[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CVS21HPQM5VT17SAESSV65":3,"skill-same:01M7CVS21HPQM5VT17SAESSV65":62},{"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":6,"ownerHtmlUrl":30,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":31,"install":32,"resources":45},"01M7CVS21HPQM5VT17SAESSV65","sop-writer",null,"Write GCP-compliant standard operating procedures for laboratory, clinical, or research workflows; use when a reproducible SOP with roles, steps, and control points is required.","mrsonord2240\u002Fopenscience-specialists","mrsonord2240","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76487430?v=4","01M7CTEM40S43GSJZ9A3YCCXZK","Samuel Nord","01M7CTEM4K69JD1GBYNHVJ9ZP4",2,"MIT","https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Fpreclinical-validation-designer\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fsop-writer\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: sop-writer\ndescription: Write GCP-compliant standard operating procedures for laboratory, clinical, or research workflows; use when a reproducible SOP with roles, steps, and control points is required.\nlicense: MIT\nauthor: AIPOCH\n---\n> **Source**: [https:\u002F\u002Fgithub.com\u002Faipoch\u002Fmedical-research-skills](https:\u002F\u002Fgithub.com\u002Faipoch\u002Fmedical-research-skills)\n\n# SOP Writer\n\nGenerate compliant Standard Operating Procedures.\n\n## Quick Check\n\nUse this command to verify that the packaged script entry point can be parsed before deeper execution.\n\n```bash\npython -m py_compile scripts\u002Fmain.py\n```\n\n## Audit-Ready Commands\n\nUse these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.\n\n```bash\npython -m py_compile scripts\u002Fmain.py\npython scripts\u002Fmain.py --help\n```\n\n## When to Use\n\n- Use this skill when the task is to Write GCP-compliant Standard Operating Procedures for labs and clinical.\n- Use this skill for protocol design tasks that require explicit assumptions, bounded scope, and a reproducible output format.\n- Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.\n\n## Workflow\n\n1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.\n2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.\n3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.\n4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.\n5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.\n\n## Use Cases\n- Sample processing SOPs\n- Equipment calibration SOPs\n- Safety procedure documentation\n- GCP compliance procedures\n\n## Parameters\n- `procedure_name`: SOP title\n- `scope`: Applicable areas\n- `responsibility`: Who performs\n\n## Returns\n- ISO 15189 compliant SOP\n- Version control section\n- Signature page template\n\n## Example\nInput: Blood sample processing\nOutput: Complete SOP with materials, methods, QC checks\n\n## References\n\n- [references\u002Faudit-reference.md](references\u002Faudit-reference.md) - Supported SOP scope, audit commands, and fallback boundaries\n\n## Risk Assessment\n\n| Risk Indicator | Assessment | Level |\n|----------------|------------|-------|\n| Code Execution | Python\u002FR scripts executed locally | Medium |\n| Network Access | No external API calls | Low |\n| File System Access | Read input files, write output files | Medium |\n| Instruction Tampering | Standard prompt guidelines | Low |\n| Data Exposure | Output files saved to workspace | Low |\n\n## Security Checklist\n\n- [ ] No hardcoded credentials or API keys\n- [ ] No unauthorized file system access (..\u002F)\n- [ ] Output does not expose sensitive information\n- [ ] Prompt injection protections in place\n- [ ] Input file paths validated (no ..\u002F traversal)\n- [ ] Output directory restricted to workspace\n- [ ] Script execution in sandboxed environment\n- [ ] Error messages sanitized (no stack traces exposed)\n- [ ] Dependencies audited\n\n## Prerequisites\n\nNo additional Python packages required.\n\n## Evaluation Criteria\n\n### Success Metrics\n- [ ] Successfully executes main functionality\n- [ ] Output meets quality standards\n- [ ] Handles edge cases gracefully\n- [ ] Performance is acceptable\n\n### Test Cases\n1. **Basic Functionality**: Standard input → Expected output\n2. **Edge Case**: Invalid input → Graceful error handling\n3. **Performance**: Large dataset → Acceptable processing time\n\n## Lifecycle Status\n\n- **Current Stage**: Draft\n- **Next Review Date**: 2026-03-06\n- **Known Issues**: None\n- **Planned Improvements**: \n  - Performance optimization\n  - Additional feature support\n\n## Output Requirements\n\nEvery final response should make these items explicit when they are relevant:\n\n- Objective or requested deliverable\n- Inputs used and assumptions introduced\n- Workflow or decision path\n- Core result, recommendation, or artifact\n- Constraints, risks, caveats, or validation needs\n- Unresolved items and next-step checks\n\n## Error Handling\n\n- If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.\n- If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.\n- If `scripts\u002Fmain.py` fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.\n- Do not fabricate files, citations, data, search results, or execution outcomes.\n\n## Input Validation\n\nThis skill accepts requests that match the documented purpose of `sop-writer` and include enough context to complete the workflow safely.\n\nDo not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:\n\n> `sop-writer` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.\n\n## Response Template\n\nUse the following fixed structure for non-trivial requests:\n\n1. Objective\n2. Inputs Received\n3. Assumptions\n4. Workflow\n5. Deliverable\n6. Risks and Limits\n7. Next Checks\n\nIf the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.\n\n## When Not to Use\n\n- Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.\n- Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.\n- Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.\n\n## Required Inputs\n\n| Field | Required | Format\u002FSource | Example | If Missing |\n|---|---|---|---|---|\n| User task description | Yes | Text | Research question, writing goal, analysis objective | Stop and ask user to provide |\n| Primary input material | Depends on task | Text, file path, ID, table, or literature | PMID, PDF, CSV, DOCX, keywords, etc. | Specify which material type is missing |\n| Output preference | No | Text | Language, format, target journal, template | Use skill default format |\n\n## Output Contract\n\n- Primary output: Structured result or target file aligned with this skill's objective.\n- Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.\n- Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.\n- If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.\n\n## Failure Handling\n\n- Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.\n- Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.\n- Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.\n\n## User Checkpoints\n\n- Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.\n- Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.\n\n## Quick Validation\n\n- Check that key scripts, templates, or reference file paths this skill depends on exist.\n- Check that the final output contains the core fields, sections, or files specified for this task.\n- Check that results clearly mark assumptions, limitations, and incomplete items.\n","> **Source**: [https:\u002F\u002Fgithub.com\u002Faipoch\u002Fmedical-research-skills](https:\u002F\u002Fgithub.com\u002Faipoch\u002Fmedical-research-skills)\n\n# SOP Writer\n\nGenerate compliant Standard Operating Procedures.\n\n## Quick Check\n\nUse this command to verify that the packaged script entry point can be parsed before deeper execution.\n\n```bash\npython -m py_compile scripts\u002Fmain.py\n```\n\n## Audit-Ready Commands\n\nUse these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.\n\n```bash\npython -m py_compile scripts\u002Fmain.py\npython scripts\u002Fmain.py --help\n```\n\n## When to Use\n\n- Use this skill when the task is to Write GCP-compliant Standard Operating Procedures for labs and clinical.\n- Use this skill for protocol design tasks that require explicit assumptions, bounded scope, and a reproducible output format.\n- Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.\n\n## Workflow\n\n1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.\n2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.\n3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.\n4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.\n5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.\n\n## Use Cases\n- Sample processing SOPs\n- Equipment calibration SOPs\n- Safety procedure documentation\n- GCP compliance procedures\n\n## Parameters\n- `procedure_name`: SOP title\n- `scope`: Applicable areas\n- `responsibility`: Who performs\n\n## Returns\n- ISO 15189 compliant SOP\n- Version control section\n- Signature page template\n\n## Example\nInput: Blood sample processing\nOutput: Complete SOP with materials, methods, QC checks\n\n## References\n\n- [references\u002Faudit-reference.md](references\u002Faudit-reference.md) - Supported SOP scope, audit commands, and fallback boundaries\n\n## Risk Assessment\n\n| Risk Indicator | Assessment | Level |\n|----------------|------------|-------|\n| Code Execution | Python\u002FR scripts executed locally | Medium |\n| Network Access | No external API calls | Low |\n| File System Access | Read input files, write output files | Medium |\n| Instruction Tampering | Standard prompt guidelines | Low |\n| Data Exposure | Output files saved to workspace | Low |\n\n## Security Checklist\n\n- [ ] No hardcoded credentials or API keys\n- [ ] No unauthorized file system access (..\u002F)\n- [ ] Output does not expose sensitive information\n- [ ] Prompt injection protections in place\n- [ ] Input file paths validated (no ..\u002F traversal)\n- [ ] Output directory restricted to workspace\n- [ ] Script execution in sandboxed environment\n- [ ] Error messages sanitized (no stack traces exposed)\n- [ ] Dependencies audited\n\n## Prerequisites\n\nNo additional Python packages required.\n\n## Evaluation Criteria\n\n### Success Metrics\n- [ ] Successfully executes main functionality\n- [ ] Output meets quality standards\n- [ ] Handles edge cases gracefully\n- [ ] Performance is acceptable\n\n### Test Cases\n1. **Basic Functionality**: Standard input → Expected output\n2. **Edge Case**: Invalid input → Graceful error handling\n3. **Performance**: Large dataset → Acceptable processing time\n\n## Lifecycle Status\n\n- **Current Stage**: Draft\n- **Next Review Date**: 2026-03-06\n- **Known Issues**: None\n- **Planned Improvements**: \n  - Performance optimization\n  - Additional feature support\n\n## Output Requirements\n\nEvery final response should make these items explicit when they are relevant:\n\n- Objective or requested deliverable\n- Inputs used and assumptions introduced\n- Workflow or decision path\n- Core result, recommendation, or artifact\n- Constraints, risks, caveats, or validation needs\n- Unresolved items and next-step checks\n\n## Error Handling\n\n- If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.\n- If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.\n- If `scripts\u002Fmain.py` fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.\n- Do not fabricate files, citations, data, search results, or execution outcomes.\n\n## Input Validation\n\nThis skill accepts requests that match the documented purpose of `sop-writer` and include enough context to complete the workflow safely.\n\nDo not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:\n\n> `sop-writer` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.\n\n## Response Template\n\nUse the following fixed structure for non-trivial requests:\n\n1. Objective\n2. Inputs Received\n3. Assumptions\n4. Workflow\n5. Deliverable\n6. Risks and Limits\n7. Next Checks\n\nIf the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.\n\n## When Not to Use\n\n- Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.\n- Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.\n- Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.\n\n## Required Inputs\n\n| Field | Required | Format\u002FSource | Example | If Missing |\n|---|---|---|---|---|\n| User task description | Yes | Text | Research question, writing goal, analysis objective | Stop and ask user to provide |\n| Primary input material | Depends on task | Text, file path, ID, table, or literature | PMID, PDF, CSV, DOCX, keywords, etc. | Specify which material type is missing |\n| Output preference | No | Text | Language, format, target journal, template | Use skill default format |\n\n## Output Contract\n\n- Primary output: Structured result or target file aligned with this skill's objective.\n- Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.\n- Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.\n- If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.\n\n## Failure Handling\n\n- Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.\n- Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.\n- Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.\n\n## User Checkpoints\n\n- Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.\n- Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.\n\n## Quick Validation\n\n- Check that key scripts, templates, or reference file paths this skill depends on exist.\n- Check that the final output contains the core fields, sections, or files specified for this task.\n- Check that results clearly mark assumptions, limitations, and incomplete items.\n",{"name":5,"author":24,"license":15,"description":7},"AIPOCH","main","e0956dc9606356ab78468936b502e55a365282e2","specialists\u002Fpreclinical-validation-designer\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fsop-writer",0,"https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists","https:\u002F\u002Fgithub.com\u002Fmrsonord2240","2026-10-12 03:19:04",{"source":8,"skillName":5,"commands":33,"prompt":43,"zipAvailable":44},[34,37,40],{"tool":35,"command":36},"npx","npx skills add mrsonord2240\u002Fopenscience-specialists --skill sop-writer",{"tool":38,"command":39},"bunx","bunx skills add mrsonord2240\u002Fopenscience-specialists --skill sop-writer",{"tool":41,"command":42},"pnpm","pnpm dlx skills add mrsonord2240\u002Fopenscience-specialists --skill sop-writer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Fpreclinical-validation-designer\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fsop-writer\u002FSKILL.md",true,[46,52,56],{"path":47,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":50,"sourceHtmlUrl":51},"POLISH_CHANGELOG.md","REFERENCE","参考文档",593,"https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Fpreclinical-validation-designer\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fsop-writer\u002FPOLISH_CHANGELOG.md",{"path":53,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":54,"sourceHtmlUrl":55},"references\u002Faudit-reference.md",532,"https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Fpreclinical-validation-designer\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fsop-writer\u002Freferences\u002Faudit-reference.md",{"path":57,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":60,"sourceHtmlUrl":61},"scripts\u002Fmain.py","SCRIPT","脚本",2005,"https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Fpreclinical-validation-designer\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fsop-writer\u002Fscripts\u002Fmain.py",[63,71,78,85,92,99],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":67,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"publishedAt":19},"01M7CW36A6QAGWFE2C008M1XPG","bio-workflows-metabolomics-pipeline","Orchestrates the untargeted LC-MS metabolomics pipeline end-to-end (xcms 4.x feature extraction, QC\u002Fdrift\u002Fnormalization, confidence-stratified annotation, permutation-validated statistics, background-aware pathway mapping), naming what each stage decides and where it silently fails. Use when running a full LC-MS metabolomics study from raw mzML to enriched pathways and needing the honest handoffs between stages. Each stage defers to its component skill for parameters and traps; for stable-isotope flux (a separate branch, not this untargeted flow) see metabolomics\u002Fisotope-tracing.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Funtargeted-metabolomics-analyst\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fbio-workflows-metabolomics-pipeline\u002FSKILL.md",[],[],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":67,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":77,"publishedAt":19},"01M7CW2Y18623NA2S1Y30FRQW1","bio-metabolomics-xcms-preprocessing","Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment\u002FXcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time alignment, peak-density correspondence, gap-filling, CAMERA redundancy collapse, and built-in QC feature filtering. Use when converting centroided LC-MS runs into a features-by-samples matrix and deciding centWave\u002Fgrouping\u002Falignment parameters. For drift correction and QC\u002FCV filtering execution see metabolomics\u002Fnormalization-qc; for metabolite identification see metabolomics\u002Fmetabolite-annotation; for the MS-DIAL GUI alternative with MS2Dec deconvolution see metabolomics\u002Fmsdial-preprocessing; for downstream statistics see metabolomics\u002Fstatistical-analysis.","https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Funtargeted-metabolomics-analyst\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fbio-metabolomics-xcms-preprocessing\u002FSKILL.md",[],[],{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":67,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":19},"01M7CW2M364HVWPNQEKE2WP4PP","bio-metabolomics-statistical-analysis","Decision-grade statistical analysis for metabolomics intensity tables. Covers transformation and scaling (Pareto vs unit-variance as a hidden hypothesis), unsupervised structure (PCA\u002FHCA for QC), permutation-validated PLS-DA\u002FOPLS-DA (R2 vs Q2, double CV, VIP as heuristic), univariate testing (Welch\u002FMann-Whitney\u002FANOVA\u002FLMM with covariate adjustment), and dependence-aware multiple testing. Use when testing which metabolites differ, building or validating a discriminant model, choosing a scaling, or correcting many correlated tests. For sample-wise normalization\u002Fdrift correction see metabolomics\u002Fnormalization-qc; for ML classifiers and selection-inside-CV leakage see machine-learning\u002Fbiomarker-discovery and machine-learning\u002Fmodel-validation; for pathway interpretation see metabolomics\u002Fpathway-mapping; for design\u002Fpower\u002Fmultiplicity regime see experimental-design\u002Fmultiple-testing.","https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Funtargeted-metabolomics-analyst\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fbio-metabolomics-statistical-analysis\u002FSKILL.md",[],[],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":67,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":19},"01M7CW2BCBF851WYE03Z8XPEDQ","bio-metabolomics-pathway-mapping","Maps metabolomics results to biological pathways via over-representation (ORA), metabolite-set enrichment (MSEA\u002FQEA), mummichog\u002FPSEA on raw m\u002Fz peaks, and network-diffusion enrichment (FELLA), with correct background-set construction and honest interpretive ceilings. Use when interpreting differential metabolites or an untargeted LC-MS feature table in pathway context, choosing ORA vs MSEA vs mummichog vs topology, or setting the reference\u002Fbackground set. For annotation confidence levels feeding ORA see metabolomics\u002Fmetabolite-annotation; for gene-set concepts see pathway-analysis\u002Fgo-enrichment and pathway-analysis\u002Fgsea; for joint gene+metabolite pathways see multi-omics-integration\u002Fmofa-integration.","https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Funtargeted-metabolomics-analyst\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fbio-metabolomics-pathway-mapping\u002FSKILL.md",[],[],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":67,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":19},"01M7CW21NSZMCPMF7D9CHJM718","bio-metabolomics-normalization-qc","Designs QC, corrects signal drift, removes batch effects, filters features, normalizes samples, and imputes missing values for untargeted LC-MS\u002FGC-MS metabolomics, framing each step as a measurement model that can create or erase biological signal. Use when processing a peak\u002Ffeature table before statistical analysis, choosing a drift-correction or sample-normalization method, deciding QC RSD vs D-ratio filtering, or handling left-censored missing values. The feature table is produced by metabolomics\u002Fxcms-preprocessing or metabolomics\u002Fmsdial-preprocessing; transformation\u002Fscaling for modeling defers to metabolomics\u002Fstatistical-analysis; cross-study design issues link to experimental-design\u002Fbatch-design.","https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Funtargeted-metabolomics-analyst\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fbio-metabolomics-normalization-qc\u002FSKILL.md",[],[],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":67,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":19},"01M7CW1SWVM0NYHVE6Q7MA3G7R","bio-metabolomics-msdial-preprocessing","Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA\u002FDIA (SWATH) or GC-MS raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MSDIALCUI console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics\u002Fxcms-preprocessing; for lipid annotation mode see metabolomics\u002Flipidomics; for MSI-level confidence honesty see metabolomics\u002Fmetabolite-annotation; for drift correction and QC see metabolomics\u002Fnormalization-qc.","https:\u002F\u002Fgithub.com\u002Fmrsonord2240\u002Fopenscience-specialists\u002Fblob\u002Fmain\u002Fspecialists\u002Funtargeted-metabolomics-analyst\u002Fversions\u002F1.0.0\u002Fpackage\u002Fskills\u002Fbio-metabolomics-msdial-preprocessing\u002FSKILL.md",[],[]]