[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M75GMCV2XJMZ9QPVC24EN593":3,"skill-same:01M75GMCV2XJMZ9QPVC24EN593":79},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":25,"commitSha":26,"skillDirectory":27,"forksCount":28,"repositoryHtmlUrl":29,"repositoryHomepageUrl":29,"ownerHtmlUrl":30,"ownerBlogUrl":6,"ownerDescription":31,"updatedAt":32,"install":33,"resources":46},"01M75GMCV2XJMZ9QPVC24EN593","pyhealth",null,"Build clinical\u002Fhealthcare deep-learning pipelines with PyHealth — loading EHR\u002Fsignal\u002Fimaging datasets (MIMIC-III\u002FIV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer, RETAIN, GAMENet, SafeDrug, MICRON, StageNet, AdaCare, CNN\u002FRNN\u002FMLP), training with the PyHealth Trainer, computing clinical metrics, and using medical code utilities (ICD\u002FATC\u002FNDC\u002FRxNorm lookup and cross-mapping). Use this skill whenever the user mentions PyHealth, MIMIC, eICU, OMOP, EHR modeling, clinical prediction, drug recommendation, sleep staging, medical code mapping, ICD\u002FATC codes, or any healthcare ML pipeline that fits the dataset → task → model → trainer → metrics pattern, even if \"PyHealth\" isn't named explicitly.","Lord1Egypt\u002Fai-skillforge","Lord1Egypt","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F99746359?v=4","01M7592YDNT0XK9WJ0T2MQVPDN","Mohamed Mounir Ali AL Keshita","01M7597M9CBBVAPX1CYE1KFTSM",2,"MIT","https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fpyhealth\u002FSKILL.md",[],[],"2026-11-12 13:55:43",false,"---\nname: pyhealth\ndescription: Build clinical\u002Fhealthcare deep-learning pipelines with PyHealth — loading EHR\u002Fsignal\u002Fimaging datasets (MIMIC-III\u002FIV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer, RETAIN, GAMENet, SafeDrug, MICRON, StageNet, AdaCare, CNN\u002FRNN\u002FMLP), training with the PyHealth Trainer, computing clinical metrics, and using medical code utilities (ICD\u002FATC\u002FNDC\u002FRxNorm lookup and cross-mapping). Use this skill whenever the user mentions PyHealth, MIMIC, eICU, OMOP, EHR modeling, clinical prediction, drug recommendation, sleep staging, medical code mapping, ICD\u002FATC codes, or any healthcare ML pipeline that fits the dataset → task → model → trainer → metrics pattern, even if \"PyHealth\" isn't named explicitly.\nmetadata:\n    skill-author: Lord1Egypt\n---\n\n# PyHealth\n\nPyHealth (https:\u002F\u002Fpyhealth.dev\u002F) is a Python toolkit for clinical deep learning. It provides a unified, modular pipeline across electronic health records (EHR), physiological signals, and medical imaging.\n\nThe library is built around a **5-stage pipeline** — `Dataset → Task → Model → Trainer → Metrics` — where each stage is replaceable and the interfaces between stages are stable. Code that follows this pipeline shape composes well; code that bypasses it usually fights the library.\n\n## When to use this skill\n\nUse this skill whenever the user is doing clinical\u002Fhealthcare ML and any of the following are true:\n\n- They mention PyHealth, MIMIC-III\u002FIV, eICU, OMOP-CDM, EHRShot, SleepEDF, SHHS, ISRUC, COVID19-CXR, ChestX-ray14, TUEV\u002FTUAB.\n- They want to predict mortality, readmission, length of stay, drug recommendations, sleep stages, ICD codes, EEG events, or de-identification.\n- They need to look up or cross-map medical codes (ICD-9-CM, ICD-10-CM, ATC, NDC, RxNorm, CCS).\n- They have EHR-shaped data and want to train a clinical model without writing the plumbing themselves.\n\nPyHealth is the right tool when the workflow fits its 5 stages. If the user just wants generic PyTorch on tabular data, this skill is not necessary.\n\n## Installation (uv)\n\nPyHealth 2.0 requires Python ≥ 3.12, \u003C 3.14. Use `uv` for environment management — it's faster and reproducible.\n\n```bash\n# Create a project with the right Python\nuv init my-pyhealth-project\ncd my-pyhealth-project\nuv python pin 3.12\n\n# Add PyHealth (this also pulls in PyTorch and friends)\nuv add pyhealth\n\n# Run scripts inside the env\nuv run python train.py\n```\n\nFor a one-off script without a project, use `uv run --with pyhealth python script.py`. For the legacy 1.x line (Python 3.9+), `uv add pyhealth==1.16`. Detailed install notes, MIMIC access, and GPU\u002FCPU device tips are in `references\u002Finstallation.md`.\n\n## The 5-stage pipeline\n\nA complete pipeline is typically \u003C20 lines. This is the canonical shape — start here and modify pieces:\n\n```python\nfrom pyhealth.datasets import MIMIC3Dataset, split_by_patient, get_dataloader\nfrom pyhealth.tasks import MortalityPredictionMIMIC3\nfrom pyhealth.models import Transformer\nfrom pyhealth.trainer import Trainer\nfrom pyhealth.metrics.binary import binary_metrics_fn\n\n# 1. Dataset — raw patient registry\nbase = MIMIC3Dataset(\n    root=\"https:\u002F\u002Fstorage.googleapis.com\u002Fpyhealth\u002FSynthetic_MIMIC-III\u002F\",\n    tables=[\"DIAGNOSES_ICD\", \"PROCEDURES_ICD\", \"PRESCRIPTIONS\"],\n)\n\n# 2. Task — converts patients into supervised samples\nsamples = base.set_task(MortalityPredictionMIMIC3())\n\n# 3. Split + DataLoaders (split by patient to avoid leakage)\ntrain_ds, val_ds, test_ds = split_by_patient(samples, [0.8, 0.1, 0.1])\ntrain_loader = get_dataloader(train_ds, batch_size=32, shuffle=True)\nval_loader   = get_dataloader(val_ds,   batch_size=32, shuffle=False)\ntest_loader  = get_dataloader(test_ds,  batch_size=32, shuffle=False)\n\n# 4. Model — must be passed the SampleDataset, not the BaseDataset\nmodel = Transformer(dataset=samples)\n\n# 5. Train + evaluate\ntrainer = Trainer(model=model)\ntrainer.train(\n    train_dataloader=train_loader,\n    val_dataloader=val_loader,\n    epochs=50,\n    monitor=\"pr_auc\",\n)\n\ny_true, y_prob, _ = trainer.inference(test_loader)\nprint(binary_metrics_fn(y_true, y_prob, metrics=[\"pr_auc\", \"roc_auc\"]))\n```\n\nA copy-pasteable starter is in `assets\u002Fstarter_pipeline.py`.\n\n## Critical things to get right\n\nThese are the mistakes that PyHealth code most commonly trips on. Internalize them before writing pipelines:\n\n1. **Models take a `SampleDataset`, not a `BaseDataset`.** `MIMIC3Dataset(...)` returns a `BaseDataset` (a queryable patient registry). Only after `.set_task(task)` do you get a `SampleDataset`, which is what models, splitters, and DataLoaders expect. If you pass `base` to a model, it will fail or behave wrong.\n\n2. **Always split by patient (or visit), not by sample.** Random sample-level splits leak information across train\u002Ftest because the same patient can appear in both. Use `split_by_patient` for patient-level prediction, `split_by_visit` only when visits are independent.\n\n3. **Match the task to the dataset.** Tasks are dataset-specific: `MortalityPredictionMIMIC3` won't work on MIMIC-IV — use `MortalityPredictionMIMIC4` or `InHospitalMortalityMIMIC4`. The full mapping is in `references\u002Ftasks.md`.\n\n4. **Pick `monitor` to match the task type.** For binary classification use `\"pr_auc\"` or `\"roc_auc\"`. For multilabel (drug rec) use `\"pr_auc_samples\"` or `\"jaccard_samples\"`. For multiclass use `\"accuracy\"` or `\"f1_macro\"`. Wrong monitor → checkpoint selection saves the wrong epoch.\n\n5. **MIMIC-IV uses `ehr_root=`, not `root=`.** This is the one inconsistency in the dataset constructors.\n\n6. **For reproducible work, point `cache_dir=` somewhere persistent.** PyHealth caches the parsed dataset; without `cache_dir`, you re-parse every run.\n\n## How to use this skill\n\nPyHealth has a large API surface — there's no point loading it all at once. Read the reference file that matches the user's task:\n\n| If the user is asking about… | Read |\n|---|---|\n| Installing, env setup, MIMIC access, GPU | `references\u002Finstallation.md` |\n| Which dataset class to use, loading patterns, splitting | `references\u002Fdatasets.md` |\n| What prediction task to choose (mortality, readmission, drug rec, sleep…) | `references\u002Ftasks.md` |\n| Picking a model architecture, model-specific arguments | `references\u002Fmodels.md` |\n| Looking up or cross-mapping ICD\u002FATC\u002FNDC\u002FRxNorm\u002FCCS codes, tokenizers | `references\u002Fmedcode.md` |\n| End-to-end recipes for common scenarios | `references\u002Fexamples.md` |\n\nFor multi-step tasks (e.g., \"build a drug recommendation pipeline on MIMIC-IV\"), read `tasks.md` + `models.md` + `examples.md` together — they cross-reference each other.\n\n## A note on style\n\nWrite minimal, idiomatic PyHealth. The library is opinionated; lean into its abstractions instead of reimplementing them in raw PyTorch. If you find yourself writing a custom training loop, ask whether `Trainer` would do the job — it almost always will, and it handles checkpointing, logging, and best-model selection for free.\n\nWhen the user has private MIMIC access, point them at the local CSV root; for demos and learning, the synthetic MIMIC-III bucket (`https:\u002F\u002Fstorage.googleapis.com\u002Fpyhealth\u002FSynthetic_MIMIC-III\u002F`) is fine and works without credentialing.\n","# PyHealth\n\nPyHealth (https:\u002F\u002Fpyhealth.dev\u002F) is a Python toolkit for clinical deep learning. It provides a unified, modular pipeline across electronic health records (EHR), physiological signals, and medical imaging.\n\nThe library is built around a **5-stage pipeline** — `Dataset → Task → Model → Trainer → Metrics` — where each stage is replaceable and the interfaces between stages are stable. Code that follows this pipeline shape composes well; code that bypasses it usually fights the library.\n\n## When to use this skill\n\nUse this skill whenever the user is doing clinical\u002Fhealthcare ML and any of the following are true:\n\n- They mention PyHealth, MIMIC-III\u002FIV, eICU, OMOP-CDM, EHRShot, SleepEDF, SHHS, ISRUC, COVID19-CXR, ChestX-ray14, TUEV\u002FTUAB.\n- They want to predict mortality, readmission, length of stay, drug recommendations, sleep stages, ICD codes, EEG events, or de-identification.\n- They need to look up or cross-map medical codes (ICD-9-CM, ICD-10-CM, ATC, NDC, RxNorm, CCS).\n- They have EHR-shaped data and want to train a clinical model without writing the plumbing themselves.\n\nPyHealth is the right tool when the workflow fits its 5 stages. If the user just wants generic PyTorch on tabular data, this skill is not necessary.\n\n## Installation (uv)\n\nPyHealth 2.0 requires Python ≥ 3.12, \u003C 3.14. Use `uv` for environment management — it's faster and reproducible.\n\n```bash\n# Create a project with the right Python\nuv init my-pyhealth-project\ncd my-pyhealth-project\nuv python pin 3.12\n\n# Add PyHealth (this also pulls in PyTorch and friends)\nuv add pyhealth\n\n# Run scripts inside the env\nuv run python train.py\n```\n\nFor a one-off script without a project, use `uv run --with pyhealth python script.py`. For the legacy 1.x line (Python 3.9+), `uv add pyhealth==1.16`. Detailed install notes, MIMIC access, and GPU\u002FCPU device tips are in `references\u002Finstallation.md`.\n\n## The 5-stage pipeline\n\nA complete pipeline is typically \u003C20 lines. This is the canonical shape — start here and modify pieces:\n\n```python\nfrom pyhealth.datasets import MIMIC3Dataset, split_by_patient, get_dataloader\nfrom pyhealth.tasks import MortalityPredictionMIMIC3\nfrom pyhealth.models import Transformer\nfrom pyhealth.trainer import Trainer\nfrom pyhealth.metrics.binary import binary_metrics_fn\n\n# 1. Dataset — raw patient registry\nbase = MIMIC3Dataset(\n    root=\"https:\u002F\u002Fstorage.googleapis.com\u002Fpyhealth\u002FSynthetic_MIMIC-III\u002F\",\n    tables=[\"DIAGNOSES_ICD\", \"PROCEDURES_ICD\", \"PRESCRIPTIONS\"],\n)\n\n# 2. Task — converts patients into supervised samples\nsamples = base.set_task(MortalityPredictionMIMIC3())\n\n# 3. Split + DataLoaders (split by patient to avoid leakage)\ntrain_ds, val_ds, test_ds = split_by_patient(samples, [0.8, 0.1, 0.1])\ntrain_loader = get_dataloader(train_ds, batch_size=32, shuffle=True)\nval_loader   = get_dataloader(val_ds,   batch_size=32, shuffle=False)\ntest_loader  = get_dataloader(test_ds,  batch_size=32, shuffle=False)\n\n# 4. Model — must be passed the SampleDataset, not the BaseDataset\nmodel = Transformer(dataset=samples)\n\n# 5. Train + evaluate\ntrainer = Trainer(model=model)\ntrainer.train(\n    train_dataloader=train_loader,\n    val_dataloader=val_loader,\n    epochs=50,\n    monitor=\"pr_auc\",\n)\n\ny_true, y_prob, _ = trainer.inference(test_loader)\nprint(binary_metrics_fn(y_true, y_prob, metrics=[\"pr_auc\", \"roc_auc\"]))\n```\n\nA copy-pasteable starter is in `assets\u002Fstarter_pipeline.py`.\n\n## Critical things to get right\n\nThese are the mistakes that PyHealth code most commonly trips on. Internalize them before writing pipelines:\n\n1. **Models take a `SampleDataset`, not a `BaseDataset`.** `MIMIC3Dataset(...)` returns a `BaseDataset` (a queryable patient registry). Only after `.set_task(task)` do you get a `SampleDataset`, which is what models, splitters, and DataLoaders expect. If you pass `base` to a model, it will fail or behave wrong.\n\n2. **Always split by patient (or visit), not by sample.** Random sample-level splits leak information across train\u002Ftest because the same patient can appear in both. Use `split_by_patient` for patient-level prediction, `split_by_visit` only when visits are independent.\n\n3. **Match the task to the dataset.** Tasks are dataset-specific: `MortalityPredictionMIMIC3` won't work on MIMIC-IV — use `MortalityPredictionMIMIC4` or `InHospitalMortalityMIMIC4`. The full mapping is in `references\u002Ftasks.md`.\n\n4. **Pick `monitor` to match the task type.** For binary classification use `\"pr_auc\"` or `\"roc_auc\"`. For multilabel (drug rec) use `\"pr_auc_samples\"` or `\"jaccard_samples\"`. For multiclass use `\"accuracy\"` or `\"f1_macro\"`. Wrong monitor → checkpoint selection saves the wrong epoch.\n\n5. **MIMIC-IV uses `ehr_root=`, not `root=`.** This is the one inconsistency in the dataset constructors.\n\n6. **For reproducible work, point `cache_dir=` somewhere persistent.** PyHealth caches the parsed dataset; without `cache_dir`, you re-parse every run.\n\n## How to use this skill\n\nPyHealth has a large API surface — there's no point loading it all at once. Read the reference file that matches the user's task:\n\n| If the user is asking about… | Read |\n|---|---|\n| Installing, env setup, MIMIC access, GPU | `references\u002Finstallation.md` |\n| Which dataset class to use, loading patterns, splitting | `references\u002Fdatasets.md` |\n| What prediction task to choose (mortality, readmission, drug rec, sleep…) | `references\u002Ftasks.md` |\n| Picking a model architecture, model-specific arguments | `references\u002Fmodels.md` |\n| Looking up or cross-mapping ICD\u002FATC\u002FNDC\u002FRxNorm\u002FCCS codes, tokenizers | `references\u002Fmedcode.md` |\n| End-to-end recipes for common scenarios | `references\u002Fexamples.md` |\n\nFor multi-step tasks (e.g., \"build a drug recommendation pipeline on MIMIC-IV\"), read `tasks.md` + `models.md` + `examples.md` together — they cross-reference each other.\n\n## A note on style\n\nWrite minimal, idiomatic PyHealth. The library is opinionated; lean into its abstractions instead of reimplementing them in raw PyTorch. If you find yourself writing a custom training loop, ask whether `Trainer` would do the job — it almost always will, and it handles checkpointing, logging, and best-model selection for free.\n\nWhen the user has private MIMIC access, point them at the local CSV root; for demos and learning, the synthetic MIMIC-III bucket (`https:\u002F\u002Fstorage.googleapis.com\u002Fpyhealth\u002FSynthetic_MIMIC-III\u002F`) is fine and works without credentialing.\n",{"name":5,"metadata":24,"description":7},{"skill-author":9},"main","3f88e1b76af67701a6405069a542e21a997d2868","skills\u002Fscientific\u002Fpyhealth",0,"https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge","https:\u002F\u002Fgithub.com\u002FLord1Egypt","Unbreakable. Unstoppable. Unmatched⚡.\r\n Speaker of machine tongues 🤖  | World-builder of decentralized dreams 🌐 | Defender of the digital realm🛡️.","2026-06-05 10:16:24",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add Lord1Egypt\u002Fai-skillforge --skill pyhealth",{"tool":39,"command":40},"bunx","bunx skills add Lord1Egypt\u002Fai-skillforge --skill pyhealth",{"tool":42,"command":43},"pnpm","pnpm dlx skills add Lord1Egypt\u002Fai-skillforge --skill pyhealth","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fpyhealth\u002FSKILL.md",true,[47,53,59,63,67,71,75],{"path":48,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":51,"sourceHtmlUrl":52},"assets\u002Fstarter_pipeline.py","SCRIPT","脚本",2338,"https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fpyhealth\u002Fassets\u002Fstarter_pipeline.py",{"path":54,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":57,"sourceHtmlUrl":58},"references\u002Fdatasets.md","REFERENCE","参考文档",4892,"https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fpyhealth\u002Freferences\u002Fdatasets.md",{"path":60,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":61,"sourceHtmlUrl":62},"references\u002Fexamples.md",8137,"https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fpyhealth\u002Freferences\u002Fexamples.md",{"path":64,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":65,"sourceHtmlUrl":66},"references\u002Finstallation.md",3972,"https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fpyhealth\u002Freferences\u002Finstallation.md",{"path":68,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":69,"sourceHtmlUrl":70},"references\u002Fmedcode.md",3394,"https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fpyhealth\u002Freferences\u002Fmedcode.md",{"path":72,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Fmodels.md",4551,"https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fpyhealth\u002Freferences\u002Fmodels.md",{"path":76,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Ftasks.md",5267,"https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fpyhealth\u002Freferences\u002Ftasks.md",[80,87,94,101,108,115],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":19},"01M75GWTHWA52GYQKS347FF6CD","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fzarr-python\u002FSKILL.md",[],[],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":19},"01M75GW9FBP9NAFBSNVJ23RHSX","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fwhat-if-oracle\u002FSKILL.md",[],[],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":19},"01M75GW351MZZQ1V7207PDCX5D","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fvenue-templates\u002FSKILL.md",[],[],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":19},"01M75GVYGMAZDV2NHWSV6PP6TK","vaex","Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV\u002FHDF5\u002FArrow\u002FParquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.","https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fvaex\u002FSKILL.md",[],[],{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":19},"01M75GVS0ADH3DZ3QB564EVYC9","usfiscaldata","Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.","https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fusfiscaldata\u002FSKILL.md",[],[],{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":19},"01M75GVDTBSXRQ4Q11RVJQC56M","treatment-plans","Generate concise (3-4 page), focused medical treatment plans in LaTeX\u002FPDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.","https:\u002F\u002Fgithub.com\u002FLord1Egypt\u002Fai-skillforge\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Ftreatment-plans\u002FSKILL.md",[],[]]