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Health Data

Listed
glebis·vlatest·database·MIT

Analyzes patient health records to identify trends, anomalies, and potential risks based on provided data.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add glebis/claude-skills --skill health-data -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

glebis

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 4 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: hl7.org, loinc.org, terminology.hl7.org, unitsofmeasure.org.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    hl7.orgundeclaredterminology.hl7.orgundeclaredloinc.orgundeclaredunitsofmeasure.orgundeclared

    What it found (3 of 23)

    • health-data/references/fhir_mappings.md:41contacts hl7.org
      "profile": ["http://hl7.org/fhir/StructureDefinition/vitalsigns"]
    • health-data/references/fhir_mappings.md:46contacts terminology.hl7.org
      "system": "http://terminology.hl7.org/CodeSystem/observation-category",
    • health-data/references/fhir_mappings.md:100contacts terminology.hl7.org
      "system": "http://terminology.hl7.org/CodeSystem/observation-category",

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned glebis/claude-skills at 7524dff0c5.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7524dff0c5
Manifest
health-data/SKILL.md
Scanned
5 files, 1,595 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to query and analyze health data stored in a local SQLite database containing over 6 million records across 43 different health metrics. It provides both pre-built Python scripts for common queries and allows for direct SQL execution for more customized analysis. The skill can retrieve daily summaries, weekly trends, sleep patterns, recent vital signs, activity ring progress, and workout history from the provided data.

When to use it

  • To understand your fitness trends over time (e.g., weekly steps or exercise minutes).
  • To review recent health metrics like heart rate and blood oxygen levels.
  • To analyze sleep patterns and identify potential issues.
  • To track workout history, including duration, distance, and calories burned for specific activities like running.
  • To generate reports summarizing daily activity and progress towards fitness goals.

Key capabilities

  • Data Source: Accesses health data from a local SQLite database (~/data/health.db).
  • Pre-built Python Scripts: Offers scripts for common queries (daily summary, weekly trends, sleep analysis, latest vitals, activity rings, workout history).
  • Custom SQL Queries: Allows direct execution of SQL queries against the database.
  • Output Formats: Supports output in Markdown, JSON, FHIR and ASCII formats.
  • Query Examples: Provides pre-defined queries for daily summaries, weekly trends, sleep analysis, latest vitals, activity rings, and workout history.

Example prompts

  • "Get my daily health summary."
  • "Show me my weekly trends in exercise minutes over the last 4 weeks as JSON."
  • "Analyze my sleep patterns for the past two weeks."
  • "What are my latest vital readings?"
  • "Give me a markdown report of my running workout history from the last 30 days."

Tips & gotchas

  • Database Location: The skill requires access to the ~/data/health.db database file. Ensure this path is accessible.
  • SQL Knowledge (Optional): While pre-built scripts are available, custom queries require familiarity with SQL syntax. Refer to references/schema.md for table structures and query templates.
  • Output Format: Specify the desired output format (markdown, json, fhir, ascii) using the --format flag in the Python script commands.