medical-case-interpreter
Generates compliant medical case report articles for WeChat.
Best use case
medical-case-interpreter is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generates compliant medical case report articles for WeChat.
Teams using medical-case-interpreter should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/medical-case-interpreter/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How medical-case-interpreter Compares
| Feature / Agent | medical-case-interpreter | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generates compliant medical case report articles for WeChat.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
## When to Use
- Use this skill when the request matches its documented task boundary.
- Use it when the user can provide the required inputs and expects a structured deliverable.
- Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.
## Key Features
- Scope-focused workflow aligned to: Generates compliant medical case report articles for WeChat.
- Packaged executable path(s): `scripts/validate_skill.py`.
- Structured execution path designed to keep outputs consistent and reviewable.
## Dependencies
- `Python`: `3.10+`. Repository baseline for current packaged skills.
- `Third-party packages`: `not explicitly version-pinned in this skill package`. Add pinned versions if this skill needs stricter environment control.
## Example Usage
```bash
cd "20260316/scientific-skills/Others/medical-case-interpreter"
python -m py_compile scripts/validate_skill.py
python scripts/validate_skill.py --help
```
Example run plan:
1. Confirm the user input, output path, and any required config values.
2. Edit the in-file `CONFIG` block or documented parameters if the script uses fixed settings.
3. Run `python scripts/validate_skill.py` with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.
## Implementation Details
- Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
- Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
- Primary implementation surface: `scripts/validate_skill.py`.
- Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
- Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.
## Validation Shortcut
Run this minimal command first to verify the supported execution path:
```bash
python scripts/validate_skill.py --help
```
# Medical Case Interpreter
## Description
This skill acts as a **Senior Medical Expert** to transform raw clinical data into a professional WeChat case report article. It enforces strict privacy, formatting, and style rules without requiring external scripts.
## Inputs
* **Case Data**: Raw text or files containing patient history, exams, and treatment.
* **Product Name**: The target drug/product to highlight.
* **Doctor Info**: Name and affiliation of the expert.
* **Drug Mapping**: (Optional) Specific Chinese translations for English drug names.
## Generation Rules
When executing this skill, the Agent must follow these steps and constraints:
### Step 1: Privacy & Data Cleaning (Mental Check)
* **Strictly Anonymize**: Convert all patient names to "Patient (Gender, Age)".
* **Remove Dates**: Convert specific admission dates to relative time (e.g., "Day 1", "Month 3").
* **Hospital Info**: Do not mention specific hospital ID numbers.
### Step 2: Content Generation
Generate the article in a single pass using the following structure:
#### 1. Title
* Create an engaging, professional title suitable for a medical audience.
#### 2. Foreword
* **Constraint**: Strictly **under 200 words**.
* **Format**: Single paragraph.
* **Content**: Summarize the case significance and hook the reader. Do not reveal the full outcome yet.
#### 3. Case Report Body
* **Header**: Start with `## Case Report`.
* **Structure**:
* `### Patient Information`: History, Symptoms (Anonymized).
* `### Auxiliary Examination`: Key lab results and imaging.
* `### Diagnosis`: Clear clinical diagnosis.
* `### Treatment Process`: Detail the regimen. **Highlight the [Product Name]** usage here.
* `### Summary`: Clinical takeaways.
* **Drug Names**: If [Drug Mapping] is provided, ALWAYS use the specified Chinese names.
## Example Prompt for User
"Please generate a case report for **[Product Name]** based on the attached file. The doctor is **[Doctor Name]**."
## When Not to Use
- Do not use this skill when the required source data, identifiers, files, or credentials are missing.
- Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
- Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.
## Required Inputs
- A clearly specified task goal aligned with the documented scope.
- All required files, identifiers, parameters, or environment variables before execution.
- Any domain constraints, formatting requirements, and expected output destination if applicable.
## Recommended Workflow
1. Validate the request against the skill boundary and confirm all required inputs are present.
2. Select the documented execution path and prefer the simplest supported command or procedure.
3. Produce the expected output using the documented file format, schema, or narrative structure.
4. Run a final validation pass for completeness, consistency, and safety before returning the result.
## Output Contract
- Return a structured deliverable that is directly usable without reformatting.
- If a file is produced, prefer a deterministic output name such as `medical_case_interpreter_result.md` unless the skill documentation defines a better convention.
- Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.
## Validation and Safety Rules
- Validate required inputs before execution and stop early when mandatory fields or files are missing.
- Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
- Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
- Keep the output safe, reproducible, and within the documented scope at all times.
## Failure Handling
- If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
- If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
- If partial output is returned, label it clearly and identify which checks could not be completed.
## Quick Validation
Run this minimal verification path before full execution when possible:
```text
No local script validation step is required for this skill.
```
Expected output format:
```text
Result file: medical_case_interpreter_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any
```
## Deterministic Output Rules
- Use the same section order for every supported request of this skill.
- Keep output field names stable and do not rename documented keys across examples.
- If a value is unavailable, emit an explicit placeholder instead of omitting the field.
## Completion Checklist
- Confirm all required inputs were present and valid.
- Confirm the supported execution path completed without unresolved errors.
- Confirm the final deliverable matches the documented format exactly.
- Confirm assumptions, limitations, and warnings are surfaced explicitly.Related Skills
medical-unit-converter
Convert medical laboratory values between units (mg/dL to mmol/L, etc.) with formula transparency and clinical reference ranges. Supports glucose, cholesterol, creatinine, and hemoglobin conversions.
medical-case-report-generator
Generates a patient-friendly medical case report tweet from case images and disease name. Use when the user provides a medical case image and wants a structured report or tweet.
unstructured-medical-text-miner
Mine unstructured clinical text from MIMIC-IV to extract diagnostic logic.
fastqc-report-interpreter
Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
case-control-study-quality-assessment-nos
Clinical Research Bias Assessment - Case-Control Study (NOS) v2.3.0. Use when you need to assess the bias of a case-control study using the Newcastle-Ottawa Scale (NOS) criteria, or when evaluating the quality of a medical paper.
usmle-case-generator
Generate USMLE Step 1/2 style clinical cases with patient history, physical.
medical-translation
Use medical translation for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.
medical-scribe-dictation
Convert physician verbal dictation into structured SOAP notes. Trigger.
medical-email-polisher
Transforms rough email drafts into polished, professional medical correspondence.
medical-device-mdr-auditor
Audit medical device technical files against EU MDR 2017/745 regulations.
medical-research-gap-to-study-planner
Converts an audited medical research gap into a complete, structured, gap-traceable study design. Always use this skill whenever a user already has one or more candidate research gaps and wants to transform them into an executable biomedical research plan rather than re-run broad topic ideation. Covers six gap-to-design patterns (evidence-completion, mechanism-resolution, cell-state/context-mapping, translation-bridge, causality-upgrade, population/stage-specific) and always outputs one recommended primary protocol, a gap-to-design dependency map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and verified design-support literature rules. Never fabricate references. Preserve claim-evidence discipline and do not replace a topic-specific gap with a generic workflow.
medical-research-algorithm-matcher
Matches a user’s biomedical research direction, disease problem, study aim, data modality, and resource constraints to the most relevant recent algorithms and method papers. Always search real recent algorithm literature first, prioritize the last 12 months, expand to 1–3 years only when needed, and add canonical baselines only when necessary. Every formal algorithm recommendation must include the verified primary method paper, plus published downstream papers that actually cite/use the algorithm when such papers are found, with DOI when available. Never fabricate papers, algorithm names, authors, journals, years, DOI, PMID, links, or benchmark claims. If no directly verified algorithm paper is found, say so explicitly.