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awslabs/hcls-agent-skills

Open-source healthcare AI agent skills and life sciences agent skills. 42 skills for medical coding, risk adjustment, claims analytics, quality measures, genomics, imaging, and drug discovery across 13 HCLS domains.

Engineering Data

History

Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.

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`skills/aws-genai-ml-architect/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

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`skills/bioinformatics-on-healthomics/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/biomarker-discovery/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/cdisc-compliance/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/cell-type-annotation/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/cheminformatics/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/claims-analytics/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/claims-billing-rules/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/clinical-data-standards/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/coordination-of-benefits/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/dicom-processing/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/digital-pathology/SKILL.md`

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`skills/drug-repurposing/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/edc-data-validation/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/ehr-data-parsing/SKILL.md`

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`skills/genomic-variant-interpretation/SKILL.md`

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`skills/hedis-measure-specification/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/imaging-study-design/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/medical-device-software-compliance/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/ml-researcher/SKILL.md`

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`skills/molecular-docking/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/multi-omics-integration/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/multi-omics-pipeline/SKILL.md`

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`skills/ngs-quality-control/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/pa-clinical-policy/SKILL.md`

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`skills/pa-decision-automation/SKILL.md`

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`skills/pharmacoepidemiology/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/protein-structure-analysis/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/provider-denial-workup/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/quality-measures/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/quantitative-proteomics/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/radiology-preprocessing/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/risk-adjustment/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/risk-adjustment-strategy/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/risk-stratification-indices/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/rna-seq-analysis/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/rwd-cohort-analysis/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/scrna-seq-pipeline/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/structure-based-drug-design/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/trajectory-analysis/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/translational-research/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

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`skills/variant-calling/SKILL.md`

Week of Nov 2, 2026 · Line counts unavailable

Before Not checked yet → After Local scripts

First observation → 4603541c396c

No earlier version was observed for comparison.

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View on GitHub Add to my skills

Verified commit 4603541c396cdd845dbae554800a65132d12bb63

73GitHub stars

5stars in 7 days

42skills in this repository

0commits in 30 days

Heat

4.9

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Star growth, last 24 hoursNot available
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Commits, last 30 daysNot available
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Hot votes this weekNot available

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Stars over time

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Individual skills in this repository

aws-genai-ml-architect skills/aws-genai-ml-architect
bioinformatics-on-healthomics skills/bioinformatics-on-healthomics
biomarker-discovery skills/biomarker-discovery
cdisc-compliance skills/cdisc-compliance
cell-type-annotation skills/cell-type-annotation
cheminformatics skills/cheminformatics
claims-analytics skills/claims-analytics
claims-billing-rules skills/claims-billing-rules
clinical-data-standards skills/clinical-data-standards
coordination-of-benefits skills/coordination-of-benefits
dicom-processing skills/dicom-processing
digital-pathology skills/digital-pathology
drug-repurposing skills/drug-repurposing
edc-data-validation skills/edc-data-validation
ehr-data-parsing skills/ehr-data-parsing
genomic-variant-interpretation skills/genomic-variant-interpretation
hedis-measure-specification skills/hedis-measure-specification
imaging-study-design skills/imaging-study-design
medical-device-software-compliance skills/medical-device-software-compliance
ml-researcher skills/ml-researcher
molecular-docking skills/molecular-docking
multi-omics-integration skills/multi-omics-integration
multi-omics-pipeline skills/multi-omics-pipeline
ngs-quality-control skills/ngs-quality-control
pa-clinical-policy skills/pa-clinical-policy
pa-decision-automation skills/pa-decision-automation
pharmacoepidemiology skills/pharmacoepidemiology
protein-structure-analysis skills/protein-structure-analysis
provider-denial-workup skills/provider-denial-workup
quality-measures skills/quality-measures
quantitative-proteomics skills/quantitative-proteomics
radiology-preprocessing skills/radiology-preprocessing
risk-adjustment skills/risk-adjustment
risk-adjustment-strategy skills/risk-adjustment-strategy
risk-stratification-indices skills/risk-stratification-indices
rna-seq-analysis skills/rna-seq-analysis
rwd-cohort-analysis skills/rwd-cohort-analysis
scrna-seq-pipeline skills/scrna-seq-pipeline
structure-based-drug-design skills/structure-based-drug-design
trajectory-analysis skills/trajectory-analysis
translational-research skills/translational-research
variant-calling skills/variant-calling

How it works

Choose a skill

aws-genai-ml-architect

When your agent uses it

Reasoning skill for designing AWS GenAI and ML architectures for healthcare and life sciences workloads. Use when the user asks to choose between SageMaker and Bedrock, design a RAG system over medical literature, architect clinical NLP or medical imaging inference, plan genomics or drug discovery pipelines on AWS, address HIPAA/PHI compliance in ML systems, design MLOps for regulated clinical models, or optimize cost for HCLS ML workloads. Triggers include "AWS architecture", "SageMaker vs Bedrock", "HIPAA ML", "clinical RAG", "medical imaging inference", "genomics on AWS", "PHI training", "MLOps healthcare", "Bedrock guardrails", "HealthLake", "HCLS cloud architecture", "BAA compliance", "SageMaker endpoint", "Bedrock knowledge base", "clinical NLP on AWS", "FDA SaMD on AWS".

What it loads into context

Frontmatter

259 tokens

Always available to the agent

SKILL.md

4,014 tokens

When the skill is activated

Reference documents

0 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

Overview
Usage
Response Format
Core Concepts
Service Selection Framework
HCLS Architecture Patterns
Data Architecture
Security & Compliance
MLOps for HCLS
Cost Optimization
Decision Trees
When NOT to Use This Skill
Common Mistakes
When to Escalate to a Human Expert
References

Files

.gitkeepasset0 bytes
SKILL.mdcore18,472 bytes
SKILL.mdcore18,472 bytes
SKILL.mdcore18,472 bytes

No excerpt is shown for this license. Read the source on GitHub.

View the complete source on GitHub

medical-device-software-compliance

When your agent uses it

Reason about SaMD (Software as a Medical Device) regulatory compliance for FDA, EU MDR, and global submissions. Use when the user asks about IEC 62304 safety classification, ISO 14971 risk management, 21 CFR 820/QMSR quality system requirements, Design History File structure, 510(k)/De Novo/PMA pathway selection, SOUP/OTS assessment, verification and validation planning, cybersecurity for medical devices, PCCP for AI/ML devices, design reviews, traceability matrices, or post-market surveillance. Triggers include "SaMD", "medical device software", "IEC 62304", "ISO 14971", "ISO 13485", "21 CFR 820", "QMSR", "510(k)", "De Novo", "PMA", "design history file", "DHF", "software safety classification", "SOUP list", "risk management", "hazard analysis", "design review", "V&V protocol", "FDA submission", "EU MDR", "clinical evaluation", "PCCP", "predetermined change control", "GMLP", "cybersecurity premarket", "Part 11", "design controls", "design inputs", "design outputs", "traceability matrix", "post-market surveillance", "CAPA", "Class II device", "Class III device", "regulatory pathway", "predicate device", "substantial equivalence".

What it loads into context

Frontmatter

368 tokens

Always available to the agent

SKILL.md

5,359 tokens

When the skill is activated

Reference documents

0 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

Overview
Usage
Response Format
1. IEC 62304 Safety Classification Decision Tree
2. FDA Regulatory Pathway Decision Tree
3. Design Controls Lifecycle (21 CFR 820.30 / QMSR)
4. SOUP/OTS Assessment Framework
5. ISO 14971 Risk Management Process
6. V&V Planning by Safety Class
7. Cybersecurity Requirements (FDA 2023 Guidance)
8. AI/ML SaMD Lifecycle (PCCP Framework)
9. Traceability Requirements
10. Design Review Checklist (DR1 / DR2 / DR3)
11. FDA vs EU MDR Comparison for SaMD
12. Common SaMD Compliance Mistakes (Severity-Ranked)
13. Part 11 (Electronic Records) Decision Tree
14. Post-Market Surveillance Essentials
When NOT to Use This Skill
When to Escalate to Human Expert

Files

SKILL.mdcore23,681 bytes
SKILL.mdcore23,681 bytes

No excerpt is shown for this license. Read the source on GitHub.

View the complete source on GitHub

Skills included

Repository facts

License: MIT-0
Created: May 4, 2026
Last pushed: Oct 7, 2026
Forks: 14
Open issues: 1
Project activity: Active

Before you install

Network or broad shell

Automated signals, not a guarantee

Checked Nov 7, 2026

Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.

Show 123 notes

skills/aws-genai-ml-architect/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/aws-genai-ml-architect/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/bioinformatics-on-healthomics/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/bioinformatics-on-healthomics/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/bioinformatics-on-healthomics/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/cell-type-annotation/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/cell-type-annotation/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/cell-type-annotation/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/cell-type-annotation/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/cell-type-annotation/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/claims-analytics/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/claims-analytics/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/claims-analytics/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/claims-analytics/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/claims-analytics/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/claims-analytics/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/clinical-data-standards/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/clinical-data-standards/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/dicom-processing/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
skills/dicom-processing/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed

No broad tool permissions
3 dangerous shell commandsin 2 files: digital-pathology/SKILL.md:284 biomarker-discovery/SKILL.md:292
103 network referencesin 19 files: ehr-data-parsing/SKILL.md:218 clinical-data-standards/SKILL.md:386 pharmacoepidemiology/SKILL.md:357 and 16 more
No hidden or obfuscated text
No instruction overrides
9 executable scriptsin 9 files: bioinformatics-on-healthomics/scripts/setup-healthomics-role.sh:1 bioinformatics-on-healthomics/scripts/setup-s3-bucket.sh:1 digital-pathology/hoptimus0-endpoint/Dockerfile:1 and 6 more
1 reference to secretsin 1 file: digital-pathology/hoptimus0-endpoint/inference.py:13
No requests to vote in Hot

Show all 116 signals by file

`skills/aws-genai-ml-architect/SKILL.md`

Network accessline 338

- HIPAA eligible AWS services: https://aws.amazon.com/compliance/hipaa-eligible-services-reference/

Network accessline 339

- AWS Well-Architected ML Lens: https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/machine-learning-lens.html

Network accessline 340

- SageMaker documentation: https://docs.aws.amazon.com/sagemaker/

Network accessline 341

- Bedrock documentation: https://docs.aws.amazon.com/bedrock/

`skills/bioinformatics-on-healthomics/scripts/setup-healthomics-role.sh`

Executable scriptline 1
```
#!/usr/bin/env bash
```

`skills/bioinformatics-on-healthomics/scripts/setup-s3-bucket.sh`

Executable scriptline 1
```
#!/usr/bin/env bash
```

`skills/biomarker-discovery/SKILL.md`

Network accessline 288

- Steyerberg EW. Clinical Prediction Models. Springer 2019 - Vickers AJ et al. Decision curve analysis. Med Decis Making 2006, https://doi.org/10.1177/0272989X06295361

Network accessline 289

- Vickers AJ et al. Decision curve analysis. Med Decis Making 2006, https://doi.org/10.1177/0272989X06295361

Network accessline 290

- Meinshausen & Bühlmann. Stability selection. JRSS-B 2010, https://doi.org/10.1111/j.1467-9868.2010.00740.x

Dangerous shell operationline 292

- Simon RM et al. J Natl Cancer Inst 2009, https://doi.org/10.1093/jnci/djp335

Network accessline 292

- Simon RM et al. J Natl Cancer Inst 2009, https://doi.org/10.1093/jnci/djp335

`skills/cell-type-annotation/SKILL.md`

Network accessline 342

- CellTypist: Dominguez Conde et al. Science 2022, https://doi.org/10.1126/science.abl5197

Network accessline 343

- SingleR: Aran et al. Nat Immunol 2019, https://doi.org/10.1038/s41590-018-0276-y

Network accessline 344

- scANVI: Xu et al. Mol Syst Biol 2021, https://doi.org/10.15252/msb.20209620

Network accessline 345

- celldex: Aran et al. 2019, https://bioconductor.org/packages/celldex

`skills/clinical-data-standards/SKILL.md`

Network accessline 386

- MedDRA Introductory Guide: https://www.meddra.org/how-to-use/support-documentation

Network accessline 387

- ICH E2B(R3) guideline: https://database.ich.org/sites/default/files/E2B_R3__Guideline.pdf

Network accessline 388

- ICD-10-WHO: https://icd.who.int/browse10/2019/en

Network accessline 389

- ICD-10-CM: https://www.cms.gov/medicare/coding-billing/icd-10-codes

Network accessline 390

- SNOMED CT Starter Guide: https://confluence.ihtsdotools.org/display/DOCSTART

Network accessline 391

- SNOMED CT to ICD-10 Map: https://www.snomed.org/snomed-ct/Use-SNOMED-CT/maps

Network accessline 392

- LOINC Users' Guide: https://loinc.org/kb/users-guide/

Network accessline 393

- CDISC SDTM Implementation Guide: https://www.cdisc.org/standards/foundational/sdtm

Network accessline 394

- OMOP CDM Vocabulary: https://ohdsi.github.io/CommonDataModel/

`skills/dicom-processing/SKILL.md`

Network accessline 262

- dcm2niix: Li et al. J Neurosci Methods 2016, https://doi.org/10.1016/j.jneumeth.2016.03.001

Network accessline 263

- pydicom: https://pydicom.github.io/pydicom/stable/

Network accessline 264

- BIDS: Gorgolewski et al. Sci Data 2016, https://doi.org/10.1038/sdata.2016.44

Network accessline 265

- DICOM de-identification: DICOM PS3.15 Annex E, https://dicom.nema.org/medical/dicom/current/output/chtml/part15/chapter_E.html

`skills/digital-pathology/SKILL.md`

Dangerous shell operationline 284

RUN apt-get update && apt-get install -y openslide-tools libpixman-1-dev libvips   && rm -rf /var/lib/apt/lists/*

Dangerous shell operationline 285
```
&& rm -rf /var/lib/apt/lists/*
```

Network accessline 345

- TIAToolbox: Pocock et al. Commun Med 2022, https://doi.org/10.1038/s43856-022-00186-5

Network accessline 346

- H-optimus-0: Bioptimus, https://huggingface.co/bioptimus/H-optimus-0

Network accessline 347

- CAMELYON16: Bejnordi et al. JAMA 2017, https://doi.org/10.1001/jama.2017.14585

Network accessline 348

- CAMELYON17: Bandi et al. IEEE TMI 2019, https://doi.org/10.1109/TMI.2018.2867350

`skills/digital-pathology/hoptimus0-endpoint/Dockerfile`

Executable scriptline 1
```
Executable file
```

`skills/digital-pathology/hoptimus0-endpoint/build_and_push.sh`

Executable scriptline 1
```
#!/usr/bin/env bash
```

`skills/digital-pathology/hoptimus0-endpoint/inference.py`

Executable scriptline 1
```
Executable file
```
Access to secretsline 13
```
[Credential-related source omitted]
```

`skills/edc-data-validation/SKILL.md`

Network accessline 271

output_path: str = "define.xml") -> None:     """Generate minimal define.xml 2.0 skeleton from SDTM datasets."""     NS = "http://www.cdisc.org/ns/odm/v1.3"

Network accessline 272

"""Generate minimal define.xml 2.0 skeleton from SDTM datasets."""     NS = "http://www.cdisc.org/ns/odm/v1.3"     DEF = "http://www.cdisc.org/ns/def/v2.0"

Network accessline 273
```
NS = "http://www.cdisc.org/ns/odm/v1.3"
```

Network accessline 274

DEF = "http://www.cdisc.org/ns/def/v2.0"

Network accessline 275

nsmap = {None: NS, "def": DEF, "xlink": "http://www.w3.org/1999/xlink"}

`skills/ehr-data-parsing/SKILL.md`

Network accessline 218

"id": "example-001",     "identifier": [         {"system": "http://hospital.example.org/mrn", "value": "MRN123"}

Network accessline 219

"identifier": [         {"system": "http://hospital.example.org/mrn", "value": "MRN123"}     ],

Network accessline 220

{"system": "http://hospital.example.org/mrn", "value": "MRN123"}

Network accessline 253

return {         "id": obs.id, "status": obs.status,         "loinc_code": next((c.code for c in (obs.code.coding or []) if c.system == "http://loinc.org"), None),

Network accessline 254

"id": obs.id, "status": obs.status,         "loinc_code": next((c.code for c in (obs.code.coding or []) if c.system == "http://loinc.org"), None),         "display": obs.code.coding[0].display if obs.code.coding else None,

Network accessline 255

"loinc_code": next((c.code for c in (obs.code.coding or []) if c.system == "http://loinc.org"), None),

Network accessline 403

- **Wrong:** Extracting coded values by array position (e.g., `coding[0]`) instead of by system URI   **Right:** Extract by system URI (e.g., filter for `"http://snomed.info/sct"`) — a Condition may carry both SNOMED and ICD-10 codings

Network accessline 404

**Right:** Extract by system URI (e.g., filter for `"http://snomed.info/sct"`) — a Condition may carry both SNOMED and ICD-10 codings

Network accessline 415

- **Wrong:** Accepting coded values without validating the code system URI   **Right:** Verify that the `system` field matches the expected URI (e.g., `http://loinc.org` for LOINC codes)

Network accessline 416

**Right:** Verify that the `system` field matches the expected URI (e.g., `http://loinc.org` for LOINC codes)

Network accessline 421

- python-hl7: https://python-hl7.readthedocs.io/

Network accessline 422

- fhir.resources (Python): https://pypi.org/project/fhir.resources/

Network accessline 423

- FHIR R4 Specification: https://hl7.org/fhir/R4/

Network accessline 424
```
- LOINC: https://loinc.org/
```
Network accessline 425
```
- SNOMED CT: https://www.snomed.org/
```

`skills/genomic-variant-interpretation/SKILL.md`

Network accessline 311

- ACMG/AMP 2015: Richards et al. Genet Med 2015, https://doi.org/10.1038/gim.2015.30

Network accessline 312

- ClinGen SVI: https://clinicalgenome.org/working-groups/sequence-variant-interpretation/

Network accessline 313

- PVS1 decision tree: Abou Tayoun et al. Hum Mutat 2018, https://doi.org/10.1002/humu.23626

Network accessline 314

- Bayesian ACMG: Tavtigian et al. Hum Mutat 2018, https://doi.org/10.1002/humu.23388

Network accessline 315

- gnomAD: Karczewski et al. Nature 2020, https://doi.org/10.1038/s41586-020-2308-7

Network accessline 316

- ClinVar: Landrum et al. Nucleic Acids Res 2018, https://doi.org/10.1093/nar/gkx1153

`skills/imaging-study-design/SKILL.md`

Network accessline 176

## References - IBSI: Zwanenburg et al. Radiology 2020, https://doi.org/10.1148/radiol.2020191145

Network accessline 177

- IBSI: Zwanenburg et al. Radiology 2020, https://doi.org/10.1148/radiol.2020191145

Network accessline 178

- ADNI protocol: https://adni.loni.usc.edu/methods/mri-tool/mri-analysis/

Network accessline 179

- ComBat harmonization: Fortin et al. NeuroImage 2018, https://doi.org/10.1016/j.neuroimage.2017.11.024

`skills/ml-researcher/SKILL.md`

Network accessline 328

- TRIPOD+AI: Collins et al. BMJ 2024, https://doi.org/10.1136/bmj-2023-078378

Network accessline 329

- PROBAST+AI: Wolff et al. BMJ 2024, https://doi.org/10.1136/bmj-2023-078434

Network accessline 330

- CLAIM 2024: Mongan et al. Radiology:AI 2024, https://doi.org/10.1148/ryai.230513

Network accessline 331

- FDA AI/ML SaMD guidance: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device

Network accessline 332

- Decision curve analysis: Vickers et al. Med Decis Making 2006, https://doi.org/10.1177/0272989X06295361

`skills/ngs-quality-control/SKILL.md`

Network accessline 284

- FastQC: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

Network accessline 285

- fastp: Chen et al. Bioinformatics 2018, https://doi.org/10.1093/bioinformatics/bty560

Network accessline 286

- Picard: https://broadinstitute.github.io/picard/

Network accessline 287

- mosdepth: Pedersen & Quinlan, Bioinformatics 2018, https://doi.org/10.1093/bioinformatics/btx699

Network accessline 288

- Coverage recommendations: Sims et al. Nat Rev Genet 2014, https://doi.org/10.1038/nrg3642

`skills/ngs-quality-control/scripts/qc_report.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`skills/pharmacoepidemiology/SKILL.md`

Network accessline 357

- Hernán MA, Robins JM. Causal Inference: What If. Chapman & Hall/CRC 2020, https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Network accessline 358

- Hernán MA, Alonso A, Logan R et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology 2008, https://doi.org/10.1097/EDE.0b013e3181875e61

Network accessline 359

- Lund JL, Richardson DB, Stürmer T. The active comparator, new user study design in pharmacoepidemiology. Epidemiology 2015, https://doi.org/10.1097/EDE.0000000000000231

Network accessline 360

- Suissa S. Immortal time bias in pharmacoepidemiology. Am J Epidemiol 2008, https://doi.org/10.1093/aje/kwn138

Network accessline 361

- Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000, https://doi.org/10.1097/00001648-200009000-00011

Network accessline 362

- VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med 2017, https://doi.org/10.7326/M16-2607

Network accessline 363

- Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011, https://doi.org/10.1080/00273171.2011.568786

Network accessline 364

- Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology 2010, https://doi.org/10.1097/EDE.0b013e3181d61eeb

`skills/radiology-preprocessing/SKILL.md`

Network accessline 280

- HD-BET: Isensee et al. Hum Brain Mapp 2019, https://doi.org/10.1002/hbm.24750

Network accessline 281

- ANTs: Avants et al. NeuroImage 2011, https://doi.org/10.1016/j.neuroimage.2010.09.025

Network accessline 282

- N4 bias correction: Tustison et al. IEEE TMI 2010, https://doi.org/10.1109/TMI.2010.2046908

Network accessline 283

- FSL: Jenkinson et al. NeuroImage 2012, https://doi.org/10.1016/j.neuroimage.2011.09.015

`skills/rna-seq-analysis/scripts/deseq2_contrast.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`skills/rwd-cohort-analysis/SKILL.md`

Network accessline 450

- Hernán MA, Robins JM. Causal Inference: What If. Chapman & Hall/CRC 2020, https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Network accessline 451

- Austin PC. An introduction to propensity score methods. Multivariate Behav Res 2011, https://doi.org/10.1080/00273171.2011.568786

Network accessline 452

- Pharmacy Quality Alliance. PDC measure specifications, https://www.pqaalliance.org/

Network accessline 453

- Davidson-Pilon C. lifelines documentation, https://lifelines.readthedocs.io/

Network accessline 454

- Ho DE et al. MatchIt: nonparametric preprocessing for parametric causal inference. J Stat Softw 2011, https://doi.org/10.18637/jss.v042.i08

`skills/scrna-seq-pipeline/SKILL.md`

Network accessline 194

- Scanpy: Wolf et al. Genome Biol 2018, https://doi.org/10.1186/s13059-017-1382-0

Network accessline 195

- Scanpy tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html

Network accessline 196

- scVI: Gayoso et al. Nat Biotechnol 2022, https://doi.org/10.1038/s41587-021-01206-w

Network accessline 197

- Harmony: Korsunsky et al. Nat Methods 2019, https://doi.org/10.1038/s41592-019-0619-0

`skills/structure-based-drug-design/SKILL.md`

Network accessline 227

- Druggability: Halgren. Chem Biol Drug Des 2009, https://doi.org/10.1111/j.1747-0285.2009.00890.x

Network accessline 228

- fpocket: Le Guilloux et al. BMC Bioinformatics 2009, https://doi.org/10.1186/1471-2105-10-168

Network accessline 229

- Vina scoring: Trott & Olson. J Comput Chem 2010, https://doi.org/10.1002/jcc.21334

Network accessline 230

- FEP accuracy: Cournia et al. J Chem Inf Model 2017, https://doi.org/10.1021/acs.jcim.7b00564

`skills/trajectory-analysis/SKILL.md`

Network accessline 347

- CellRank 2: Weiler et al. Nat Methods 2024, https://doi.org/10.1038/s41592-023-02088-9

Network accessline 348

- scVelo: Bergen et al. Nat Biotechnol 2020, https://doi.org/10.1038/s41587-020-0591-3

Network accessline 349

- CellRank: Lange et al. Nat Methods 2022, https://doi.org/10.1038/s41592-021-01346-6

Network accessline 350

- PAGA: Wolf et al. Genome Biol 2019, https://doi.org/10.1186/s13059-019-1663-x

Network accessline 351

- velocyto: La Manno et al. Nature 2018, https://doi.org/10.1038/s41586-018-0414-6

`skills/translational-research/SKILL.md`

Network accessline 298

- FDA-NIH BEST Resource: https://www.ncbi.nlm.nih.gov/books/NBK338448/

Network accessline 299

- ARRIVE 2.0: Percie du Sert et al. PLoS Biol 2020, https://doi.org/10.1371/journal.pbio.3000410

Network accessline 300

- MR for drug target validation: Schmidt et al. Nat Commun 2020, https://doi.org/10.1038/s41467-020-16969-0

Network accessline 301

- Translational failure in neuroscience: Bhatt et al. Transl Med Commun 2019, https://doi.org/10.1186/s41231-019-0050-7

`skills/variant-calling/scripts/filter_by_acmg.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`skills/variant-calling/scripts/run_germline_pipeline.sh`

Executable scriptline 1
```
#!/usr/bin/env bash
```

How the signals fit together

Facts from a static read of the files at the pinned commit. They describe what the code does, not why.

Some files could not be read; the facts below still hold.

Signal confidence: 116 facts

Network access in prose of the SKILL.mdaws-genai-ml-architect/SKILL.md:338Low confidence
Network access in prose of the SKILL.mdaws-genai-ml-architect/SKILL.md:339Low confidence
Network access in prose of the SKILL.mdaws-genai-ml-architect/SKILL.md:340Low confidence
Network access in prose of the SKILL.mdaws-genai-ml-architect/SKILL.md:341Low confidence
Executable script in code of a file that runsbioinformatics-on-healthomics/scripts/setup-healthomics-role.sh:1High confidence
Executable script in code of a file that runsbioinformatics-on-healthomics/scripts/setup-s3-bucket.sh:1High confidence
Network access in prose of the SKILL.mdbiomarker-discovery/SKILL.md:288Low confidence
Network access in prose of the SKILL.mdbiomarker-discovery/SKILL.md:289Low confidence
Network access in prose of the SKILL.mdbiomarker-discovery/SKILL.md:290Low confidence
Dangerous shell operation in prose of the SKILL.mdbiomarker-discovery/SKILL.md:292Low confidence
Network access in prose of the SKILL.mdbiomarker-discovery/SKILL.md:292Low confidence
Network access in prose of the SKILL.mdcell-type-annotation/SKILL.md:342Low confidence
Network access in prose of the SKILL.mdcell-type-annotation/SKILL.md:343Low confidence
Network access in prose of the SKILL.mdcell-type-annotation/SKILL.md:344Low confidence
Network access in prose of the SKILL.mdcell-type-annotation/SKILL.md:345Low confidence
Network access in prose of the SKILL.mdclinical-data-standards/SKILL.md:386Low confidence
Network access in prose of the SKILL.mdclinical-data-standards/SKILL.md:387Low confidence
Network access in prose of the SKILL.mdclinical-data-standards/SKILL.md:388Low confidence
Network access in prose of the SKILL.mdclinical-data-standards/SKILL.md:389Low confidence
Network access in prose of the SKILL.mdclinical-data-standards/SKILL.md:390Low confidence
Network access in prose of the SKILL.mdclinical-data-standards/SKILL.md:391Low confidence
Network access in prose of the SKILL.mdclinical-data-standards/SKILL.md:392Low confidence
Network access in prose of the SKILL.mdclinical-data-standards/SKILL.md:393Low confidence
Network access in prose of the SKILL.mdclinical-data-standards/SKILL.md:394Low confidence
Network access in prose of the SKILL.mddicom-processing/SKILL.md:262Low confidence
Network access in prose of the SKILL.mddicom-processing/SKILL.md:263Low confidence
Network access in prose of the SKILL.mddicom-processing/SKILL.md:264Low confidence
Network access in prose of the SKILL.mddicom-processing/SKILL.md:265Low confidence
Dangerous shell operation in code of the SKILL.mddigital-pathology/SKILL.md:284High confidence
Dangerous shell operation in code of the SKILL.mddigital-pathology/SKILL.md:285High confidence
Network access in prose of the SKILL.mddigital-pathology/SKILL.md:345Low confidence
Network access in prose of the SKILL.mddigital-pathology/SKILL.md:346Low confidence
Network access in prose of the SKILL.mddigital-pathology/SKILL.md:347Low confidence
Network access in prose of the SKILL.mddigital-pathology/SKILL.md:348Low confidence
Executable script in code of a file that runsdigital-pathology/hoptimus0-endpoint/Dockerfile:1High confidence
Executable script in code of a file that runsdigital-pathology/hoptimus0-endpoint/build_and_push.sh:1High confidence
Executable script in code of a file that runsdigital-pathology/hoptimus0-endpoint/inference.py:1High confidence
Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuedigital-pathology/hoptimus0-endpoint/inference.py:13Low confidence
Network access in code of the SKILL.mdedc-data-validation/SKILL.md:271High confidence
Network access in code of the SKILL.mdedc-data-validation/SKILL.md:272High confidence
Network access in code of the SKILL.mdedc-data-validation/SKILL.md:273High confidence
Network access in code of the SKILL.mdedc-data-validation/SKILL.md:274High confidence
Network access in code of the SKILL.mdedc-data-validation/SKILL.md:275High confidence
Network access in code of the SKILL.mdehr-data-parsing/SKILL.md:218High confidence
Network access in code of the SKILL.mdehr-data-parsing/SKILL.md:219High confidence
Network access in code of the SKILL.mdehr-data-parsing/SKILL.md:220High confidence
Network access in code of the SKILL.mdehr-data-parsing/SKILL.md:253High confidence
Network access in code of the SKILL.mdehr-data-parsing/SKILL.md:254High confidence
Network access in code of the SKILL.mdehr-data-parsing/SKILL.md:255High confidence
Network access in prose of the SKILL.mdehr-data-parsing/SKILL.md:403Low confidence

and 66 more

Works with

Agent compatibility, from the files in this repository
Agent	Status	Evidence	Install to
Claude Code	Compatible	Standard SKILL.md skills/aws-genai-ml-architect/SKILL.md	`.claude/skills/<skill-name>/`
Codex	Compatible	Standard SKILL.md skills/aws-genai-ml-architect/SKILL.md	`.agents/skills/<skill-name>/`
Cursor	Compatible	Standard SKILL.md skills/aws-genai-ml-architect/SKILL.md	`.cursor/skills/<skill-name>/`
Gemini CLI	Compatible	Standard SKILL.md skills/aws-genai-ml-architect/SKILL.md	`.gemini/skills/<skill-name>/`
GitHub Copilot	Compatible	Standard SKILL.md skills/aws-genai-ml-architect/SKILL.md	`.github/skills/<skill-name>/`
OpenCode	Compatible	Standard SKILL.md skills/aws-genai-ml-architect/SKILL.md	`.opencode/skills/<skill-name>/`

Native The repository ships a manifest, folder or frontmatter field for this agent.
Compatible A standard SKILL.md that this agent can read.
Unknown No evidence either way.

Derived automatically from paths, manifests and frontmatter. Not tested on each agent.