القائمة
Simulate standardized patient encounters for medical training, supporting OSCE-style history-taking practice, communication skills rehearsal, and educational debriefing.
A comprehensive auditor for any agent skill — including Manus, OpenClaw/ClawHub, Claude, LobeHub, or custom SKILL.md-based skills. Use this skill whenever a user wants to evaluate, audit, review, score, or quality-check an agent skill before publishing, updating, or deploying. Covers two hard veto gates (structural redlines + research integrity redlines), static quality scoring across 25 criteria (ISO 25010 + OpenSSF + Agent), dynamic test input generation, multi-mode execution testing, multi-layer output evaluation with five specialized category rubrics (Evidence Insight / Protocol Design / Data Analysis / Academic Writing / Other), a Research Veto that applies to all four research categories, human eval viewer generation, actionable P0/P1/P2 optimization recommendations, and automatic skill improvement that outputs a polished, production-ready SKILL.md. Also use whenever a user says "audit my skill", "evaluate my skill", "improve my skill", or wants a corrected version after evaluation.
Run GSEA on a ranked gene list and produce the enrichment table, running-score table, and enrichment plots.
Identifies real, evidence-audited, topic-specific research gaps in medical research by first retrieving and verifying literature from trusted sources, then mapping the current evidence landscape, rejecting pseudo-gaps, and converting only medium/high-confidence gaps into study-ready research opportunities. Always require real literature retrieval before formal gap claims. Never fabricate references, metadata, or findings.
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.
Evidence-based medical knowledge and research mentor grounded in the Bian Que tradition. Covers clinical reasoning, diagnostic thinking (望闻问切), pharmacology, pathology, differential diagnosis, medical literature appraisal, and the philosophy of early intervention. Trigger whenever users ask about medicine, clinical science, drugs, disease mechanisms, diagnosis, lab interpretation, treatment comparison, or health sciences. Even without explicit research framing, trigger on any topic touching disease, therapeutics, or clinical decision-making. Part of the AIPOCH Medical Research Skill Hub.
Use when correcting batch effects in merged bulk expression matrices with sample-level batch metadata while preserving biological group structure and generating before-and-after QC plots. NOT for: single-cell integration, raw FASTQ processing, differential expression without batch labels, or datasets without biological groups.
| name | virtual-patient-roleplay |
| description | Simulate standardized patient encounters for medical training, supporting OSCE-style history-taking practice, communication skills rehearsal, and educational debriefing. |
| license | MIT |
| author | AIPOCH |
Structured standardized-patient simulation for medical training and clinical interview practice.
Educational Disclaimer: All output is for training simulation only. This skill does not provide real clinical diagnosis, treatment selection, or emergency instructions. Faculty supervision is required for formal assessment use.
python -m py_compile scripts/main.py
python -c "from scripts.main import PatientSimulator; sim=PatientSimulator('chest_pain'); print(sim.ask('Where does the pain go?')['patient_response'])"
python -c "from scripts.main import PatientSimulator; sim=PatientSimulator('chest_pain'); print(sim.ask('Where does the pain go?')['patient_response'])"
python -c "from scripts.main import PatientSimulator; sim=PatientSimulator('headache'); print(sim.ask('Did the pain start suddenly?')['patient_response'])"
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
scenario | string | No | chest_pain | Scenario: chest_pain, headache, abdominal_pain |
student_question | string | Yes (for interaction) | — | Learner question posed to the patient |
difficulty | string | No | intermediate | Scenario difficulty level |
For complex multi-constraint requests, always include these explicit blocks:
scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.This skill accepts: a scenario identifier and a learner question for standardized patient simulation in a medical training context.
If the request does not involve educational patient simulation — for example, asking for real clinical diagnosis, treatment recommendations, emergency triage, or non-medical roleplay — do not proceed with the workflow. Instead respond:
"virtual-patient-roleplay is designed for medical training simulations only. Your request appears to be outside this scope. Please provide a scenario and learner question for educational practice, or use a more appropriate tool."
Use the following fixed structure for non-trivial requests:
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.