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-window-designgit 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-window-design)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/context-window-design"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/context-window-design/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-window-design"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/context-window-design.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 | $0.00016 | $0.00397 |
| Opus 5.5 | $0.00006 | $0.00159 |
| Sonnet 5.5 | $0.00003 | $0.00079 |
| Haiku 4.5 | $0.00002 | $0.00040 |
Grade A, and why
context-window-design 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 1mo 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.
What it actually says
Context Window Design
Every AI model has a finite context window. Designing within this constraint — and designing the user experience around it — is a core skill for AI product design.
The Context Window as a Design Material
The context window is not just a technical limitation. It's a design material:
- What goes in: System prompts, conversation history, retrieved documents, tool results, user preferences
- What gets dropped: Older messages, less relevant context, verbose instructions
- What the user sees: The conversation as presented may differ from what the model actually processes Designers must understand context window allocation to design reliable experiences.
Memory and Persistence
Users expect AI to remember. Design for different memory horizons:
- Within-conversation memory: What was said earlier in this chat. Usually handled by the context window itself.
- Cross-conversation memory: Preferences, past decisions, ongoing projects. Requires explicit memory systems.
- Shared memory: Context shared across multiple users or agents. Requires careful privacy design.
Strategies for Limited Context
- Summarisation: Compress earlier conversation into summaries to free up tokens
- Retrieval-augmented generation: Pull in relevant context on demand rather than keeping everything loaded
- Priority ordering: Put the most important context closest to the prompt (recency bias in attention)
- User-controlled context: Let users pin, remove, or prioritise what the AI remembers
- Graceful degradation: When context is lost, acknowledge it rather than hallucinating continuity
Design Artefacts
- Context budget allocations (how many tokens for system prompt, history, retrieval, etc.)
- Memory architecture diagrams showing what persists and what's ephemeral
- Context overflow UX flows (what happens when the window fills up)
- User-facing memory controls specification
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.
- 1mo ago First seen · 29 lines · 16 tokens per session scan A 28ae430e395e
context-window-design is a skill published in the GitHub repository Owl-Listener/ai-design-skills (189 stars, last pushed 4mo ago), licensed MIT. It adds 16 tokens to every session and 397 once invoked, about $0.0001 per session on Opus 5.5. 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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