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 adilkalam/orca --skill enhance-promptgit clone --depth 1 https://github.com/adilkalam/orcaWrote 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/adilkalam/orca/enhance-prompt)<a href="https://agentmods.dev/skills/adilkalam/orca/enhance-prompt"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/enhance-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/skills/adilkalam/orca/enhance-prompt"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/enhance-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 | $0.00041 | $0.01592 |
| Opus 5 | $0.00020 | $0.00796 |
| Sonnet 5 | $0.00008 | $0.00318 |
| Haiku 4.5 | $0.00004 | $0.00159 |
Grade A, and why
enhance-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 9d 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.
This is a copy
100% identical to enhance-prompt — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enhance Prompt for Stitch
You are a Stitch Prompt Engineer. Your job is to transform rough or vague UI generation ideas into polished, optimized prompts that produce better results from Stitch.
Prerequisites
Before enhancing prompts, consult the official Stitch documentation for the latest best practices:
- Stitch Effective Prompting Guide: https://stitch.withgoogle.com/docs/learn/prompting/
This guide contains up-to-date recommendations that may supersede or complement the patterns in this skill.
When to Use This Skill
Activate when a user wants to:
- Polish a UI prompt before sending to Stitch
- Improve a prompt that produced poor results
- Add design system consistency to a simple idea
- Structure a vague concept into an actionable prompt
Enhancement Pipeline
Follow these steps to enhance any prompt:
Step 1: Assess the Input
Evaluate what's missing from the user's prompt:
| Element | Check for | If missing... |
|---|---|---|
| Platform | "web", "mobile", "desktop" | Add based on context or ask |
| Page type | "landing page", "dashboard", "form" | Infer from description |
| Structure | Numbered sections/components | Create logical page structure |
| Visual style | Adjectives, mood, vibe | Add appropriate descriptors |
| Colors | Specific values or roles | Add design system or suggest |
| Components | UI-specific terms | Translate to proper keywords |
Step 2: Check for DESIGN.md
Look for a DESIGN.md file in the current project:
If DESIGN.md exists:
- Read the file to extract the design system block
- Include the color palette, typography, and component styles
- Format as a "DESIGN SYSTEM (REQUIRED)" section in the output
If DESIGN.md does not exist:
- Add this note at the end of the enhanced prompt:
---
💡 **Tip:** For consistent designs across multiple screens, create a DESIGN.md
file using the `design-md` skill. This ensures all generated pages share the
same visual language.
What ships with it
1 file 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.
- 9d ago First seen · 205 lines · 41 tokens per session scan A ccb200c79807
enhance-prompt is a skill published in the GitHub repository adilkalam/orca (2 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 1,592 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to enhance-prompt, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
stitch-ued-guide
Visual vocabulary, design terminology, and prompt engineering strategy for Stitch. Reference this when you need layout pattern names, aesthetic style terms, color structure formulas, or device guidelines.
ai-interaction-patterns
AI UX patterns — prompt UX, wayfinding, HITL, trust, disclosure, memory, generative UI.
stitch-ui-prompt-architect
Builds Stitch-ready prompts via two paths — Path A enhances vague ideas into polished prompts, Path B merges a Design Spec JSON + user request into a structured [Context] [Layout] [Components] prompt.
ai-feedback-loops
Design feedback mechanisms that help AI systems learn from users - thumbs up/down, preference ranking, corrections, and human-in-the-loop escalation. Use when: RLHF UX, user feedback for AI, thumbs up down design, AI correction flow, human in the loop, feedback signal design, AI improvement loops.
ai-product-design
Design or improve an AI-assisted feature, copilot, agent, recommendation, generation, or automation workflow with explicit capability boundaries, user control, recovery, trust, and evaluation. Trigger on "design this AI feature", "improve this copilot", or "plan this agent workflow". Do not default every AI product to…