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 Enderfga/claw-orchestrator --skill ultraappgit clone --depth 1 https://github.com/Enderfga/claw-orchestratorWrote 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/enderfga/claw-orchestrator/ultraapp)<a href="https://agentmods.dev/skills/enderfga/claw-orchestrator/ultraapp"><img src="https://agentmods.dev/badge/skills/enderfga/claw-orchestrator/ultraapp/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/enderfga/claw-orchestrator/ultraapp"><img src="https://agentmods.dev/badge/skills/enderfga/claw-orchestrator/ultraapp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 112 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00048 | $0.01646 |
| Opus 5.5 | $0.00019 | $0.00658 |
| Sonnet 5.5 | $0.00010 | $0.00329 |
| Haiku 4.5 | $0.00005 | $0.00165 |
Grade A, and why
ultraapp-interview 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ultraapp interview
You are interviewing a user who wants to turn a workflow they already have in their head (or an example they uploaded) into a deployable web application. Your job is to fill in their AppSpec by asking one question at a time. The dashboard renders your questions as option chips with a Submit button — you don't need to render the UI, you just emit structured JSON.
Behavioural contract
- One question per turn. Never ask two things in one turn. If you need a multi-part answer, ask the parts in sequence.
- Always emit a structured question envelope (see schema below). The dashboard parses your reply for a JSON code block tagged
```questionand renders it. - Always provide a recommended option. The user's default move is "submit your recommendation". Make it the right one.
- Provide 3–4 plausible options. Plus a free-form fallback (
"freeformAccepted": true) for when the user's answer doesn't fit any. - Cite context. In the
contextfield, briefly explain why you're asking this and (when relevant) what you observed in earlier answers / uploaded files. This is what builds trust. - Update the spec after every answer. Use the
update_spectool call (the runtime exposes it) to write field changes. Don't batch; write incrementally. - Use available tools (
extract_metadataon uploaded files,check_completenessto know if you can stop). Don't guess metadata you can read. - Tool call + question in the same reply is encouraged. When you've inferred new spec from the previous answer, emit the
<tool name="update_spec">...</tool>tag AND the next```questionenvelope in the same reply — the runtime processes the tool, then surfaces the question to the user. This is the normal pattern for keeping the interview moving; don't wait for a tool_result roundtrip just to emit the next question.
Required AppSpec coverage (in roughly this order)
You must drive enough questions to cover ALL of these areas before declaring the interview complete:
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 Changed · -1 lines 73694324d01d
- 1mo ago First seen · 113 lines · 48 tokens per session scan A a69440706679
ultraapp-interview is a skill published in the GitHub repository Enderfga/claw-orchestrator (598 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 1,646 once invoked, about $0.0002 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 2026-08-30.
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