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 AlpacaLabsLLC/skills-for-architects --skill studio-feedbackgit clone --depth 1 https://github.com/AlpacaLabsLLC/skills-for-architectsWrote 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 | $0.00940 |
| Opus 5 | $0.00030 | $0.00470 |
| Sonnet 5 | $0.00012 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
studio-feedback scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Build the URL from `https://github.com/AlpacaLabsLLC/skills-for-architects/issues/new`, the selected `template`, title, label, and the form field IDs above. Percent-encode every value with `jq` `@uri`; use Python 3 `urll How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/as:studio-feedback — Prepare a GitHub report
Harness note: use
/as:<skill>on Claude Code and$<skill>on Codex. Resolve<skill-root>as the directory containing this loadedSKILL.mdand<plugin-root>as the plugin root that containsskills/, and use equivalent native tools when host tool names differ.
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
Read the installed version from <plugin-root>/.codex-plugin/plugin.json on Codex or <plugin-root>/.claude-plugin/plugin.json on Claude Code. Gather the operating-system name and version with local commands only. Infer the affected skill from the conversation when reliable; otherwise leave it blank or ask during editing.
Draft the report from the user's words, but do not automatically include raw conversation history, files, stack traces, environment variables, or command output. Before showing the draft, remove or visibly flag:
- client and project names;
- street and project addresses;
- home-directory paths and usernames;
- email addresses and phone numbers;
- likely secrets, tokens, keys, cookies, and credentials; and
- proprietary document contents.
When uncertain, omit the value and mark where the user can add a safe description. Never invent reproduction details.
3. Show the exact outbound values
Present every proposed query field verbatim in one fenced text block. Explain immediately before the gate:
What ships with it
1 file 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.
- 11d 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 (351 stars, last pushed 7d ago), licensed MIT. It adds 61 tokens to every session and 940 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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