Borrowing it
Nothing to install: this file belongs to webmaxru/is-ai-native. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/webmaxru/is-ai-native/main/.agents/skills/prompt-api/SKILL.mdgit clone --depth 1 https://github.com/webmaxru/is-ai-nativeWrote 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/webmaxru/is-ai-native/prompt-api)<a href="https://agentmods.dev/skills/webmaxru/is-ai-native/prompt-api"><img src="https://agentmods.dev/badge/skills/webmaxru/is-ai-native/prompt-api/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/webmaxru/is-ai-native/prompt-api"><img src="https://agentmods.dev/badge/skills/webmaxru/is-ai-native/prompt-api.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 | $0.00070 | $0.01251 |
| Opus 5.5 | $0.00028 | $0.00500 |
| Sonnet 5.5 | $0.00014 | $0.00250 |
| Haiku 4.5 | $0.00007 | $0.00125 |
Grade A, and why
prompt-api 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt API
Procedures
Step 1: Identify the integration surface
- Inspect the workspace for browser entry points, UI handlers, and any existing AI abstraction layer.
- Execute
node scripts/find-frontend-targets.mjs .to inventory likely frontend files and existing Prompt API usage when a Node runtime is available. - If a Node runtime is unavailable, inspect the nearest
package.json, HTML entry point, and framework entry files manually to identify the browser app boundary. - If the workspace contains multiple frontend apps, prefer the app that contains the active route, component, or user-requested feature surface.
- If the inventory still leaves multiple plausible frontend targets, stop and ask the user which app should receive the Prompt API integration.
- If the project is not a browser web app, stop and explain that this skill does not apply.
Step 2: Confirm Prompt API viability
- Read
references/prompt-api-reference.mdbefore writing code. - Read
references/examples.mdwhen the feature needs a spec-valid message shape for text, multimodal, prefix, or tool-enabled sessions. - Read
references/compatibility.mdwhen the feature must support multiple browser generations or decide between native support and polyfills. - Read
references/polyfills.mdwhen the feature needs concrete package installation or backend configuration examples for Prompt API or Task API polyfills. - Verify that the feature runs in a secure window context and that the
language-modelpermissions-policy allows access from the current frame. - If the integration must run in a Web Worker or other non-window context, stop and explain the platform limitation.
- Choose the session shape the feature needs:
prompt(),promptStreaming(),initialPrompts,append(),measureContextUsage(),tools, orresponseConstraint. - If the project uses TypeScript, add or preserve typings that cover the Prompt API surface used by the project.
Step 3: Implement a guarded session wrapper
- Read
assets/language-model-service.template.tsand adapt it to the framework, state model, and file layout in the workspace. - Gate session creation behind
LanguageModel.availability()using the same creation options that the feature will use at runtime, including expected modalities and tools. - Create sessions only after user activation when model download or instantiation may begin.
- Use
AbortControllerfor cancelable prompts and calldestroy()when the session is no longer needed. - If the feature runs in a cross-origin iframe, require
allow="language-model"on the embedding iframe. - Do not depend on
params(),topK, ortemperature; the spec marks them EXPERIMENTAL and extension-only, so portable web page integrations must not require them. - Treat
availability()as a passive capability check: if it reportsdownloadingbefore user activation, do not assume the current page initiated that download or lock the UI into an app-started busy state.
What ships with it
7 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 · 61 lines · 70 tokens per session scan A f9cef6805f09
prompt-api is a skill published in the GitHub repository webmaxru/is-ai-native (5 stars, last pushed 2mo ago), licensed MIT. It adds 70 tokens to every session and 1,251 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.
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