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 mskayyali/Stateful --skill state-promptgit clone --depth 1 https://github.com/mskayyali/StatefulWrote 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/mskayyali/stateful/state-prompt)<a href="https://agentmods.dev/skills/mskayyali/stateful/state-prompt"><img src="https://agentmods.dev/badge/skills/mskayyali/stateful/state-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/mskayyali/stateful/state-prompt"><img src="https://agentmods.dev/badge/skills/mskayyali/stateful/state-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 | $0.00081 | $0.00457 |
| Opus 5.5 | $0.00032 | $0.00183 |
| Sonnet 5.5 | $0.00016 | $0.00091 |
| Haiku 4.5 | $0.00008 | $0.00046 |
Grade A, and why
state-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 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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 · 69 lines · 81 tokens per session scan A c3bfde4928d7
state-prompt is a skill published in the GitHub repository mskayyali/Stateful (4 stars, last pushed 2mo ago), with no licence file. It adds 81 tokens to every session and 457 once invoked, about $0.0003 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 2026-08-31.
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.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.
ai-interaction-patterns
AI UX patterns — prompt UX, wayfinding, HITL, trust, disclosure, memory, generative UI.
prompt-ux-design
Use when designing the user-facing prompt experience for any AI feature. Covers input design, suggestion patterns, history, feedback signals, and the interaction model between user intent and model execution.
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-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.