--- name: 'cytokine-storm-analysis-agent' description: 'AI-powered cytokine release syndrome (CRS) and cytokine storm analysis for prediction, monitoring, and management in immunotherapy and infectious disease.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # Cytokine Storm Analysis Agent The **Cytokine Storm Analysis Agent** provides comprehensive AI-driven analysis of cytokine release syndrome (CRS) and hyperinflammatory states. It integrates cytokine profiling, clinical parameters, and immunological markers for early prediction, severity grading, and treatment guidance in CAR-T therapy, sepsis, and viral infections. ## When to Use This Skill * When monitoring CAR-T patients for cytokine release syndrome risk. * To predict CRS severity and timing post-immunotherapy. * For analyzing cytokine panels in sepsis and viral infections (COVID-19). * When guiding tocilizumab/siltuximab anti-IL-6 therapy decisions. * To distinguish CRS from ICANS, HLH, and other inflammatory syndromes. ## Core Capabilities 1. **CRS Risk Prediction**: ML models predict CRS development and severity from baseline factors (tumor burden, disease type, CAR-T product). 2. **Real-Time Monitoring**: Track cytokine dynamics (IL-6, IFN-γ, IL-10, ferritin) with early warning alerts. 3. **Severity Grading**: Automated ASTCT CRS grading using clinical parameters and biomarkers. 4. **Differential Diagnosis**: Distinguish CRS from HLH/MAS, ICANS, infection, and tumor lysis syndrome. 5. **Treatment Guidance**: AI-driven recommendations for tocilizumab, corticosteroids, and supportive care. 6. **Outcome Prediction**: Model response to anti-cytokine therapy and overall outcomes. ## Cytokine Panel Analysis | Cytokine | Role in CRS | Kinetics | Therapeutic Target | |----------|-------------|----------|-------------------| | IL-6 | Central mediator | Early peak | Tocilizumab, Siltuximab | | IFN-γ | T-cell activation | Early | Emapalumab | | IL-1β | Inflammasome | Early | Anakinra | | IL-10 | Regulatory | Variable | - | | TNF-α | Pro-inflammatory | Early | Infliximab (caution) | | IL-2 | T-cell expansion | Early | - | | GM-CSF | Myeloid activation | Sustained | Lenzilumab | ## ASTCT CRS Grading (Automated) | Grade | Fever | Hypotension | Hypoxia | |-------|-------|-------------|---------| | 1 | ≥38°C | None | None | | 2 | ≥38°C | Responsive to fluids | Low-flow O2 | | 3 | ≥38°C | One vasopressor | High-flow O2 | | 4 | ≥38°C | Multiple vasopressors | Ventilation | ## Workflow 1. **Input**: Cytokine levels, vital signs, laboratory values, treatment history. 2. **Risk Assessment**: Baseline CRS risk stratification pre-therapy. 3. **Monitoring**: Real-time cytokine tracking with trend analysis. 4. **Grading**: Automated CRS grade assignment per ASTCT criteria. 5. **Differential**: Rule out mimics (infection, HLH, ICANS). 6. **Treatment**: Generate management recommendations. 7. **Output**: CRS risk score, grade, differential diagnosis, treatment plan. ## Example Usage **User**: "Monitor this CAR-T patient's cytokine levels and predict CRS severity." **Agent Action**: ```bash python3 Skills/Immunology_Vaccines/Cytokine_Storm_Analysis_Agent/crs_analyzer.py \ --patient_data demographics.json \ --cytokines cytokine_panel.csv \ --vitals vital_signs.csv \ --labs laboratory_values.csv \ --cart_product tisagenlecleucel \ --day_post_infusion 5 \ --model crs_predictor_v3 \ --output crs_report.json ``` ## AI/ML Models **CRS Risk Prediction**: - Features: tumor burden (LDH), lymphodepletion intensity, CAR-T dose, disease type - Model: Gradient boosting with SHAP interpretability - Performance: AUC 0.82-0.88 for severe CRS **Severity Trajectory**: - Time-series modeling of cytokine dynamics - LSTM networks for temporal patterns - Early warning 24-48 hours before clinical deterioration **Treatment Response**: - Tocilizumab response prediction - Corticosteroid escalation timing - ICU admission risk ## Differential Diagnosis Decision Tree ``` Fever + Elevated Cytokines | CAR-T context? / \ Yes No | | Hypotension? Infection workup | | CRS Sepsis vs viral | Neuro symptoms? | ICANS vs CRS | Ferritin >10,000? | HLH/MAS evaluation ``` ## Clinical Decision Support **Tocilizumab Indication**: - Grade 2+ CRS - Rapidly rising cytokines - High-risk baseline features **Corticosteroid Indication**: - Tocilizumab-refractory CRS - ICANS any grade - Grade 3+ CRS ## Prerequisites * Python 3.10+ * scikit-learn, XGBoost for ML * Time-series analysis libraries * FHIR client for EHR integration ## Related Skills * CART_Design_Optimizer_Agent - For CAR-T design * TCell_Exhaustion_Analysis_Agent - For T-cell function * Clinical_NLP - For extracting symptoms from notes ## Special Populations 1. **Pediatric**: Different baseline cytokine ranges 2. **Post-COVID**: Altered inflammatory responses 3. **Bridging Therapy**: Impact on CRS risk 4. **Concurrent Infection**: Confounding cytokine elevation ## Author AI Group - Biomedical AI Platform --- Source attribution: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Mega AI Hub resource metadata Creator / publisher: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Data & Analytics, Healthcare & Life Sciences, Product Management Roles: Clinical Research, Data Scientist, Health Informatics, Medical Writer, Product Manager Specialties: API, Testing Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 5047cc619f22df195be00f128f8de5cedcb3ce33ee69002fbe367eb14e5417b5 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.