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 AI-Riksarkivet/ra-mcp --skill feedback-lsgit clone --depth 1 https://github.com/AI-Riksarkivet/ra-mcpWrote 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/ai-riksarkivet/ra-mcp/feedback-ls)<a href="https://agentmods.dev/skills/ai-riksarkivet/ra-mcp/feedback-ls"><img src="https://agentmods.dev/badge/skills/ai-riksarkivet/ra-mcp/feedback-ls/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/ai-riksarkivet/ra-mcp/feedback-ls"><img src="https://agentmods.dev/badge/skills/ai-riksarkivet/ra-mcp/feedback-ls.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 Privilege Escalation · line 189 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00066 | $0.01650 |
| Opus 5 | $0.00033 | $0.00825 |
| Sonnet 5 | $0.00013 | $0.00330 |
| Haiku 4.5 | $0.00007 | $0.00165 |
Grade A, and why
feedback-ls 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 9d 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Label Studio Feedback Workflow
Send document pages to Label Studio for human annotation and feedback.
Tool
label_import_to_label_studio— Import pages to a Label Studio project. Two modes:- With ALTO: pre-annotated tasks with polygons and transcriptions
- Images only: blank tasks for annotation from scratch
When to Use
- User asks to send pages for human review or annotation
- User wants to flag transcription or segmentation issues
- User wants to create Label Studio tasks from browse results
- User wants to send images for annotation from scratch
- User asks for quality assurance on specific pages
Two Modes
Mode 1: Pre-annotated (with ALTO XML)
Use when pages already have AI transcriptions. The tool fetches ALTO XML, extracts text line polygons and transcriptions, and creates VectorLabels pre-annotations.
{
"image_urls": [
"https://lbiiif.riksarkivet.se/arkis!30002056_00004/full/max/0/default.jpg"
],
"alto_urls": [
"https://sok.riksarkivet.se/dokument/alto/SE_RA_30002056_00004"
],
"feedback": [["Transcription"]]
}
Mode 2: Images only (no ALTO)
Use when there are no existing transcriptions — the user wants to annotate
from scratch. Just pass image URLs, omit alto_urls.
{
"image_urls": [
"https://lbiiif.riksarkivet.se/arkis!30002056_00004/full/max/0/default.jpg",
"https://lbiiif.riksarkivet.se/arkis!30002056_00005/full/max/0/default.jpg"
]
}
Choosing the Right Source for Pre-annotations
When the user wants to send pages with pre-annotations, choose the source based on what's available:
| Situation | Source | How |
|---|---|---|
Pages from browse_document results |
ALTO XML from Riksarkivet | Pass alto_urls from browse output |
| User has images without transcriptions | No pre-annotations | Omit alto_urls (image-only mode) |
| User has images and wants AI transcription first | HTR transcription | Use htr_transcribe first to generate ALTO XML, then pass the ALTO export URLs as alto_urls |
| User wants to label/annotate themselves | No pre-annotations | Omit alto_urls — blank tasks for manual annotation |
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.
- 9d ago First seen · 193 lines · 66 tokens per session scan A 79bbd692fe8d
feedback-ls is a skill published in the GitHub repository AI-Riksarkivet/ra-mcp (22 stars, last pushed 2d ago), licensed Apache-2.0. It adds 66 tokens to every session and 1,650 once invoked, about $0.0003 per session on Opus 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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