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Treatment-Plan Documentation Validator

Formats and structurally validates treatment-plan documentation for decisions already made and verified by licensed clinicians — fully offline.

Docs & OfficeAdvanced★ 33,030⑂ 3,248AI score 8/10Last updated: Aug 9, 2026

What it does

  • Accepts only clinician-authored, already-verified decisions and organizes them into six bounded JSON templates: source-fact manifest, intervention record, goals/monitoring checkpoints, shared-decision record, transition reconciliation, and intended-use handoff.
  • Runs deterministic local Python checks (validate_treatment_plan.py, validate_traceability.py, check_completeness.py, privacy_process_check.py, check_consistency.py) for format, provenance, missing fields, and internal consistency.
  • Builds a date schedule using only dates already present in the package — it never derives recurrence or clinical intervals.
  • Forces a visible "DRAFT — NOT MEDICAL ADVICE — DOCUMENTATION-ONLY — CLINICIAN SIGN-OFF REQUIRED" banner and keeps the release gate blocked until authorized human review completes.
  • Explicitly refuses diagnosis, therapy selection, dosing, titration, interaction/contraindication checks, triage, and prognosis.

Who it's for

  • Clinical documentation and health-information staff standardizing plan format and source traceability.
  • Quality/handoff teams formalizing transition checklists, ownership, and unresolved-item routing.
  • Healthcare software engineers prototyping documentation pipelines with synthetic data.

Example uses

  1. Create a synthetic package: python3 scripts/generate_template.py --output-dir ./local-plan-package --subject-ref SYNTHETIC-CASE-001 --classification synthetic to rehearse the workflow safely.
  2. Validate transcribed records: run the validation scripts in sequence to surface missing fields, absent source locators, and inconsistencies as rule codes and field paths.
  3. Generate a handoff schedule: use timeline_generator.py to emit explicit-date-schedule.json containing only explicitly supplied checkpoint and transition dates for a shift handoff review.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/treatment-plans/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on AI Skill Mart.
Copy the skills/treatment-plans folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/treatment-plans/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills && mkdir -p ~/.claude/skills && cp -r /tmp/scientific-agent-skills/skills/treatment-plans ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal (Terminal on macOS/Linux, WSL or Git Bash on Windows).
  2. Verify Python 3.11 or newer: python3 --version
  3. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  4. Create the skills folder: mkdir -p ~/.claude/skills
  5. Copy this skill only: cp -r scientific-agent-skills/skills/treatment-plans ~/.claude/skills/
  6. Restart Claude Code and ask something like "validate my treatment-plan documentation package" to trigger the skill.
  7. Before touching any patient-derived data, read references/privacy_governance.md and references/safety_scope.md, and follow your institution's authorized privacy and review policies.
View source on GitHub ↗License: MIT