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Extract a source-coded problem list from synthetic clinical evidence deterministically.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "clinical-notes-summarizer-problem-list-extract" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/aibast-agents-library/main/solutions/clinical-notes-summarizer/manual/skills/problem-list-extract/SKILL.md 2. Save it as ~/.claude/skills/problem-list-extract/SKILL.md 3. Reload skills and tell me it's ready
Extract the source-coded problems for SYN-ENC-001. Preserve original ordering, identifiers, dates, statuses, and values, and do not confirm or add any diagnosis.
A problem-list extract with the required heading, source-coded problems, and a closing note that no diagnosis was added, confirmed, or changed.
Extract the problem list for SYN-ENC-999. If the evidence is missing, state what is missing and list the known synthetic identifiers without substituting another record.
A fallback response explaining that the requested synthetic identifier or evidence is missing, plus the known available synthetic identifiers.
Researchers or analysts can use it to extract a problem list from packaged synthetic evidence and verify that problems are presented in original source order. It is suited for read-only review and does not change diagnoses.
Developers can use it to reproduce a fixed problem-list extraction workflow and check whether the heading, field fidelity, and closing statement match the contract. It is useful for stability or regression testing.
The document describes a read-only Clinical Notes Agent workflow for extracting a problem list from packaged synthetic clinical evidence. It requires using two knowledge files, processing only the exact requested synthetic identifier, and preserving original fields and source order. The output must follow a fixed heading and closing statement. If the identifier or evidence is missing, it should explain what is missing and list the known synthetic identifiers.
Extract the source-coded problems for SYN-ENC-001 without confirming a diagnosis.
Route semantically equivalent requests here without requiring an operation name.
Use both packaged knowledge files. Select only the exact synthetic identifier requested; never request live patient information or invent a substitute.
# Problem-List Source Extract; exact problems in order including source-coded type 2 diabetes; end with No diagnosis was added, confirmed, or changed.
Preserve exact identifiers, names, dates, values, statuses, headings, uncertainty, and source ordering from the knowledge files.
This is read-only synthetic evidence. Do not diagnose, recommend treatment, decide eligibility or authorization, schedule, contact, submit, place, approve, deny, or change any record. Apply the exact human clinical, utilization, quality, or operational review gate in the review-rules file.
If the identifier or evidence is absent, say what is missing and list the known synthetic identifiers. Do not substitute another record.
It extracts a problem list for a requested identifier from packaged synthetic clinical evidence. The output must preserve original identifiers, names, dates, values, statuses, headings, uncertainty, and source ordering.
No. The documentation explicitly says not to add, confirm, or change any diagnosis, and not to submit, approve, deny, schedule, or modify any record.
It should state whether the identifier or evidence is missing and list the known synthetic identifiers. It will not substitute another record.
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Triages fictional insurance claims and summarizes key intake details and priority.
Compare incentive options by synthetic cost, modeled lift, and net value.
Estimate policy-term settlement, coverage limits, and deductibles for claims processing.
Segment a fictional client portfolio and visualize health and churn indicators.
Extract and summarize medication inventories from packaged synthetic clinical evidence.
Extract referral context and missing actions from synthetic clinical note evidence.
Generate source-grounded encounter summaries from packaged synthetic clinical evidence.
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