based prompts.
skill-meta-prompt
Craft better prompts using proven optimization techniques — use when your prompt needs refinement.
evolving-ai-agents
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
dashscope
DashScope (Alibaba Cloud Bailian / 阿里云百炼) integration — image generation (qwen-image-2.0-pro), text-to-speech (qwen3-tts-flash), and ASR with word-level timestamps (qwen3-asr-flash-filetrans). Use when generating images via Qwen-Image, narrating via Qwen-TTS, or transcribing with word-level timestamps via Qwen-ASR.