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.
git 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/commands/owl-listener/ai-design-skills/design-prompt)<a href="https://agentmods.dev/commands/owl-listener/ai-design-skills/design-prompt"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/design-prompt/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/commands/owl-listener/ai-design-skills/design-prompt"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/design-prompt.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 | $0.00010 | $0.00444 |
| Opus 5 | $0.00005 | $0.00222 |
| Sonnet 5 | $0.00002 | $0.00089 |
| Haiku 4.5 | $0.00001 | $0.00044 |
Grade A, and why
design-prompt 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 12d 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
You are designing a system prompt. Use only skills from the prompt-architecture plugin. Follow this process:
Step 1: Define Requirements
- What is this AI feature for?
- Who are the users?
- What should the AI do? What should it NOT do?
- What output format and quality is expected?
Step 2: Structure the Prompt
Using system-prompt-structure:
- Write the Identity and Role section
- Write the Context and Knowledge section
- Write the Behavioral Rules section
- Write the Output Specifications section
- Ensure sections are clearly separated and ordered by importance
Step 3: Design Constraints
Using constraint-specification:
- Define format constraints (output structure, required fields)
- Define length constraints (word count ranges, section proportions)
- Define content constraints (topics to include/exclude, source restrictions)
- Define tone constraints (formality, voice, audience)
- Define quality constraints (accuracy, completeness, actionability)
- Establish constraint priority hierarchy
Step 4: Add Examples
Using few-shot-patterns:
- Create 2-3 input-output examples demonstrating ideal behavior
- Include one common case and one edge case
- Ensure examples are high quality and consistent with the constraints
Step 5: Design Context Integration
Using context-engineering:
- Define where retrieved context will be injected
- Specify context selection criteria
- Allocate the context budget across sections
- Design the information architecture within the prompt
Step 6: Plan for Iteration
Using prompt-versioning:
- Document the rationale for key design decisions
- Define test cases for evaluating the prompt
- Establish the review and deployment process
Output
Deliver a complete system prompt package:
- The system prompt itself (ready to use)
- Prompt design rationale document
- Constraint specification
- Example library (2-3 examples)
- Context integration specification
- Test cases for validation
- Version notes (v1.0)
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.
- 12d ago First seen · 52 lines · 10 tokens per session scan A 7aaad593106f
design-prompt is a command published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 10 tokens to every session and 444 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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