[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFG6W1W25P0BA3ETTHKW5V":3,"skill-same:01M6AFG6W1W25P0BA3ETTHKW5V":82},{"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":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":45,"ownerHtmlUrl":46,"ownerBlogUrl":47,"ownerDescription":48,"updatedAt":49,"install":50,"resources":63},"01M6AFG6W1W25P0BA3ETTHKW5V","matchms",null,"Mass spectrometry analysis. Process mzML\u002FMGF\u002FMSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmatchms\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],"mass spectrometry","cosine","for","spectral","processing","metabolomics","harmonization","similarity","id","metadata","compound","ms","process","data processing","analysis","data","2026-11-10 10:12:30",false,"---\nname: matchms\ndescription: \"Mass spectrometry analysis. Process mzML\u002FMGF\u002FMSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.\"\n---\n\n# Matchms\n\n## Overview\n\nMatchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows.\n\n## Core Capabilities\n\n### 1. Importing and Exporting Mass Spectrometry Data\n\nLoad spectra from multiple file formats and export processed data:\n\n```python\nfrom matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json\nfrom matchms.exporting import save_as_mgf, save_as_msp, save_as_json\n\n# Import spectra\nspectra = list(load_from_mgf(\"spectra.mgf\"))\nspectra = list(load_from_mzml(\"data.mzML\"))\nspectra = list(load_from_msp(\"library.msp\"))\n\n# Export processed spectra\nsave_as_mgf(spectra, \"output.mgf\")\nsave_as_json(spectra, \"output.json\")\n```\n\n**Supported formats:**\n- mzML and mzXML (raw mass spectrometry formats)\n- MGF (Mascot Generic Format)\n- MSP (spectral library format)\n- JSON (GNPS-compatible)\n- metabolomics-USI references\n- Pickle (Python serialization)\n\nFor detailed importing\u002Fexporting documentation, consult `references\u002Fimporting_exporting.md`.\n\n### 2. Spectrum Filtering and Processing\n\nApply comprehensive filters to standardize metadata and refine peak data:\n\n```python\nfrom matchms.filtering import default_filters, normalize_intensities\nfrom matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks\n\n# Apply default metadata harmonization filters\nspectrum = default_filters(spectrum)\n\n# Normalize peak intensities\nspectrum = normalize_intensities(spectrum)\n\n# Filter peaks by relative intensity\nspectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)\n\n# Require minimum peaks\nspectrum = require_minimum_number_of_peaks(spectrum, n_required=5)\n```\n\n**Filter categories:**\n- **Metadata processing**: Harmonize compound names, derive chemical structures, standardize adducts, correct charges\n- **Peak filtering**: Normalize intensities, select by m\u002Fz or intensity, remove precursor peaks\n- **Quality control**: Require minimum peaks, validate precursor m\u002Fz, ensure metadata completeness\n- **Chemical annotation**: Add fingerprints, derive InChI\u002FSMILES, repair structural mismatches\n\nMatchms provides 40+ filters. For the complete filter reference, consult `references\u002Ffiltering.md`.\n\n### 3. Calculating Spectral Similarities\n\nCompare spectra using various similarity metrics:\n\n```python\nfrom matchms import calculate_scores\nfrom matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian\n\n# Calculate cosine similarity (fast, greedy algorithm)\nscores = calculate_scores(references=library_spectra,\n                         queries=query_spectra,\n                         similarity_function=CosineGreedy())\n\n# Calculate modified cosine (accounts for precursor m\u002Fz differences)\nscores = calculate_scores(references=library_spectra,\n                         queries=query_spectra,\n                         similarity_function=ModifiedCosine(tolerance=0.1))\n\n# Get best matches\nbest_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]\n```\n\n**Available similarity functions:**\n- **CosineGreedy\u002FCosineHungarian**: Peak-based cosine similarity with different matching algorithms\n- **ModifiedCosine**: Cosine similarity accounting for precursor mass differences\n- **NeutralLossesCosine**: Similarity based on neutral loss patterns\n- **FingerprintSimilarity**: Molecular structure similarity using fingerprints\n- **MetadataMatch**: Compare user-defined metadata fields\n- **PrecursorMzMatch\u002FParentMassMatch**: Simple mass-based filtering\n\nFor detailed similarity function documentation, consult `references\u002Fsimilarity.md`.\n\n### 4. Building Processing Pipelines\n\nCreate reproducible, multi-step analysis workflows:\n\n```python\nfrom matchms import SpectrumProcessor\nfrom matchms.filtering import default_filters, normalize_intensities\nfrom matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz\n\n# Define a processing pipeline\nprocessor = SpectrumProcessor([\n    default_filters,\n    normalize_intensities,\n    lambda s: select_by_relative_intensity(s, intensity_from=0.01),\n    lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17)\n])\n\n# Apply to all spectra\nprocessed_spectra = [processor(s) for s in spectra]\n```\n\n### 5. Working with Spectrum Objects\n\nThe core `Spectrum` class contains mass spectral data:\n\n```python\nfrom matchms import Spectrum\nimport numpy as np\n\n# Create a spectrum\nmz = np.array([100.0, 150.0, 200.0, 250.0])\nintensities = np.array([0.1, 0.5, 0.9, 0.3])\nmetadata = {\"precursor_mz\": 250.5, \"ionmode\": \"positive\"}\n\nspectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata)\n\n# Access spectrum properties\nprint(spectrum.peaks.mz)           # m\u002Fz values\nprint(spectrum.peaks.intensities)  # Intensity values\nprint(spectrum.get(\"precursor_mz\")) # Metadata field\n\n# Visualize spectra\nspectrum.plot()\nspectrum.plot_against(reference_spectrum)\n```\n\n### 6. Metadata Management\n\nStandardize and harmonize spectrum metadata:\n\n```python\n# Metadata is automatically harmonized\nspectrum.set(\"Precursor_mz\", 250.5)  # Gets harmonized to lowercase key\nprint(spectrum.get(\"precursor_mz\"))   # Returns 250.5\n\n# Derive chemical information\nfrom matchms.filtering import derive_inchi_from_smiles, derive_inchikey_from_inchi\nfrom matchms.filtering import add_fingerprint\n\nspectrum = derive_inchi_from_smiles(spectrum)\nspectrum = derive_inchikey_from_inchi(spectrum)\nspectrum = add_fingerprint(spectrum, fingerprint_type=\"morgan\", nbits=2048)\n```\n\n## Common Workflows\n\nFor typical mass spectrometry analysis workflows, including:\n- Loading and preprocessing spectral libraries\n- Matching unknown spectra against reference libraries\n- Quality filtering and data cleaning\n- Large-scale similarity comparisons\n- Network-based spectral clustering\n\nConsult `references\u002Fworkflows.md` for detailed examples.\n\n## Installation\n\n```bash\nuv pip install matchms\n```\n\nFor molecular structure processing (SMILES, InChI):\n```bash\nuv pip install matchms[chemistry]\n```\n\n## Reference Documentation\n\nDetailed reference documentation is available in the `references\u002F` directory:\n- `filtering.md` - Complete filter function reference with descriptions\n- `similarity.md` - All similarity metrics and when to use them\n- `importing_exporting.md` - File format details and I\u002FO operations\n- `workflows.md` - Common analysis patterns and examples\n\nLoad these references as needed for detailed information about specific matchms capabilities.\n","# Matchms\n\n## Overview\n\nMatchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows.\n\n## Core Capabilities\n\n### 1. Importing and Exporting Mass Spectrometry Data\n\nLoad spectra from multiple file formats and export processed data:\n\n```python\nfrom matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json\nfrom matchms.exporting import save_as_mgf, save_as_msp, save_as_json\n\n# Import spectra\nspectra = list(load_from_mgf(\"spectra.mgf\"))\nspectra = list(load_from_mzml(\"data.mzML\"))\nspectra = list(load_from_msp(\"library.msp\"))\n\n# Export processed spectra\nsave_as_mgf(spectra, \"output.mgf\")\nsave_as_json(spectra, \"output.json\")\n```\n\n**Supported formats:**\n- mzML and mzXML (raw mass spectrometry formats)\n- MGF (Mascot Generic Format)\n- MSP (spectral library format)\n- JSON (GNPS-compatible)\n- metabolomics-USI references\n- Pickle (Python serialization)\n\nFor detailed importing\u002Fexporting documentation, consult `references\u002Fimporting_exporting.md`.\n\n### 2. Spectrum Filtering and Processing\n\nApply comprehensive filters to standardize metadata and refine peak data:\n\n```python\nfrom matchms.filtering import default_filters, normalize_intensities\nfrom matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks\n\n# Apply default metadata harmonization filters\nspectrum = default_filters(spectrum)\n\n# Normalize peak intensities\nspectrum = normalize_intensities(spectrum)\n\n# Filter peaks by relative intensity\nspectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)\n\n# Require minimum peaks\nspectrum = require_minimum_number_of_peaks(spectrum, n_required=5)\n```\n\n**Filter categories:**\n- **Metadata processing**: Harmonize compound names, derive chemical structures, standardize adducts, correct charges\n- **Peak filtering**: Normalize intensities, select by m\u002Fz or intensity, remove precursor peaks\n- **Quality control**: Require minimum peaks, validate precursor m\u002Fz, ensure metadata completeness\n- **Chemical annotation**: Add fingerprints, derive InChI\u002FSMILES, repair structural mismatches\n\nMatchms provides 40+ filters. For the complete filter reference, consult `references\u002Ffiltering.md`.\n\n### 3. Calculating Spectral Similarities\n\nCompare spectra using various similarity metrics:\n\n```python\nfrom matchms import calculate_scores\nfrom matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian\n\n# Calculate cosine similarity (fast, greedy algorithm)\nscores = calculate_scores(references=library_spectra,\n                         queries=query_spectra,\n                         similarity_function=CosineGreedy())\n\n# Calculate modified cosine (accounts for precursor m\u002Fz differences)\nscores = calculate_scores(references=library_spectra,\n                         queries=query_spectra,\n                         similarity_function=ModifiedCosine(tolerance=0.1))\n\n# Get best matches\nbest_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]\n```\n\n**Available similarity functions:**\n- **CosineGreedy\u002FCosineHungarian**: Peak-based cosine similarity with different matching algorithms\n- **ModifiedCosine**: Cosine similarity accounting for precursor mass differences\n- **NeutralLossesCosine**: Similarity based on neutral loss patterns\n- **FingerprintSimilarity**: Molecular structure similarity using fingerprints\n- **MetadataMatch**: Compare user-defined metadata fields\n- **PrecursorMzMatch\u002FParentMassMatch**: Simple mass-based filtering\n\nFor detailed similarity function documentation, consult `references\u002Fsimilarity.md`.\n\n### 4. Building Processing Pipelines\n\nCreate reproducible, multi-step analysis workflows:\n\n```python\nfrom matchms import SpectrumProcessor\nfrom matchms.filtering import default_filters, normalize_intensities\nfrom matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz\n\n# Define a processing pipeline\nprocessor = SpectrumProcessor([\n    default_filters,\n    normalize_intensities,\n    lambda s: select_by_relative_intensity(s, intensity_from=0.01),\n    lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17)\n])\n\n# Apply to all spectra\nprocessed_spectra = [processor(s) for s in spectra]\n```\n\n### 5. Working with Spectrum Objects\n\nThe core `Spectrum` class contains mass spectral data:\n\n```python\nfrom matchms import Spectrum\nimport numpy as np\n\n# Create a spectrum\nmz = np.array([100.0, 150.0, 200.0, 250.0])\nintensities = np.array([0.1, 0.5, 0.9, 0.3])\nmetadata = {\"precursor_mz\": 250.5, \"ionmode\": \"positive\"}\n\nspectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata)\n\n# Access spectrum properties\nprint(spectrum.peaks.mz)           # m\u002Fz values\nprint(spectrum.peaks.intensities)  # Intensity values\nprint(spectrum.get(\"precursor_mz\")) # Metadata field\n\n# Visualize spectra\nspectrum.plot()\nspectrum.plot_against(reference_spectrum)\n```\n\n### 6. Metadata Management\n\nStandardize and harmonize spectrum metadata:\n\n```python\n# Metadata is automatically harmonized\nspectrum.set(\"Precursor_mz\", 250.5)  # Gets harmonized to lowercase key\nprint(spectrum.get(\"precursor_mz\"))   # Returns 250.5\n\n# Derive chemical information\nfrom matchms.filtering import derive_inchi_from_smiles, derive_inchikey_from_inchi\nfrom matchms.filtering import add_fingerprint\n\nspectrum = derive_inchi_from_smiles(spectrum)\nspectrum = derive_inchikey_from_inchi(spectrum)\nspectrum = add_fingerprint(spectrum, fingerprint_type=\"morgan\", nbits=2048)\n```\n\n## Common Workflows\n\nFor typical mass spectrometry analysis workflows, including:\n- Loading and preprocessing spectral libraries\n- Matching unknown spectra against reference libraries\n- Quality filtering and data cleaning\n- Large-scale similarity comparisons\n- Network-based spectral clustering\n\nConsult `references\u002Fworkflows.md` for detailed examples.\n\n## Installation\n\n```bash\nuv pip install matchms\n```\n\nFor molecular structure processing (SMILES, InChI):\n```bash\nuv pip install matchms[chemistry]\n```\n\n## Reference Documentation\n\nDetailed reference documentation is available in the `references\u002F` directory:\n- `filtering.md` - Complete filter function reference with descriptions\n- `similarity.md` - All similarity metrics and when to use them\n- `importing_exporting.md` - File format details and I\u002FO operations\n- `workflows.md` - Common analysis patterns and examples\n\nLoad these references as needed for detailed information about specific matchms capabilities.\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmatchms",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add davila7\u002Fclaude-code-templates --skill matchms",{"tool":56,"command":57},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill matchms",{"tool":59,"command":60},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill matchms","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmatchms\u002FSKILL.md",true,[64,70,74,78],{"path":65,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":68,"sourceHtmlUrl":69},"references\u002Ffiltering.md","REFERENCE","参考文档",10038,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmatchms\u002Freferences\u002Ffiltering.md",{"path":71,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":72,"sourceHtmlUrl":73},"references\u002Fimporting_exporting.md",10894,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmatchms\u002Freferences\u002Fimporting_exporting.md",{"path":75,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fsimilarity.md",12974,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmatchms\u002Freferences\u002Fsimilarity.md",{"path":79,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fworkflows.md",22230,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmatchms\u002Freferences\u002Fworkflows.md",[83,114,141,174,209,233],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":94,"publishedAt":35},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[89],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[95,96,97,98,99,21,100,101,102,103,104,105,106,107,108,109,110,111,112,113],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":121,"publishedAt":35},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[120],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},[122,123,124,125,126,127,128,129,130,131,132,103,133,134,135,136,137,138,139,140],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":156,"publishedAt":35},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. 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