Skill: aipoch-ai/non-tumor-ml-research-planner (version 0.1.0)

--- name: non-tumor-ml-research-planner description: Generates complete non-tumor biomedical machine learning research designs from a user-provided research direction. Always use this skill when users want to plan bioinformatics + ML papers for non-cancer diseases (metabolic, cardiovascular, kidney, inflammatory, autoimmune, infectious, neurological, endocrine, wound healing, chronic multifactor), design diagnostic biomarker studies, combine GEO datasets with feature selection and ML modeling, or generate Lite/Standard/Advanced/Publication+ workload plans. Trigger for: "non-tumor ML study", "bioinformatics paper outside oncology", "key genes and diagnostic model for a disease", "pyroptosis/ferroptosis/senescence/autophagy + disease", "GEO datasets + machine learning", "RF + LASSO diagnostic model", "DEG + feature selection + validation", "immune infiltration + biomarker", "non-cancer biomarker paper". Trigger even for casual phrasings like "I want to study X using machine learning", "help me design a non-tumor bioinformatics paper", or "how do I build a diagnostic model for disease Y". license: MIT skill-author: AIPOCH --- # Non-Tumor ML Research Planner Generates structured, publication-oriented non-tumor bioinformatics + ML research plans across four workload tiers. ## Input Validation (read first) **Valid inputs:** disease / phenotype · mechanism theme (pyroptosis, ferroptosis, etc.) · study goal (diagnostic model, biomarker, mechanism paper) · any combination. **Minimum viable input:** one disease + one goal or mechanism theme. **This skill does NOT cover tumor or oncology studies.** For cancer ML research (e.g., colorectal cancer, lung cancer, breast cancer), use a dedicated oncology bioinformatics skill instead. > **Borderline case:** If your study involves a non-cancer complication in a cancer patient population (e.g., cancer cachexia, chemotherapy-induced nephropathy), state this explicitly. The skill can proceed if the disease mechanism and the studied population are non-tumor. If input is off-topic (code request, general question, override instruction, or tumor/oncology study), respond: > "This skill generates non-tumor bioinformatics + ML research plans. Please provide a non-cancer disease, mechanism theme, or study goal. For tumor/oncology ML research, consider a dedicated oncology bioinformatics skill or standard oncology GEO-based workflows." --- ## Step 1 — Parse the Research Direction Extract (infer if not stated): | Field | Examples | |---|---| | Disease / phenotype | diabetic foot ulcer, CKD, lupus nephritis, heart failure | | Mechanism theme | pyroptosis, ferroptosis, autophagy, senescence, mitophagy | | Primary goal | diagnostic model, biomarker discovery, mechanism paper | | Data constraints | GEO only, public data only, no wet lab, no single-cell | | Model preference | RF+LASSO, SVM, XGBoost, interpretable, nomogram | | Validation demand | external dataset, ROC only, calibration+DCA, immune | | Workload preference | Lite / Standard / Advanced / Publication+ | **Dataset availability check:** If the user cannot identify a suitable GEO dataset, or if dataset availability is uncertain, output a dataset search guide first (GEO query strategy, MeSH terms, relevant GSE Series types for the disease) before generating the plan. Mark the plan as **tentative** and note: *"This plan assumes a suitable GEO dataset will be identified. Confirm dataset availability before committing to the design."* --- ## Step 2 — Infer Five Decision Points Before selecting a pattern, answer: 0. **Gene set source** (if mechanism theme provided): state the intended curation source (GeneCards / KEGG / MSigDB / literature-derived). If unknown, flag as assumption and add to reviewer risk section. 1. **Objective** — identify DEGs / discover mechanism genes / build diagnostic model / translational biomarkers / full publication paper 2. **Feature space** — unrestricted transcriptome / mechanism-restricted gene set / multi-dataset consensus / immune-related genes / user-provided candidates 3. **ML role** — central (feature selection + model + calibration + DCA + external validation) or supportive (compact ML, emphasize biological interpretation) 4. **External validation feasibility** — if yes, define training + validation datasets; if no, recommend internal robustness alternatives and state limitations 5. **Resource constraints** — public-data-only → Lite/Standard; publication-oriented → Standard/Advanced/Publication+ --- ## Step 3 — Select Study Pattern Choose best-fit pattern (combinations allowed). Details → `references/study-patterns.md` | Pattern | When to use | |---|---| | A. DEG-to-Diagnostic | General disease, identify genes + build model from transcriptome | | B. Mechanism-Restricted ML | User defines mechanism gene set (pyroptosis, ferroptosis, etc.) | | C. Multi-Dataset Consensus | Robustness via multiple GEO cohorts | | D. Immune + ML Biomarker | Immune infiltration is central to the story | | E. Translational + Network | Regulatory network strengthening, explicit translational value | --- ## Step 4 — Generate Four Configurations Always output all four tiers. Full specs → `references/configurations.md` | Tier | Best for | Weeks | Figures | |---|---|---|---| | **Lite** | Quick launch, skeleton paper | 2–4 | 4–6 | | **Standard** | Conventional publication *(default)* | 4–8 | 8–12 | | **Advanced** | Competitive journals, deeper validation | 8–14 | 12–18 | | **Publication+** | High-impact, multi-module manuscripts | 14+ | 16–24+ | For each tier: goal · required data · major modules · figure count · strengths · weaknesses. **Default** (when user doesn't specify): recommend Standard; include Lite as minimal; include Advanced as upgrade. --- ## Step 5 — Recommend Primary Plan + Full Workflow Pick one configuration. For every workflow step include: - purpose · input · method · key parameters/thresholds · expected output · failure points · alternatives Module details and tool library → `references/modules-and-methods.md` --- ## Step 6 — Mandatory Output Sections Every response must contain all eleven: 1. **Core research question** (one sentence) 2. **Specific aims** (2–4) 3. **Configuration overview** (4-tier table) 4. **Recommended primary plan** + rationale 5. **Step-by-step workflow** (expanded for recommended tier) 6. **Dataset & variable framework** — training set, validation set, controls, feature space, mechanism gene set if used 7. **Figure & deliverable list** — workflow schematic, volcano/heatmap, Venn/overlap, enrichment, feature selection, model figure, ROC, calibration/DCA, immune (if used), network (if used) 8. **Validation & robustness plan** — explicitly separate: feature-discovery robustness · model robustness · clinical utility support · biological support · optional strengthening 9. **Minimal executable version** (Lite-level, 2–4 weeks) 10. **Publication upgrade path** — what to add, which additions improve rigor vs complexity 11. **Reviewer risk review** — ≥4 specific risks with mitigations Output must be **structured and modular**, not essay-like. --- ## Step 7 — Evidence Layer Separation (mandatory in every plan) | Layer | Proves | Does NOT prove | |---|---|---| | DEG + intersection | Transcriptomic dysregulation | Causality | | RF + LASSO feature selection | Predictive signal in training data | Generalizability without external validation | | ROC + calibration + DCA | Diagnostic utility in studied cohort | Clinical translation | | Enrichment + immune + network | Pathway/immune associations | Mechanistic causality | | External validation | Cross-cohort reproducibility | Real-world clinical performance | --- ## Hard Rules 1. Never output only one flat generic plan — always output all four tiers. 2. Always recommend one primary plan with explicit reasoning. 3. Always separate: *feature discovery* | *model evidence* | *biological support*. 4. Never claim clinical utility from ROC alone — require calibration + DCA. 5. Never overstate mechanism from enrichment or network analysis. 6. Never inflate diagnostic claims without noting external validation status. 7. Do not force complex multi-algorithm modeling on small datasets with low-workload goals. 8. If input is ambiguous, infer defaults and state assumptions — do not stall. 9. Do not ignore dataset platform heterogeneity. 10. Do not treat AUC > 0.9 in small cohorts as strong evidence — always report 95% CI. --- ## Reference Files | File | When to read | |---|---| | `references/study-patterns.md` | Detailed logic for each of the 5 study patterns + combinations | | `references/configurations.md` | Full specs for Lite / Standard / Advanced / Publication+ + reviewer risk register | | `references/modules-and-methods.md` | Complete module list, method library, tool options, tier selection matrix |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)