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open-notebook

Organizes research with the self-hosted Open Notebook alternative to NotebookLM. Supports source ingestion (PDFs, web pages, audio, video, and Office documents), cited document chat, text and vector search, notes, custom transformations, and multi-speaker podcasts. Use when automating Open Notebook through its REST API or configuring its local or cloud AI providers, including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral.

76

Quality

96%

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SecuritybySnyk

Low

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A high-quality skill body: lean, contract-dense, and immediately actionable, with explicit validation checkpoints and error-recovery guidance across ingestion, chat, search, batch listing, and destructive deletion, and a clean overview-plus-verified-references structure. The only notable weakness is inline time-sensitive version/date provenance that would fit better in a dedicated versioning section.

DimensionReasoningScore

Conciseness

The body is lean and dense with hard-won contract detail on nearly every line ("async_processing and embed both default to false. The fields text and process_async do not implement these options"), with no explanations of concepts Claude already knows. It falls short of the score-5 anchor only because time-sensitive version and date information ("targets the latest published release observed on 2026-09-30, v1.14.0", a pinned commit hash, and the v1.14.0-vs-main search scoping note) sits inline rather than in a dedicated version/deprecated section.

4 / 5

Actionability

Guidance is fully executable: copy-paste curl and docker compose commands, and a Python quick-start block whose imports and signatures (create_notebook, add_text_source, wait_for_processing, build_context, create_chat_session, send_chat_message) match the bundled scripts exactly, including a concrete validation raise. Endpoint paths, form fields, and enumerated model_type values make the remaining workflows concrete; the common cases are covered by runnable code.

5 / 5

Workflow Clarity

The ingest-embed-context-chat sequence is clearly ordered with explicit validation checkpoints at each stage ("Wait for /api/sources/{id}/status; a failed job must not flow into analysis", "Inspect full_text after extraction", "embedded_chunks > 0", "Inspect /delete-preview before intentional deletion", "poll its job with a deadline", "Review returned items and token_count"), and error recovery for batch/destructive operations is addressed ("Discard partial results after an error; raise max_pages explicitly"). This satisfies the feedback-loop requirement rather than capping at 3, and exceeds the score-4 anchor, which allows minor validation gaps.

5 / 5

Progressive Disclosure

The body is a genuine overview that pushes bulk detail to a real, one-level-deep bundle: [configuration](references/configuration.md), [API reference](references/api_reference.md), [worked examples](references/examples.md), and [architecture](references/architecture.md) all exist (verified against the file listing) and are contextually signaled, with API contracts summarized inline and the 19KB reference kept out of the main file. This matches the score-5 anchor of well-signaled, appropriately split, easy navigation.

5 / 5

Total

19

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplar description: third-person voice, comprehensive and concrete capability enumeration, an explicit 'Use when' clause with concrete triggers, and distinct anchoring to a named product plus its NotebookLM alternative. It reads like the rubric's good-overall examples.

DimensionReasoningScore

Specificity

The description lists comprehensive concrete actions: "source ingestion (PDFs, web pages, audio, video, and Office documents), cited document chat, text and vector search, notes, custom transformations, and multi-speaker podcasts" plus REST API automation and AI provider configuration. It matches the score-5 anchor (multiple specific concrete actions, comprehensive coverage) rather than score 4, which is for coverage with minor gaps.

5 / 5

Completeness

Both halves are explicit: a clear "what" ("Organizes research... Supports source ingestion (...), cited document chat, text and vector search, notes, custom transformations, and multi-speaker podcasts") and an explicit "Use when automating Open Notebook through its REST API or configuring its local or cloud AI providers" clause with concrete triggers. This exactly matches the score-5 anchor's structure and is well above the score-4 anchor, whose 'when' is only adequate.

5 / 5

Trigger Term Quality

Natural terms users would say are covered comprehensively: the product name "Open Notebook", the synonym "NotebookLM", "PDFs", "web pages", "audio, video", "Office documents", "podcasts", "REST API", and every provider name (OpenAI, Anthropic, Google, Ollama, Groq, Mistral). Score 4 was considered for lacking extension-style synonyms, but the breadth of distinct natural entry points (product, alternative, formats, providers) meets the comprehensive anchor.

5 / 5

Distinctiveness Conflict Risk

It names a specific self-hosted product and its well-known alternative ("the self-hosted Open Notebook alternative to NotebookLM"), and scopes provider configuration with "its local or cloud AI providers", so triggers route distinctly to this niche. It is not the score-4 case of overlap with closely related skills, since generic terms (providers, podcasts) are all anchored to the named product.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

Total

15

/

16

Passed

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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