A high-fidelity transcription and analysis tool that converts raw medical teleconference audio into structured, researcher-grade meeting minutes.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install callnote
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install callnote using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Healthcare Call Notes (Chinese Enhanced)?
The healthcare-call-notes-zh skill is an advanced processing engine within the Openclaw Skills ecosystem designed specifically for the pharmaceutical and medical industries. It addresses the high complexity of industry-specific terminology, financial metrics, and regulatory jargon found in earnings calls, expert interviews, and strategic healthcare meetings. Unlike generic summarizers, this skill focuses on high integrity and completeness, ensuring that critical data points like drug names, clinical trial codes, and financial growth rates are preserved without loss of accuracy.
By prioritizing the removal of oral fillers while maintaining the precise logical chain of management statements, this skill produces standardized documentation suitable for professional research archives. It bridges the gap between messy raw transcripts and actionable intelligence, making it an essential tool for investment researchers, medical analysts, and healthcare consultants.
Healthcare Call Notes (Chinese Enhanced) Use Cases
- Processing raw transcripts from pharmaceutical teleconferences and earnings calls.
- Organizing medical expert interview notes into structured Q&A formats.
- Standardizing internal healthcare service company communication records.
- Extracting key financial and operational metrics from medical strategy meetings.
- Creating research-ready archives for healthcare investment analysis.
How Healthcare Call Notes (Chinese Enhanced) Works
- The skill ingests raw transcript text, audio timestamps, and user-provided terminology lists or background materials.
- It performs an initial scan to identify the meeting structure, including opening remarks and Q&A segments.
- A specialized correction layer fixes pharmaceutical terms, drug names, and financial abbreviations based on industry context.
- Oral fillers and redundant expressions are stripped while preserving all numerical data, logical links, and risk warnings.
- Low-confidence segments are automatically flagged with specific audio timestamps (e.g., [Audio Time: 12:43]) for manual verification.
- The final output is structured into a standardized format featuring a core summary, organized Q&A, and a list of items pending confirmation.
Healthcare Call Notes (Chinese Enhanced) Setup
To integrate this capability into your workflow using Openclaw Skills, ensure your environment is configured for medical data processing:
# Install the skill via the Openclaw CLI
claw install healthcare-call-notes-zh
# Configure specific industry terminology lists
claw config --skill healthcare-call-notes-zh --terms-path ./medical-glossary.json
Healthcare Call Notes (Chinese Enhanced) Data Schema & Taxonomy
The skill organizes processed data into a structured taxonomy to ensure scannability and research utility:
| Section | Data Type | Description |
|---|---|---|
| Header | Metadata | Includes Company Name, Topic, Date, and Source. |
| Core Summary | List | 4-10 bullet points covering numbers, inflection points, and guidance. |
| Main Body | Markdown | Structured paragraphs of prepared remarks or management openings. |
| Q&A | Key-Value | Standardized 'Q' and 'A' format with rewritten, professional queries. |
| Pending Items | List | Low-confidence terms or numbers with associated audio timestamps. |
Healthcare Call Notes (Chinese Enhanced) Advanced Features
- Intelligent Q&A restructuring that merges follow-up questions into a single cohesive topic.
- Confidence-based annotation system: High (direct correction), Medium (flag for confirmation), Low (timestamped original text).
- Specialized detection for healthcare-specific policies such as VBP (Volume-Based Procurement), DRG, and DIP.
- High-fidelity preservation of management 'tone' to distinguish between factual statements and future outlooks.
- Support for custom terminology priority, where user-provided drug or product lists override general AI assumptions.
SKILL.md
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