[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71NQ2P9RGBA977Q4W4BHD3J":3,"skill-same:01M71NQ2P9RGBA977Q4W4BHD3J":125},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":32,"publishedAt":53,"linkOnly":54,"markdownContent":55,"markdownBody":56,"frontMatter":57,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M71NQ2P9RGBA977Q4W4BHD3J","instrument-data-to-allotrope",null,"Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS\u002FELN systems, or generating parser code for production pipelines.","vitalikpestov\u002FSkills-Catalog","vitalikpestov","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F13882997?v=4","01M71N4QG0CWRXX4R24JQZXDDQ","Vitalik Pestov","01M71N4QGMSYRW607DDSE0CQMM",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002FSKILL.md",[19,24,27],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":25,"categoryName":26,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000004","电子表格",{"categoryNo":28,"categoryName":29,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52],"data","instrument","instrument data to allotrope","python","json format","for","import","full","parser","python code","common","code","analysis","when","auto","files","xlsx","model","types","PDF","2026-11-10 10:56:23",false,"---\nname: instrument-data-to-allotrope\ndescription: Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS\u002FELN systems, or generating parser code for production pipelines.\n---\n\n# Instrument Data to Allotrope Converter\n\nConvert instrument files into standardized Allotrope Simple Model (ASM) format for LIMS upload, data lakes, or handoff to data engineering teams.\n\n> **Note: This is an Example Skill**\n>\n> This skill demonstrates how skills can support your data engineering tasks—automating schema transformations, parsing instrument outputs, and generating production-ready code.\n>\n> **To customize for your organization:**\n> - Modify the `references\u002F` files to include your company's specific schemas or ontology mappings\n> - Use an MCP server to connect to systems that define your schemas (e.g., your LIMS, data catalog, or schema registry)\n> - Extend the `scripts\u002F` to handle proprietary instrument formats or internal data standards\n>\n> This pattern can be adapted for any data transformation workflow where you need to convert between formats or validate against organizational standards.\n\n## Workflow Overview\n\n1. **Detect instrument type** from file contents (auto-detect or user-specified)\n2. **Parse file** using allotropy library (native) or flexible fallback parser\n3. **Generate outputs**:\n   - ASM JSON (full semantic structure)\n   - Flattened CSV (2D tabular format)\n   - Python parser code (for data engineer handoff)\n4. **Deliver** files with summary and usage instructions\n\n> **When Uncertain:** If you're unsure how to map a field to ASM (e.g., is this raw data or calculated? device setting or environmental condition?), ask the user for clarification. Refer to `references\u002Ffield_classification_guide.md` for guidance, but when ambiguity remains, confirm with the user rather than guessing.\n\n## Quick Start\n\n```python\n# Install requirements first\npip install allotropy pandas openpyxl pdfplumber --break-system-packages\n\n# Core conversion\nfrom allotropy.parser_factory import Vendor\nfrom allotropy.to_allotrope import allotrope_from_file\n\n# Convert with allotropy\nasm = allotrope_from_file(\"instrument_data.csv\", Vendor.BECKMAN_VI_CELL_BLU)\n```\n\n## Output Format Selection\n\n**ASM JSON (default)** - Full semantic structure with ontology URIs\n- Best for: LIMS systems expecting ASM, data lakes, long-term archival\n- Validates against Allotrope schemas\n\n**Flattened CSV** - 2D tabular representation\n- Best for: Quick analysis, Excel users, systems without JSON support\n- Each measurement becomes one row with metadata repeated\n\n**Both** - Generate both formats for maximum flexibility\n\n## Calculated Data Handling\n\n**IMPORTANT:** Separate raw measurements from calculated\u002Fderived values.\n\n- **Raw data** → `measurement-document` (direct instrument readings)\n- **Calculated data** → `calculated-data-aggregate-document` (derived values)\n\nCalculated values MUST include traceability via `data-source-aggregate-document`:\n\n```json\n\"calculated-data-aggregate-document\": {\n  \"calculated-data-document\": [{\n    \"calculated-data-identifier\": \"SAMPLE_B1_DIN_001\",\n    \"calculated-data-name\": \"DNA integrity number\",\n    \"calculated-result\": {\"value\": 9.5, \"unit\": \"(unitless)\"},\n    \"data-source-aggregate-document\": {\n      \"data-source-document\": [{\n        \"data-source-identifier\": \"SAMPLE_B1_MEASUREMENT\",\n        \"data-source-feature\": \"electrophoresis trace\"\n      }]\n    }\n  }]\n}\n```\n\n**Common calculated fields by instrument type:**\n| Instrument | Calculated Fields |\n|------------|-------------------|\n| Cell counter | Viability %, cell density dilution-adjusted values |\n| Spectrophotometer | Concentration (from absorbance), 260\u002F280 ratio |\n| Plate reader | Concentrations from standard curve, %CV |\n| Electrophoresis | DIN\u002FRIN, region concentrations, average sizes |\n| qPCR | Relative quantities, fold change |\n\nSee `references\u002Ffield_classification_guide.md` for detailed guidance on raw vs. calculated classification.\n\n## Validation\n\nAlways validate ASM output before delivering to the user:\n\n```bash\npython scripts\u002Fvalidate_asm.py output.json\npython scripts\u002Fvalidate_asm.py output.json --reference known_good.json  # Compare to reference\npython scripts\u002Fvalidate_asm.py output.json --strict  # Treat warnings as errors\n```\n\n**Validation Rules:**\n- Based on Allotrope ASM specification (December 2024)\n- Last updated: 2026-01-07\n- Source: https:\u002F\u002Fgitlab.com\u002Fallotrope-public\u002Fasm\n\n**Soft Validation Approach:**\nUnknown techniques, units, or sample roles generate **warnings** (not errors) to allow for forward compatibility. If Allotrope adds new values after December 2024, the validator won't block them—it will flag them for manual verification. Use `--strict` mode to treat warnings as errors if you need stricter validation.\n\n**What it checks:**\n- Correct technique selection (e.g., multi-analyte profiling vs plate reader)\n- Field naming conventions (space-separated, not hyphenated)\n- Calculated data has traceability (`data-source-aggregate-document`)\n- Unique identifiers exist for measurements and calculated values\n- Required metadata present\n- Valid units and sample roles (with soft validation for unknown values)\n\n## Supported Instruments\n\nSee `references\u002Fsupported_instruments.md` for complete list. Key instruments:\n\n| Category | Instruments |\n|----------|-------------|\n| Cell Counting | Vi-CELL BLU, Vi-CELL XR, NucleoCounter |\n| Spectrophotometry | NanoDrop One\u002FEight\u002F8000, Lunatic |\n| Plate Readers | SoftMax Pro, EnVision, Gen5, CLARIOstar |\n| ELISA | SoftMax Pro, BMG MARS, MSD Workbench |\n| qPCR | QuantStudio, Bio-Rad CFX |\n| Chromatography | Empower, Chromeleon |\n\n## Detection & Parsing Strategy\n\n### Tier 1: Native allotropy parsing (PREFERRED)\n**Always try allotropy first.** Check available vendors directly:\n\n```python\nfrom allotropy.parser_factory import Vendor\n\n# List all supported vendors\nfor v in Vendor:\n    print(f\"{v.name}\")\n\n# Common vendors:\n# AGILENT_TAPESTATION_ANALYSIS  (for TapeStation XML)\n# BECKMAN_VI_CELL_BLU\n# THERMO_FISHER_NANODROP_EIGHT\n# MOLDEV_SOFTMAX_PRO\n# APPBIO_QUANTSTUDIO\n# ... many more\n```\n\n**When the user provides a file, check if allotropy supports it before falling back to manual parsing.** The `scripts\u002Fconvert_to_asm.py` auto-detection only covers a subset of allotropy vendors.\n\n### Tier 2: Flexible fallback parsing\n**Only use if allotropy doesn't support the instrument.** This fallback:\n- Does NOT generate `calculated-data-aggregate-document`\n- Does NOT include full traceability\n- Produces simplified ASM structure\n\nUse flexible parser with:\n- Column name fuzzy matching\n- Unit extraction from headers\n- Metadata extraction from file structure\n\n### Tier 3: PDF extraction\nFor PDF-only files, extract tables using pdfplumber, then apply Tier 2 parsing.\n\n## Pre-Parsing Checklist\n\nBefore writing a custom parser, ALWAYS:\n\n1. **Check if allotropy supports it** - Use native parser if available\n2. **Find a reference ASM file** - Check `references\u002Fexamples\u002F` or ask user\n3. **Review instrument-specific guide** - Check `references\u002Finstrument_guides\u002F`\n4. **Validate against reference** - Run `validate_asm.py --reference \u003Cfile>`\n\n## Common Mistakes to Avoid\n\n| Mistake | Correct Approach |\n|---------|------------------|\n| Manifest as object | Use URL string |\n| Lowercase detection types | Use \"Absorbance\" not \"absorbance\" |\n| \"emission wavelength setting\" | Use \"detector wavelength setting\" for emission |\n| All measurements in one document | Group by well\u002Fsample location |\n| Missing procedure metadata | Extract ALL device settings per measurement |\n\n## Code Export for Data Engineers\n\nGenerate standalone Python scripts that scientists can hand off:\n\n```python\n# Export parser code\npython scripts\u002Fexport_parser.py --input \"data.csv\" --vendor \"VI_CELL_BLU\" --output \"parser_script.py\"\n```\n\nThe exported script:\n- Has no external dependencies beyond pandas\u002Fallotropy\n- Includes inline documentation\n- Can run in Jupyter notebooks\n- Is production-ready for data pipelines\n\n## File Structure\n\n```\ninstrument-data-to-allotrope\u002F\n├── SKILL.md                          # This file\n├── scripts\u002F\n│   ├── convert_to_asm.py            # Main conversion script\n│   ├── flatten_asm.py               # ASM → 2D CSV conversion\n│   ├── export_parser.py             # Generate standalone parser code\n│   └── validate_asm.py              # Validate ASM output quality\n└── references\u002F\n    ├── supported_instruments.md     # Full instrument list with Vendor enums\n    ├── asm_schema_overview.md       # ASM structure reference\n    ├── field_classification_guide.md # Where to put different field types\n    └── flattening_guide.md          # How flattening works\n```\n\n## Usage Examples\n\n### Example 1: Vi-CELL BLU file\n```\nUser: \"Convert this cell counting data to Allotrope format\"\n[uploads viCell_Results.xlsx]\n\nClaude:\n1. Detects Vi-CELL BLU (95% confidence)\n2. Converts using allotropy native parser\n3. Outputs:\n   - viCell_Results_asm.json (full ASM)\n   - viCell_Results_flat.csv (2D format)\n   - viCell_parser.py (exportable code)\n```\n\n### Example 2: Request for code handoff\n```\nUser: \"I need to give our data engineer code to parse NanoDrop files\"\n\nClaude:\n1. Generates self-contained Python script\n2. Includes sample input\u002Foutput\n3. Documents all assumptions\n4. Provides Jupyter notebook version\n```\n\n### Example 3: LIMS-ready flattened output\n```\nUser: \"Convert this ELISA data to a CSV I can upload to our LIMS\"\n\nClaude:\n1. Parses plate reader data\n2. Generates flattened CSV with columns:\n   - sample_identifier, well_position, measurement_value, measurement_unit\n   - instrument_serial_number, analysis_datetime, assay_type\n3. Validates against common LIMS import requirements\n```\n\n## Implementation Notes\n\n### Installing allotropy\n```bash\npip install allotropy --break-system-packages\n```\n\n### Handling parse failures\nIf allotropy native parsing fails:\n1. Log the error for debugging\n2. Fall back to flexible parser\n3. Report reduced metadata completeness to user\n4. Suggest exporting different format from instrument\n\n### ASM Schema Validation\nValidate output against Allotrope schemas when available:\n```python\nimport jsonschema\n# Schema URLs in references\u002Fasm_schema_overview.md\n```\n","# Instrument Data to Allotrope Converter\n\nConvert instrument files into standardized Allotrope Simple Model (ASM) format for LIMS upload, data lakes, or handoff to data engineering teams.\n\n> **Note: This is an Example Skill**\n>\n> This skill demonstrates how skills can support your data engineering tasks—automating schema transformations, parsing instrument outputs, and generating production-ready code.\n>\n> **To customize for your organization:**\n> - Modify the `references\u002F` files to include your company's specific schemas or ontology mappings\n> - Use an MCP server to connect to systems that define your schemas (e.g., your LIMS, data catalog, or schema registry)\n> - Extend the `scripts\u002F` to handle proprietary instrument formats or internal data standards\n>\n> This pattern can be adapted for any data transformation workflow where you need to convert between formats or validate against organizational standards.\n\n## Workflow Overview\n\n1. **Detect instrument type** from file contents (auto-detect or user-specified)\n2. **Parse file** using allotropy library (native) or flexible fallback parser\n3. **Generate outputs**:\n   - ASM JSON (full semantic structure)\n   - Flattened CSV (2D tabular format)\n   - Python parser code (for data engineer handoff)\n4. **Deliver** files with summary and usage instructions\n\n> **When Uncertain:** If you're unsure how to map a field to ASM (e.g., is this raw data or calculated? device setting or environmental condition?), ask the user for clarification. Refer to `references\u002Ffield_classification_guide.md` for guidance, but when ambiguity remains, confirm with the user rather than guessing.\n\n## Quick Start\n\n```python\n# Install requirements first\npip install allotropy pandas openpyxl pdfplumber --break-system-packages\n\n# Core conversion\nfrom allotropy.parser_factory import Vendor\nfrom allotropy.to_allotrope import allotrope_from_file\n\n# Convert with allotropy\nasm = allotrope_from_file(\"instrument_data.csv\", Vendor.BECKMAN_VI_CELL_BLU)\n```\n\n## Output Format Selection\n\n**ASM JSON (default)** - Full semantic structure with ontology URIs\n- Best for: LIMS systems expecting ASM, data lakes, long-term archival\n- Validates against Allotrope schemas\n\n**Flattened CSV** - 2D tabular representation\n- Best for: Quick analysis, Excel users, systems without JSON support\n- Each measurement becomes one row with metadata repeated\n\n**Both** - Generate both formats for maximum flexibility\n\n## Calculated Data Handling\n\n**IMPORTANT:** Separate raw measurements from calculated\u002Fderived values.\n\n- **Raw data** → `measurement-document` (direct instrument readings)\n- **Calculated data** → `calculated-data-aggregate-document` (derived values)\n\nCalculated values MUST include traceability via `data-source-aggregate-document`:\n\n```json\n\"calculated-data-aggregate-document\": {\n  \"calculated-data-document\": [{\n    \"calculated-data-identifier\": \"SAMPLE_B1_DIN_001\",\n    \"calculated-data-name\": \"DNA integrity number\",\n    \"calculated-result\": {\"value\": 9.5, \"unit\": \"(unitless)\"},\n    \"data-source-aggregate-document\": {\n      \"data-source-document\": [{\n        \"data-source-identifier\": \"SAMPLE_B1_MEASUREMENT\",\n        \"data-source-feature\": \"electrophoresis trace\"\n      }]\n    }\n  }]\n}\n```\n\n**Common calculated fields by instrument type:**\n| Instrument | Calculated Fields |\n|------------|-------------------|\n| Cell counter | Viability %, cell density dilution-adjusted values |\n| Spectrophotometer | Concentration (from absorbance), 260\u002F280 ratio |\n| Plate reader | Concentrations from standard curve, %CV |\n| Electrophoresis | DIN\u002FRIN, region concentrations, average sizes |\n| qPCR | Relative quantities, fold change |\n\nSee `references\u002Ffield_classification_guide.md` for detailed guidance on raw vs. calculated classification.\n\n## Validation\n\nAlways validate ASM output before delivering to the user:\n\n```bash\npython scripts\u002Fvalidate_asm.py output.json\npython scripts\u002Fvalidate_asm.py output.json --reference known_good.json  # Compare to reference\npython scripts\u002Fvalidate_asm.py output.json --strict  # Treat warnings as errors\n```\n\n**Validation Rules:**\n- Based on Allotrope ASM specification (December 2024)\n- Last updated: 2026-01-07\n- Source: https:\u002F\u002Fgitlab.com\u002Fallotrope-public\u002Fasm\n\n**Soft Validation Approach:**\nUnknown techniques, units, or sample roles generate **warnings** (not errors) to allow for forward compatibility. If Allotrope adds new values after December 2024, the validator won't block them—it will flag them for manual verification. Use `--strict` mode to treat warnings as errors if you need stricter validation.\n\n**What it checks:**\n- Correct technique selection (e.g., multi-analyte profiling vs plate reader)\n- Field naming conventions (space-separated, not hyphenated)\n- Calculated data has traceability (`data-source-aggregate-document`)\n- Unique identifiers exist for measurements and calculated values\n- Required metadata present\n- Valid units and sample roles (with soft validation for unknown values)\n\n## Supported Instruments\n\nSee `references\u002Fsupported_instruments.md` for complete list. Key instruments:\n\n| Category | Instruments |\n|----------|-------------|\n| Cell Counting | Vi-CELL BLU, Vi-CELL XR, NucleoCounter |\n| Spectrophotometry | NanoDrop One\u002FEight\u002F8000, Lunatic |\n| Plate Readers | SoftMax Pro, EnVision, Gen5, CLARIOstar |\n| ELISA | SoftMax Pro, BMG MARS, MSD Workbench |\n| qPCR | QuantStudio, Bio-Rad CFX |\n| Chromatography | Empower, Chromeleon |\n\n## Detection & Parsing Strategy\n\n### Tier 1: Native allotropy parsing (PREFERRED)\n**Always try allotropy first.** Check available vendors directly:\n\n```python\nfrom allotropy.parser_factory import Vendor\n\n# List all supported vendors\nfor v in Vendor:\n    print(f\"{v.name}\")\n\n# Common vendors:\n# AGILENT_TAPESTATION_ANALYSIS  (for TapeStation XML)\n# BECKMAN_VI_CELL_BLU\n# THERMO_FISHER_NANODROP_EIGHT\n# MOLDEV_SOFTMAX_PRO\n# APPBIO_QUANTSTUDIO\n# ... many more\n```\n\n**When the user provides a file, check if allotropy supports it before falling back to manual parsing.** The `scripts\u002Fconvert_to_asm.py` auto-detection only covers a subset of allotropy vendors.\n\n### Tier 2: Flexible fallback parsing\n**Only use if allotropy doesn't support the instrument.** This fallback:\n- Does NOT generate `calculated-data-aggregate-document`\n- Does NOT include full traceability\n- Produces simplified ASM structure\n\nUse flexible parser with:\n- Column name fuzzy matching\n- Unit extraction from headers\n- Metadata extraction from file structure\n\n### Tier 3: PDF extraction\nFor PDF-only files, extract tables using pdfplumber, then apply Tier 2 parsing.\n\n## Pre-Parsing Checklist\n\nBefore writing a custom parser, ALWAYS:\n\n1. **Check if allotropy supports it** - Use native parser if available\n2. **Find a reference ASM file** - Check `references\u002Fexamples\u002F` or ask user\n3. **Review instrument-specific guide** - Check `references\u002Finstrument_guides\u002F`\n4. **Validate against reference** - Run `validate_asm.py --reference \u003Cfile>`\n\n## Common Mistakes to Avoid\n\n| Mistake | Correct Approach |\n|---------|------------------|\n| Manifest as object | Use URL string |\n| Lowercase detection types | Use \"Absorbance\" not \"absorbance\" |\n| \"emission wavelength setting\" | Use \"detector wavelength setting\" for emission |\n| All measurements in one document | Group by well\u002Fsample location |\n| Missing procedure metadata | Extract ALL device settings per measurement |\n\n## Code Export for Data Engineers\n\nGenerate standalone Python scripts that scientists can hand off:\n\n```python\n# Export parser code\npython scripts\u002Fexport_parser.py --input \"data.csv\" --vendor \"VI_CELL_BLU\" --output \"parser_script.py\"\n```\n\nThe exported script:\n- Has no external dependencies beyond pandas\u002Fallotropy\n- Includes inline documentation\n- Can run in Jupyter notebooks\n- Is production-ready for data pipelines\n\n## File Structure\n\n```\ninstrument-data-to-allotrope\u002F\n├── SKILL.md                          # This file\n├── scripts\u002F\n│   ├── convert_to_asm.py            # Main conversion script\n│   ├── flatten_asm.py               # ASM → 2D CSV conversion\n│   ├── export_parser.py             # Generate standalone parser code\n│   └── validate_asm.py              # Validate ASM output quality\n└── references\u002F\n    ├── supported_instruments.md     # Full instrument list with Vendor enums\n    ├── asm_schema_overview.md       # ASM structure reference\n    ├── field_classification_guide.md # Where to put different field types\n    └── flattening_guide.md          # How flattening works\n```\n\n## Usage Examples\n\n### Example 1: Vi-CELL BLU file\n```\nUser: \"Convert this cell counting data to Allotrope format\"\n[uploads viCell_Results.xlsx]\n\nClaude:\n1. Detects Vi-CELL BLU (95% confidence)\n2. Converts using allotropy native parser\n3. Outputs:\n   - viCell_Results_asm.json (full ASM)\n   - viCell_Results_flat.csv (2D format)\n   - viCell_parser.py (exportable code)\n```\n\n### Example 2: Request for code handoff\n```\nUser: \"I need to give our data engineer code to parse NanoDrop files\"\n\nClaude:\n1. Generates self-contained Python script\n2. Includes sample input\u002Foutput\n3. Documents all assumptions\n4. Provides Jupyter notebook version\n```\n\n### Example 3: LIMS-ready flattened output\n```\nUser: \"Convert this ELISA data to a CSV I can upload to our LIMS\"\n\nClaude:\n1. Parses plate reader data\n2. Generates flattened CSV with columns:\n   - sample_identifier, well_position, measurement_value, measurement_unit\n   - instrument_serial_number, analysis_datetime, assay_type\n3. Validates against common LIMS import requirements\n```\n\n## Implementation Notes\n\n### Installing allotropy\n```bash\npip install allotropy --break-system-packages\n```\n\n### Handling parse failures\nIf allotropy native parsing fails:\n1. Log the error for debugging\n2. Fall back to flexible parser\n3. Report reduced metadata completeness to user\n4. Suggest exporting different format from instrument\n\n### ASM Schema Validation\nValidate output against Allotrope schemas when available:\n```python\nimport jsonschema\n# Schema URLs in references\u002Fasm_schema_overview.md\n```\n",{"name":5,"description":7},"main","fbfb242f793fdf040f3a1a6b100896c188c228c8","_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope",1,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog","https:\u002F\u002Fgithub.com\u002Fvitalikpestov","Listing on crypto exchanges","2026-06-15 22:12:56",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add vitalikpestov\u002FSkills-Catalog --skill instrument-data-to-allotrope",{"tool":72,"command":73},"bunx","bunx skills add vitalikpestov\u002FSkills-Catalog --skill instrument-data-to-allotrope",{"tool":75,"command":76},"pnpm","pnpm dlx skills add vitalikpestov\u002FSkills-Catalog --skill instrument-data-to-allotrope","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002FSKILL.md",true,[80,85,91,95,99,103,107,113,117,121],{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":16},"LICENSE.txt","LICENSE","许可证",10254,{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fasm_schema_overview.md","REFERENCE","参考文档",6110,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002Freferences\u002Fasm_schema_overview.md",{"path":92,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Ffield_classification_guide.md",17665,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002Freferences\u002Ffield_classification_guide.md",{"path":96,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fflattening_guide.md",7500,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002Freferences\u002Fflattening_guide.md",{"path":100,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fsupported_instruments.md",5304,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002Freferences\u002Fsupported_instruments.md",{"path":104,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":105,"sourceHtmlUrl":106},"requirements.txt",811,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002Frequirements.txt",{"path":108,"resourceType":109,"resourceTypeDesc":110,"sizeBytes":111,"sourceHtmlUrl":112},"scripts\u002Fconvert_to_asm.py","SCRIPT","脚本",17278,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002Fscripts\u002Fconvert_to_asm.py",{"path":114,"resourceType":109,"resourceTypeDesc":110,"sizeBytes":115,"sourceHtmlUrl":116},"scripts\u002Fexport_parser.py",13847,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002Fscripts\u002Fexport_parser.py",{"path":118,"resourceType":109,"resourceTypeDesc":110,"sizeBytes":119,"sourceHtmlUrl":120},"scripts\u002Fflatten_asm.py",7339,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002Fscripts\u002Fflatten_asm.py",{"path":122,"resourceType":109,"resourceTypeDesc":110,"sizeBytes":123,"sourceHtmlUrl":124},"scripts\u002Fvalidate_asm.py",36719,"https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_AI-Automation\u002Fknowledge-work-plugins-main\u002Fbio-research\u002Fskills\u002Finstrument-data-to-allotrope\u002Fscripts\u002Fvalidate_asm.py",[126,167,189,205,228,261],{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":145,"publishedAt":166},"01M71V1MC994P553631JZSWVYD","solana-dev","Use when user asks to \"build a Solana dapp\", \"write an Anchor program\", \"create a token\", \"debug Solana errors\", \"set up wallet connection\", \"test my Solana program\", \"deploy to devnet\", or \"explain Solana concepts\" (rent, accounts, PDAs, CPIs, etc.). End-to-end Solana development playbook covering wallet connection, Anchor\u002FPinocchio programs, Codama client generation, LiteSVM\u002FMollusk\u002FSurfpool testing, and security checklists. Integrates with the Solana MCP server for live documentation search. Prefers framework-kit (@solana\u002Fclient + @solana\u002Freact-hooks) for UI, wallet-standard-first connection (incl. ConnectorKit), @solana\u002Fkit for client\u002FRPC code, and @solana\u002Fweb3-compat for legacy boundaries.","MIT","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Web3\u002Fsolana-dev\u002Fskill\u002FSKILL.md",[133,136,139,142],{"categoryNo":134,"categoryName":135,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":137,"categoryName":138,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":140,"categoryName":141,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":143,"categoryName":144,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000030","开发工程师",[146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165],"dev","solana dev","solana","Wallet","search","testing","solana cli","kit","MCP","web3","react","create","server","write","playbook","live","security","ui","concepts","toolchain","2026-11-10 13:02:02",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":166},"01M71TYQPHE2YGKTT7DG9QCW3K","generating-trading-signals","Generate trading signals using technical indicators (RSI, MACD, Bollinger Bands, etc.).\nCombines multiple indicators into composite signals with confidence scores.\nUse when analyzing assets for trading opportunities or checking technical indicators.\nTrigger with phrases like \"get trading signals\", \"check indicators\", \"analyze for entry\",\n\"scan for opportunities\", \"generate buy\u002Fsell signals\", or \"technical analysis\".","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Trading-Finance\u002Fgenerating-trading-signals\u002FSKILL.md",[],[174,175,176,177,178,179,38,180,181,182,36,183,184,46,185,45,44,186,187,188],"generating trading signals","signals","trading signals","trading","get","technical indicators","scan","rsi","trigger","technical","use","Bash","io","check","analyze",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":195,"publishedAt":166},"01M71TY4CR3GKGQ3M12X9WN95Y","prediction-monitor","Prediction Monitor - Auto-activating skill for ML Deployment.\nTriggers on: prediction monitor, prediction monitor\nPart of the ML Deployment skill category.","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Trading-Finance\u002FAI Predictions\u002Fprediction-monitor\u002FSKILL.md",[],[196,197,198,38,199,200,159,201,202,203,47,204,185],"prediction","monitor","prediction monitor","deployment","read","edit","triggers","grep","ml",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":211,"publishedAt":166},"01M71TXD9MYXZC39HCPP909HTZ","strategy-advisor","High-level strategic thinking and business decision guidance for planning and direction-setting.\nUse when: making strategic decisions, evaluating business options, setting direction, analyzing\ntrade-offs, or when user mentions strategy, business planning, competitive analysis, or long-term planning.","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Fstrategy-advisor\u002FSKILL.md",[],[212,213,214,215,216,217,218,219,220,38,221,222,223,184,224,225,46,226,45,227],"strategy advisor","strategy","advisor","competitive","setting","thinking","planning","direction","trade-offs","long","decision","strategic","business","apps","decisions","trade",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":232,"sourceHtmlUrl":233,"categories":234,"keywords":241,"publishedAt":166},"01M71TWHBT2R1P1FNWTW67ZREP","app-paywall-pilot","Design, audit, debug, and implement App Store-compliant mobile paywalls, subscription flows, pricing screens, trials, offers, feature gates, and subscription lifecycle UX. Use when the user asks to audit my paywall, fix App Store 3.1.2 rejection, improve trial-to-paid, choose plans\u002Fprices\u002Ftrials, review RevenueCat\u002FAdapty\u002FApphud\u002FSuperwall setup, calculate LTV\u002FROAS, or build paywall implementation.","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Ffrom-dosesync\u002Fapp-paywall-pilot\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Ffrom-dosesync\u002Fapp-paywall-pilot\u002FSKILL.md",[235,238],{"categoryNo":236,"categoryName":237,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":239,"categoryName":240,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000032","设计师",[242,243,244,245,246,247,248,249,250,251,252,253,46,254,255,256,257,258,259,260],"app","paywall","app paywall pilot","paid","paywalls","prices","choose","flows","fix","improve","design","app store","review","audit","ux","subscription","mobile","my","calculate",{"skillNo":262,"name":263,"nameZh":6,"description":264,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":265,"sourceHtmlUrl":266,"categories":267,"keywords":270,"publishedAt":166},"01M71TWET8GFKEB7ZHJPMQX29V","figma","Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Ffigma\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fvitalikpestov\u002FSkills-Catalog\u002Fblob\u002Fmain\u002F_Product-Apps\u002Ffigma\u002FSKILL.md",[268,269],{"categoryNo":140,"categoryName":141,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":239,"categoryName":240,"taxonomyType":30,"taxonomyTypeDesc":31},[263,271,272,273,274,182,252,154,184,46,275,276,44,277,158,278,279,280,281,282],"fetch","ids","mcp setup","translate","mcp server","Variables","task","Nodes","figma mcp","code implementation","node","assets"]