Healthcare Claims Analytics logo

Healthcare Claims Analytics

Organization
Amazon-Quick
healthcare-claims-analytics

Provide deterministic, copy-paste-ready Python and SQL for healthcare claims processing and billing-integrity screening. Use when asked to 'parse X12 837/835 claims', 'profile provider E&M billing', 'detect upcoding', 'validate NCCI edits', 'find duplicate claims', 'run Benford's Law on charges', 'flag impossible billing days', or any claims analytics or outlier-detection task

Overview

PublisherAmazon-Quick
RepositoryAmazon-Quick-official-catalog
Skill namehealthcare-claims-analytics
Stars
50
Forks
4
Bundled files
8
LicenseMIT-0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Amazon-Quick on GitHub. Read the source before you install it.

Installation

Install the Healthcare Claims Analytics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/healthcare/healthcare-claims-analytics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Amazon-Quick/Amazon-Quick-official-catalog.git /tmp/Amazon-Quick-official-catalog
mkdir -p .claude/skills
cp -r /tmp/Amazon-Quick-official-catalog/skills/healthcare/healthcare-claims-analytics .claude/skills/healthcare-claims-analytics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Healthcare Claims Analytics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Healthcare Claims Analytics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Healthcare Claims Analytics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Overview

Deliver deterministic, copy-paste-ready Python and SQL for healthcare claims data processing and billing-integrity screening: X12 837/835 parsing, procedure and diagnosis code manipulation, provider billing profiling, Evaluation and Management (E&M) upcoding detection, National Correct Coding Initiative (NCCI) edit validation, duplicate claim identification, Benford's Law charge analysis, and impossible-day detection. Each task has a self-contained reference file holding the working code, its parameters, and its known pitfalls. Use this skill when a user needs correct code for one of these tasks, optionally run against their own data.

Workflow

TaskTrigger examplesReference file
X12 837 parsingparse claim file, read 837P, extract claimsreferences/x12-parsing.md
E&M provider profilingupcoding, 99214/99215 rate, z-score vs peersreferences/em-profiling.md
NCCI edit validationcode pair edits, bundling, modifier 59references/ncci-validation.md
Duplicate claim detectionexact and near duplicates, resubmissionsreferences/duplicate-detection.md
Benford's Law analysisfirst-digit test, charge distributionreferences/benfords-law.md
Impossible day detection>24 hours billed, time-based service sumsreferences/impossible-days.md
Parameter tuningthresholds, defaults, sensitivityreferences/parameters-and-pitfalls.md
Domain pitfallswhy a signal is a false positivereferences/parameters-and-pitfalls.md
</Definition - Analysis Tasks>

<Workflow - Deliver Claims Analytics Code description="Identify the requested claims analysis task, load its reference code, and deliver a working example, optionally running it against the user's data." tools=[file_read, run_python] triggers=["User asks to parse X12/837/835 claims", "User asks to profile provider billing or detect upcoding", "User asks to validate NCCI edits", "User asks to find duplicate claims", "User asks to run Benford's Law on charges", "User asks to flag impossible billing days", "User asks about claims analytics thresholds or pitfalls"]

  1. [Decide] Match the request to a task in <Definition - Analysis Tasks>. Validate: Exactly one task is selected. If fails: If the request maps to more than one task, handle them in sequence. If it maps to none, [Ask user] which of the supported tasks they want.

  2. [Agent] Read the reference file for the selected task with file_read. Validate: File content loaded and contains the code block for the task. If fails: Re-read using the path from <Definition - Analysis Tasks>. If still missing, tell the user the reference is unavailable and stop.

  3. [Ask user] Confirm the input the code expects: file path for X12 parsing, or the DataFrame columns for the tabular tasks (each reference file lists the columns it needs). Skip if the user already stated their inputs. Validate: The user has confirmed inputs, or the task needs none. If fails: Ask once more, naming the exact fields the code requires.

  4. [Agent] Deliver the answer using the template: confirm inputs, then the working code, then the key parameters, then the pitfalls that apply to this task (drawn from the reference file and ). Validate: Output leads with code and names every environment-specific parameter the user must set. If fails: Re-order so code comes first and add any missing parameter.

  5. [Decide] Does the user want the code run against their own data now?

    • Yes -> continue to step 6.
    • No -> state the liability disclaimer from Rule 7 and stop.
  6. [Agent] Run the code against the user's data with run_python, keeping all data in the session per Rule 1. Return the resulting rows or summary. Validate: The code runs without error and returns a result. If fails: Report the error and the offending input, correct the code or ask the user to fix the data, then re-run once.

  7. [Agent] Close with the liability disclaimer from Rule 7. Validate: The disclaimer is present in the final message. If fails: Add it before ending.

</Workflow - Deliver Claims Analytics Code>

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Healthcare Claims Analytics AI skill do?

Provide deterministic, copy-paste-ready Python and SQL for healthcare claims processing and billing-integrity screening. Use when asked to 'parse X12 837/835 claims', 'profile provider E&M billing', 'detect upcoding', 'validate NCCI edits', 'find duplicate claims', 'run Benford's Law on charges', 'flag impossible billing days', or any claims analytics or outlier-detection task

Why use Healthcare Claims Analytics on TypingMind?

Because you install it once and use it with any model. Healthcare Claims Analytics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Healthcare Claims Analytics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/healthcare/healthcare-claims-analytics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Healthcare Claims Analytics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Healthcare Claims Analytics?

As many as you like. As long as a model supports skills, you can use Healthcare Claims Analytics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Healthcare Claims Analytics AI skill free?

Yes. It is published on GitHub by Amazon-Quick under the MIT-0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇