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 jqaisystems/jqai-ai-skills --skill local-app-public-demogit clone --depth 1 https://github.com/jqaisystems/jqai-ai-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/jqaisystems/jqai-ai-skills/local-app-public-demo)<a href="https://agentmods.dev/skills/jqaisystems/jqai-ai-skills/local-app-public-demo"><img src="https://agentmods.dev/badge/skills/jqaisystems/jqai-ai-skills/local-app-public-demo/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/jqaisystems/jqai-ai-skills/local-app-public-demo"><img src="https://agentmods.dev/badge/skills/jqaisystems/jqai-ai-skills/local-app-public-demo.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 | $0.00090 | $0.01105 |
| Opus 5.5 | $0.00036 | $0.00442 |
| Sonnet 5.5 | $0.00018 | $0.00221 |
| Haiku 4.5 | $0.00009 | $0.00111 |
Grade A, and why
local-app-public-demo 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.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local App to Public Demo
Someone wants to see what you built. Installing it is too much to ask, and the app cannot deploy as-is because it writes to disk, shells out, or depends on a local session.
The answer is not a rewrite and not a screen recording. It is a static snapshot: the same frontend, served against frozen copies of its own API responses, with writing turned off honestly.
This skill covers building that snapshot and, more importantly, verifying it is
safe to make public. Pair it with github-safe-publisher when the source code is
also going out.
When to use
- Showing a local tool to a client, collaborator, or prospective employer.
- A server app that cannot run on static hosting as-is.
- Publishing a portfolio piece from something that normally runs on one machine.
- Verifying an existing demo build carries no private data.
The core decision: one codebase, not a fork
Copy the app into a demo/ folder and edit it there and the two drift within a
week. Instead:
- Ship a tiny config file in the real app, for example
demo-config.js, holding a single flag set to false. - The build copies the frontend verbatim and overwrites only that one file with the flag set to true.
- The frontend branches on the flag in exactly one place: its data layer.
Everything else stays identical, which means fixing a bug fixes it in both.
Workflow
1. Inventory what the frontend reads
List every endpoint it fetches. That list is the contract. Anything missed becomes a blank page in the demo with no error.
2. Freeze responses as static files
With the real app running, fetch each endpoint and write it as JSON at a path the demo can reach. Map API routes to file paths with one small pure function, so the mapping is testable and obvious.
3. Make writes refuse honestly
Route reads and writes through one data-layer object. In demo mode:
- Reads fetch the frozen JSON and return null on a miss.
- Writes return a friendly refusal instead of calling the network.
What ships with it
2 files 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.
- 1mo ago First seen · 115 lines · 90 tokens per session scan A df73cf4c93e4
local-app-public-demo is a skill published in the GitHub repository jqaisystems/jqai-ai-skills (3 stars, last pushed 13d ago), licensed MIT. It adds 90 tokens to every session and 1,105 once invoked, about $0.0004 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.
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