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 Jarzembak/calibremcp --skill reading_recommendationsgit clone --depth 1 https://github.com/Jarzembak/calibremcpWrote 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/jarzembak/calibremcp/reading_recommendations)<a href="https://agentmods.dev/skills/jarzembak/calibremcp/reading_recommendations"><img src="https://agentmods.dev/badge/skills/jarzembak/calibremcp/reading_recommendations/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/jarzembak/calibremcp/reading_recommendations"><img src="https://agentmods.dev/badge/skills/jarzembak/calibremcp/reading_recommendations.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 | $0.00000 | $0.00481 |
| Opus 5 | $0.00000 | $0.00241 |
| Sonnet 5 | $0.00000 | $0.00096 |
| Haiku 4.5 | $0.00000 | $0.00048 |
Grade A, and why
reading_recommendations 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.
This is a copy
100% identical to reading_recommendations — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reading Recommendations
Description: Get personalized reading recommendations from your Calibre library using series progress, ratings, tags, reading history, and collaborative filtering across similar books.
Trigger Phrases
- "What should I read next?"
- "Recommend a book like [title]"
- "What's good in my unread [genre]?"
- "Suggest something from my TBR pile"
- "Find my next series to start"
- "What have I been neglecting?"
Tools
query_books(sort="rating", unread=True, tags=[...], limit=20)— Find top-rated unread books in a genre.manage_analysis(operation="reading_stats")— Reading statistics: completion rate, genre distribution, pages read, author diversity.manage_analysis(operation="series_progress")— Series tracking: which series are started but unfinished, next-in-series ordering.manage_metadata(operation="show", book_id=...)— Get full metadata for a candidate book: description, rating, tags, series position.calibre_metadata_search(query="similar to [title]")— Semantic similarity search using LanceDB embeddings.
Workflow
- Profile the reader: Call
manage_analysis(operation="reading_stats")to understand reading patterns, preferred genres, and completion behavior. - Surface candidates: Use
query_books(unread=True, sort="rating", tags=[preferred_genre])to get top-rated unread books. Combine multiple tag filters for precision. - Series catch-up: Call
manage_analysis(operation="series_progress")to find series with book 1 read but book 2+ unread — these are high-confidence recommendations. - Similarity match: For "like this book" queries, use
calibre_metadata_search()with a descriptive query of the source book's themes. - Rank and present: Score candidates by (rating + recency + series_position). Present top 3-5 with reasoning: why this matches the reader's taste, what tags overlap.
Example
"Recommend my next read from unread fantasy with high ratings." → query_books(tags=["fantasy"], unread=True, sort="rating", limit=30) → filter top 5 → manage_analysis(operation="series_progress") to prioritize continuations → present with per-book reasoning.
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 · 33 lines · 0 tokens per session scan A 6114b44d32f6
reading_recommendations is a skill published in the GitHub repository Jarzembak/calibremcp (0 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 481 tokens. A static security scan graded it A with 0 findings. It is 100% identical to reading_recommendations, differing in 0 lines, and is treated as a copy.
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