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Document insights

Skill dzivkovi/agent-skills-on-databricks/skills/document-insights

Bring your Claude Code skill unchanged and run it as a governed Databricks job. Publish once to a Unity Catalog volume, reuse from any job, chain skills into a pipeline (markdown in, branded PowerPoint out). No external API key. Runs on Free Edition.

Install
npx -y skills add dzivkovi/agent-skills-on-databricks --skill document-insights

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 22 days oldThe repository was created 22 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Analyze a text document and produce an insights report that pairs EXACT, code-computed metrics (word/character/sentence counts, reading time) with an LLM's interpretive read (sentiment, one-line summary, key themes). Use when you need a grounded document summary where the numbers must be correct, not estimated. Triggers: 'analyze this document', 'document insights', 'sentiment and word count', 'summarize with stats'.

SKILL.md

2.2 KB, as published. Nobody here has run it

Document Insights

This skill demonstrates the core idea of an Agent Skill: pair DETERMINISTIC code (exact facts an LLM cannot reliably compute) with NON-DETERMINISTIC LLM reasoning (judgment an LLM is good at), and keep the two clearly separated in the output.

How to run this skill

  1. Deterministic step - run scripts/analyze.py <input-file>. It returns a JSON object of exact metrics: word_count, char_count, char_count_no_spaces, line_count, sentence_count, avg_words_per_sentence, reading_time_min, longest_word. These numbers are ground truth. Never let the LLM recompute or "estimate" them.
  2. Non-deterministic step - ask the model for an interpretive read of the document: overall sentiment (positive / neutral / negative) with a one-phrase justification, a single-sentence summary, and 2-3 key themes. The model must ground its answer in the document text and may reference the exact metrics from step 1 (e.g. "a short 42-word note"), but must never invent or restate counts as if it computed them.

Output contract

Produce markdown with two clearly-labeled sections so a reader can see the seam:

  • Metrics (computed by code - exact) - a table of the numbers from analyze.py.
  • Reading (interpreted by the LLM) - sentiment, one-sentence summary, key themes.

Use hyphens, not em-dashes. Keep it concise.

Why this split matters

An LLM asked "how many words is this?" will guess, and often guess wrong. analyze.py counts them exactly. Conversely, code cannot judge tone; the LLM can. The skill gets the best of both by letting each do what it is good at - and by labeling which is which, the output never passes an LLM guess off as a hard fact.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.