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 skills add Owl-Listener/ai-design-skills --skill context-engineeringgit clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/owl-listener/ai-design-skills/context-engineering)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/context-engineering"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/context-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/context-engineering"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/context-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00018 | $0.00601 |
| Opus 5 | $0.00009 | $0.00300 |
| Sonnet 5 | $0.00004 | $0.00120 |
| Haiku 4.5 | $0.00002 | $0.00060 |
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 10d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering
The context window is finite. What goes into it — and in what order — determines the quality of every output. Context engineering is the practice of deliberately designing the information architecture of the context window.
The Context Budget
Every context window has a token budget. Allocate it deliberately:
- System prompt: The foundational instructions (typically 5-20% of the budget)
- Retrieved context: Documents, data, and information pulled in for the current task
- Conversation history: Previous turns in the conversation
- User input: The current request
- Working space: Room for the model to generate its response These compete for space. More retrieved context means less conversation history. A longer system prompt means less room for everything else.
Information Architecture in Context
Order matters. The model pays different amounts of attention to different positions:
- Beginning: High attention. Put your most important instructions here.
- Middle: Lower attention. This is where information can get lost in long contexts.
- End: High attention. The most recent information (user input) naturally goes here.
- Adjacent to the task: Information placed right before the user's question gets more attention than information earlier in the context.
Context Selection
Not everything should go into the context. Design selection criteria:
- Relevance: Does this information help answer the current question?
- Recency: Is this the most up-to-date information available?
- Specificity: Is this specific enough to be useful, or is it too generic?
- Redundancy: Is this information already covered elsewhere in the context?
- Authority: Is this from a reliable source?
Context Strategies
- Retrieval-augmented generation (RAG): Pull relevant documents into the context dynamically
- Summarisation: Compress older context into summaries to free up space
- Prioritised history: Keep recent and important conversation turns, drop less important ones
- Structured context: Organise information with clear headers and sections so the model can navigate it
- Context caching: Pre-compute and cache frequently used context blocks
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.
- 10d ago First seen · 46 lines · 18 tokens per session scan A 5640622b775b
context-engineering is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 18 tokens to every session and 601 once invoked, about $0.0001 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 2026-08-30.
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