Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AlpacaLabsLLC/skills-for-architectsnpx agentmods add skills/alpacalabsllc/skills-for-architects/studio-feedbackWrote 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/alpacalabsllc/skills-for-architects/studio-feedback)<a href="https://agentmods.dev/skills/alpacalabsllc/skills-for-architects/studio-feedback"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/studio-feedback/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/alpacalabsllc/skills-for-architects/studio-feedback"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/studio-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00061 | $0.01273 |
| Opus 5.5 | $0.00024 | $0.00509 |
| Sonnet 5.5 | $0.00012 | $0.00255 |
| Haiku 4.5 | $0.00006 | $0.00127 |
Grade A, and why
studio-feedback 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/as:studio-feedback — Prepare a GitHub report
Before acting, read the host contract and this component's declaration (skill:studio-feedback). Load only its referenced mode profiles from the shared catalog. Compose modes required by the actual task; declarations are requirements, not proof of access or permission.
Host adapter: read delivery-specific guidance for invocation, metadata access, questions and browser opening.
This native report-preparation procedure has no registered operation IDs. Its steps below are the complete owner; no Arch Studio runner, installer, executable download or reconstructed helper is required. An ordinary native encoding or metadata tool may be used where available. Follow the completion contract and distinguish a prepared draft, an opened URL and a submitted issue.
Prepare a report locally, show exactly what would leave the machine, and open GitHub only after one informed confirmation. Never submit the issue, post a comment, call the GitHub API, or transmit diagnostics in the background.
1. Choose the report type
Infer bug or feature when the request is clear. Otherwise use one AskUserQuestion gate to choose the type; do not first ask the same question in prose.
Use these issue forms and labels:
- Bug:
bug-report.yml, labelbug; fieldsversion,os,skill,what-happened,expected. - Feature:
feature-request.yml, labelenhancement; fieldsversion,skill,problem,proposal.
2. Prepare minimal fields locally
For MCP, use the actual delivered release version and digest when present; a connection does not imply a local plugin path. For an installed plugin, read its actual .codex-plugin/plugin.json on Codex or .claude-plugin/plugin.json on Claude Code through the host's known package location. Gather only the operating-system name/version through available native host metadata or a bounded local command. Process access is optional and depends on that method. If metadata is unavailable, say unknown rather than searching unrelated files, installing tools or inventing it. Infer the affected skill from the conversation when reliable; otherwise leave it blank or ask during editing.
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 Changed · +12 lines 4672d25ddbfc
- 1mo ago First seen · 75 lines · 61 tokens per session scan A 6620966692c0
studio-feedback is a skill published in the GitHub repository AlpacaLabsLLC/skills-for-architects (395 stars, last pushed 7d ago), licensed MIT. It adds 61 tokens to every session and 1,273 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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