[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FJ0AW2K7PW2H2RZR643JNP":3,"skill-same:01M6FJ0AW2K7PW2H2RZR643JNP":93},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6FJ0AW2K7PW2H2RZR643JNP","tellurium",null,"Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives. Use for reaction-network time courses, kinetic parameters, concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.","boisenoise\u002Fskills-collections","boisenoise","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F193180?v=4","01M6CDBDQK6BDN55ZMA6B6PKBM","01M6CDBDVSJ3JPTBYN26FZ7TZK",12,"MIT","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fkdense-tellurium\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"only","for","combine","with","perturbations","dynamics","courses","time","deterministic","units","python","use","model","simulation","credentials","analysis","cobrapy","Reproducible","services","state","ml","2026-11-10 10:12:30",false,"---\nname: tellurium\ndescription: Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives. Use for reaction-network time courses, kinetic parameters, concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.\nlicense: MIT\ncompatibility: Requires Python 3.11 with Tellurium 2.2.13.1, libRoadRunner 2.10.0, Antimony 3.2.0, python-libsbml 5.21.2, python-libsedml 2.0.34 and python-libcombine 0.2.20. Network is needed for installation only. Native wheels were tested on macOS ARM64. No credentials or external services.\nmetadata:\n  version: \"1.1\"\n  skill-author: K-Dense Inc.\n  upstream-version: \"2.2.13.1\"\n  last-reviewed: \"2026-10-01\"\n---\n\n# Tellurium kinetic experiments\n\n## When to use\n\nUse this skill for deterministic reaction-network trajectories and independent parameter conditions\nfrom a local model. The helper performs SBML consistency checks, CVODE integration and an actual\nCOMBINE archive replay. It exports each condition's exact SBML and the SED-ML experiment rather\nthan handing off an unrecorded notebook state.\n\n## Runtime\n\n```bash\nuv venv --python 3.11 kinetic-env\nuv pip install --python kinetic-env\u002Fbin\u002Fpython tellurium==2.2.13.1 libroadrunner==2.10.0 \\\n  antimony==3.2.0 python-libsbml==5.21.2 python-libsedml==2.0.34 python-libcombine==0.2.20\n```\n\nThe full workflow ran with these packages on macOS ARM64. It constructs SED-ML with libSEDML and\narchives with Tellurium\u002FlibCombine; PhraSEDML is not required by this helper. Headless runs can set\n`MPLBACKEND=Agg`. No plotting window is opened by the helper.\n\nThe six pinned releases were rechecked against official PyPI metadata on 2026-10-01. RoadRunner's\ndocumentation site still displays an old version banner; the solver settings below were also\nchecked against released 2.10.0 source and the installed native runtime.\n\n## Workflow\n\n1. Inspect the supplied model's compartments, species, initial conditions, boundary species,\n   reactions, parameter definitions and rules\u002Fevents. Identify the scientific question and\n   distinguish a mechanistic kinetic model from a flux-balance reconstruction. Record the\n   source model, version and any literature parameters; do not treat an example model as\n   experimentally calibrated.\n2. Check units before interpreting a trajectory. SBML reaction rates have amount\u002Ftime units;\n   species may have concentration or amount semantics. In a fixed-volume first-order model,\n   `k*A*cell` converts concentration dependence into amount\u002Ftime. The helper checks SBML\n   consistency and retains every warning, including undefined units. Undefined units are\n   reported as empty\u002Findeterminable, not silently assumed to mean SI.\n3. Select concentration outputs and an experiment in the JSON format described in\n   [references\u002Fexperiments.md](references\u002Fexperiments.md). Time values use the model's own time\n   units. The tested helper outputs concentration for species with `hasOnlySubstanceUnits=false`;\n   it rejects amount-only selections to avoid changing their meaning during SED-ML replay.\n   Zero-dimensional compartments and rate-rule models are also rejected; the latter need a separate\n   tolerance workflow because RoadRunner 2.10.0 can order scalar tolerances differently from states.\n4. Run baseline and desired constant-global-parameter changes. Every scenario starts from a\n   fresh SBML model, so previous final concentrations cannot leak into the next condition.\n   Changes to species initial values, compartment volume, assignment rules or time-varying\n   inputs require explicit model changes and corresponding tests; they are not parameter\n   mutations hidden in this helper.\n5. Examine finite outputs, signs, relevant conservation relations and timescales. Check solver\n   sensitivity by repeating at stricter tolerances when the scientific interpretation depends\n   on small differences. A smooth curve or zero archive-replay error does not establish model\n   validity or parameter identifiability. Never clip negative concentrations to hide solver\n   or model problems.\n6. Review the COMBINE replay comparison, model warnings and units in `report.json`. The helper\n   replays the archive it generated and compares every selected value against the direct\n   trajectories. Deliver the archive, report, source model, experiment config and CSV curves.\n\n## Run the executable reference\n\n[assets\u002Ffirst-order.ant](assets\u002Ffirst-order.ant) defines the closed reaction A → B in a constant\n1-L compartment, initially A=1 and B=0 mol\u002FL, with k=0.2 per second.\n[assets\u002Fexperiment.json](assets\u002Fexperiment.json) runs baseline and k=0.4 per second from 0 to 10 s.\nFrom the skill directory, point the interpreter to the environment created above:\n\n```bash\nMPLBACKEND=Agg kinetic-env\u002Fbin\u002Fpython scripts\u002Fkinetic_experiment.py \\\n  --model assets\u002Ffirst-order.ant --format antimony --experiment assets\u002Fexperiment.json \\\n  --output kinetic-reference\n\n# The SBML branch was also exercised; replace these filenames with actual user inputs.\nMPLBACKEND=Agg kinetic-env\u002Fbin\u002Fpython scripts\u002Fkinetic_experiment.py \\\n  --model model.xml --format sbml --experiment experiment.json --output kinetic-analysis\n```\n\nOutput directories must be new. The reference was executed, including Antimony-to-SBML conversion,\nlibSBML checks, both direct integrations, SED-ML creation and COMBINE replay. Both conditions matched\nthe analytical `A(t)=exp(-k*t)`, `B(t)=1-A(t)` within 2e-8 absolute\u002Frelative tolerance; A+B was\nconserved within 1e-10, and archive replay matched direct output exactly on the tested stack.\nAdditional checks use a 5-L compartment and an initial amount of 10 mol (2 mol\u002FL), resolve every\nSED-ML species XPath against its actual SBML file, and verify the solver tolerance scaling.\nThat verifies this controlled example; arbitrary SBML packages, events, delays or stochastic models\nare not covered by those tests.\n\n## Artifacts\n\n| File | Contents |\n| --- | --- |\n| `baseline.csv`, other scenario CSVs | Time and selected concentrations, with bracketed species headers |\n| `model_\u003Cscenario>.xml` | Exact independent SBML condition used by both execution routes |\n| `experiment.sedml` | Uniform time course, CVODE\u002Ftolerances, models, tasks and output selections |\n| `experiment.omex` | Those SBML files plus the master SED-ML and archive manifest |\n| `report.json` | Versions, input\u002Farchive checksums, parameters, units, validation findings, initial state tolerance vectors, minimum concentrations and replay differences |\n\nThe libSEDML findings in the report are **parse diagnostics**. Successful execution and equality\nprovide additional evidence that this generated uniform-course experiment works in Tellurium;\nthey do not certify every SED-ML feature or every simulator's compatibility.\n\nIn RoadRunner 2.10.0, the JSON `absolute_tolerance` value is a **scalar adjustment factor** for\nstate\u002Famount tolerances, not a uniform concentration error bound. The archive records that meaning\nas `KISAO:0000571`; inspect `initial_state_absolute_tolerances` and the detailed explanation in\n[references\u002Fexperiments.md](references\u002Fexperiments.md). Declared SBML XPath namespaces and explicit\nstiff\u002Funiform-output settings prevent successful Tellurium replay from hiding missing archive context.\n\n## Primary references\n\n- [Tellurium model loading and export methods](https:\u002F\u002Ftellurium.readthedocs.io\u002Fen\u002Flatest\u002Ftellurium_methods.html)\n- [Antimony unit and reaction semantics](https:\u002F\u002Ftellurium.readthedocs.io\u002Fen\u002Flatest\u002Fantimony.html)\n- [Tellurium SED-ML implementation](https:\u002F\u002Ftellurium.readthedocs.io\u002Fen\u002Flatest\u002F_modules\u002Ftellurium\u002Fsedml\u002Ftesedml.html)\n- [SED-ML Level 1 Version 3 specification](https:\u002F\u002Fsed-ml.org\u002Fdocuments\u002Fsed-ml-L1V3.pdf)\n- [RoadRunner 2.10.0 CVODE implementation](https:\u002F\u002Fgithub.com\u002Fsys-bio\u002Froadrunner\u002Fblob\u002Fv2.10.0\u002Fsource\u002FCVODEIntegrator.cpp)\n- [KiSAO algorithm and parameter definitions](https:\u002F\u002Fgithub.com\u002FSED-ML\u002FKiSAO\u002Fblob\u002Fmaster\u002Fkisao.owl)\n","# Tellurium kinetic experiments\n\n## When to use\n\nUse this skill for deterministic reaction-network trajectories and independent parameter conditions\nfrom a local model. The helper performs SBML consistency checks, CVODE integration and an actual\nCOMBINE archive replay. It exports each condition's exact SBML and the SED-ML experiment rather\nthan handing off an unrecorded notebook state.\n\n## Runtime\n\n```bash\nuv venv --python 3.11 kinetic-env\nuv pip install --python kinetic-env\u002Fbin\u002Fpython tellurium==2.2.13.1 libroadrunner==2.10.0 \\\n  antimony==3.2.0 python-libsbml==5.21.2 python-libsedml==2.0.34 python-libcombine==0.2.20\n```\n\nThe full workflow ran with these packages on macOS ARM64. It constructs SED-ML with libSEDML and\narchives with Tellurium\u002FlibCombine; PhraSEDML is not required by this helper. Headless runs can set\n`MPLBACKEND=Agg`. No plotting window is opened by the helper.\n\nThe six pinned releases were rechecked against official PyPI metadata on 2026-10-01. RoadRunner's\ndocumentation site still displays an old version banner; the solver settings below were also\nchecked against released 2.10.0 source and the installed native runtime.\n\n## Workflow\n\n1. Inspect the supplied model's compartments, species, initial conditions, boundary species,\n   reactions, parameter definitions and rules\u002Fevents. Identify the scientific question and\n   distinguish a mechanistic kinetic model from a flux-balance reconstruction. Record the\n   source model, version and any literature parameters; do not treat an example model as\n   experimentally calibrated.\n2. Check units before interpreting a trajectory. SBML reaction rates have amount\u002Ftime units;\n   species may have concentration or amount semantics. In a fixed-volume first-order model,\n   `k*A*cell` converts concentration dependence into amount\u002Ftime. The helper checks SBML\n   consistency and retains every warning, including undefined units. Undefined units are\n   reported as empty\u002Findeterminable, not silently assumed to mean SI.\n3. Select concentration outputs and an experiment in the JSON format described in\n   [references\u002Fexperiments.md](references\u002Fexperiments.md). Time values use the model's own time\n   units. The tested helper outputs concentration for species with `hasOnlySubstanceUnits=false`;\n   it rejects amount-only selections to avoid changing their meaning during SED-ML replay.\n   Zero-dimensional compartments and rate-rule models are also rejected; the latter need a separate\n   tolerance workflow because RoadRunner 2.10.0 can order scalar tolerances differently from states.\n4. Run baseline and desired constant-global-parameter changes. Every scenario starts from a\n   fresh SBML model, so previous final concentrations cannot leak into the next condition.\n   Changes to species initial values, compartment volume, assignment rules or time-varying\n   inputs require explicit model changes and corresponding tests; they are not parameter\n   mutations hidden in this helper.\n5. Examine finite outputs, signs, relevant conservation relations and timescales. Check solver\n   sensitivity by repeating at stricter tolerances when the scientific interpretation depends\n   on small differences. A smooth curve or zero archive-replay error does not establish model\n   validity or parameter identifiability. Never clip negative concentrations to hide solver\n   or model problems.\n6. Review the COMBINE replay comparison, model warnings and units in `report.json`. The helper\n   replays the archive it generated and compares every selected value against the direct\n   trajectories. Deliver the archive, report, source model, experiment config and CSV curves.\n\n## Run the executable reference\n\n[assets\u002Ffirst-order.ant](assets\u002Ffirst-order.ant) defines the closed reaction A → B in a constant\n1-L compartment, initially A=1 and B=0 mol\u002FL, with k=0.2 per second.\n[assets\u002Fexperiment.json](assets\u002Fexperiment.json) runs baseline and k=0.4 per second from 0 to 10 s.\nFrom the skill directory, point the interpreter to the environment created above:\n\n```bash\nMPLBACKEND=Agg kinetic-env\u002Fbin\u002Fpython scripts\u002Fkinetic_experiment.py \\\n  --model assets\u002Ffirst-order.ant --format antimony --experiment assets\u002Fexperiment.json \\\n  --output kinetic-reference\n\n# The SBML branch was also exercised; replace these filenames with actual user inputs.\nMPLBACKEND=Agg kinetic-env\u002Fbin\u002Fpython scripts\u002Fkinetic_experiment.py \\\n  --model model.xml --format sbml --experiment experiment.json --output kinetic-analysis\n```\n\nOutput directories must be new. The reference was executed, including Antimony-to-SBML conversion,\nlibSBML checks, both direct integrations, SED-ML creation and COMBINE replay. Both conditions matched\nthe analytical `A(t)=exp(-k*t)`, `B(t)=1-A(t)` within 2e-8 absolute\u002Frelative tolerance; A+B was\nconserved within 1e-10, and archive replay matched direct output exactly on the tested stack.\nAdditional checks use a 5-L compartment and an initial amount of 10 mol (2 mol\u002FL), resolve every\nSED-ML species XPath against its actual SBML file, and verify the solver tolerance scaling.\nThat verifies this controlled example; arbitrary SBML packages, events, delays or stochastic models\nare not covered by those tests.\n\n## Artifacts\n\n| File | Contents |\n| --- | --- |\n| `baseline.csv`, other scenario CSVs | Time and selected concentrations, with bracketed species headers |\n| `model_\u003Cscenario>.xml` | Exact independent SBML condition used by both execution routes |\n| `experiment.sedml` | Uniform time course, CVODE\u002Ftolerances, models, tasks and output selections |\n| `experiment.omex` | Those SBML files plus the master SED-ML and archive manifest |\n| `report.json` | Versions, input\u002Farchive checksums, parameters, units, validation findings, initial state tolerance vectors, minimum concentrations and replay differences |\n\nThe libSEDML findings in the report are **parse diagnostics**. Successful execution and equality\nprovide additional evidence that this generated uniform-course experiment works in Tellurium;\nthey do not certify every SED-ML feature or every simulator's compatibility.\n\nIn RoadRunner 2.10.0, the JSON `absolute_tolerance` value is a **scalar adjustment factor** for\nstate\u002Famount tolerances, not a uniform concentration error bound. The archive records that meaning\nas `KISAO:0000571`; inspect `initial_state_absolute_tolerances` and the detailed explanation in\n[references\u002Fexperiments.md](references\u002Fexperiments.md). Declared SBML XPath namespaces and explicit\nstiff\u002Funiform-output settings prevent successful Tellurium replay from hiding missing archive context.\n\n## Primary references\n\n- [Tellurium model loading and export methods](https:\u002F\u002Ftellurium.readthedocs.io\u002Fen\u002Flatest\u002Ftellurium_methods.html)\n- [Antimony unit and reaction semantics](https:\u002F\u002Ftellurium.readthedocs.io\u002Fen\u002Flatest\u002Fantimony.html)\n- [Tellurium SED-ML implementation](https:\u002F\u002Ftellurium.readthedocs.io\u002Fen\u002Flatest\u002F_modules\u002Ftellurium\u002Fsedml\u002Ftesedml.html)\n- [SED-ML Level 1 Version 3 specification](https:\u002F\u002Fsed-ml.org\u002Fdocuments\u002Fsed-ml-L1V3.pdf)\n- [RoadRunner 2.10.0 CVODE implementation](https:\u002F\u002Fgithub.com\u002Fsys-bio\u002Froadrunner\u002Fblob\u002Fv2.10.0\u002Fsource\u002FCVODEIntegrator.cpp)\n- [KiSAO algorithm and parameter definitions](https:\u002F\u002Fgithub.com\u002FSED-ML\u002FKiSAO\u002Fblob\u002Fmaster\u002Fkisao.owl)\n",{"name":5,"license":14,"metadata":44,"description":7,"compatibility":49},{"version":45,"skill-author":46,"last-reviewed":47,"upstream-version":48},"1.1","K-Dense Inc.","2026-10-01","2.2.13.1","Requires Python 3.11 with Tellurium 2.2.13.1, libRoadRunner 2.10.0, Antimony 3.2.0, python-libsbml 5.21.2, python-libsedml 2.0.34 and python-libcombine 0.2.20. Network is needed for installation only. Native wheels were tested on macOS ARM64. No credentials or external services.","main","7bf2279c8a65758319526a721d5c4b75683aec9c","skills\u002Fkdense-tellurium",3,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections","https:\u002F\u002Fgithub.com\u002Fboisenoise","2026-11-11 14:20:43",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add boisenoise\u002Fskills-collections --skill tellurium",{"tool":63,"command":64},"bunx","bunx skills add boisenoise\u002Fskills-collections --skill tellurium",{"tool":66,"command":67},"pnpm","pnpm dlx skills add boisenoise\u002Fskills-collections --skill tellurium","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fkdense-tellurium\u002FSKILL.md",true,[71,77,81,87],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"assets\u002Fexperiment.json","ASSET","资产文件",195,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fkdense-tellurium\u002Fassets\u002Fexperiment.json",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"assets\u002Ffirst-order.ant",464,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fkdense-tellurium\u002Fassets\u002Ffirst-order.ant",{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fexperiments.md","REFERENCE","参考文档",7362,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fkdense-tellurium\u002Freferences\u002Fexperiments.md",{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"scripts\u002Fkinetic_experiment.py","SCRIPT","脚本",12598,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fkdense-tellurium\u002Fscripts\u002Fkinetic_experiment.py",[94,123,148,173,192,215],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":98,"licenseFileUrl":99,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":39},"01M6WNH92BHVFXHGPDZCPJ0AG0","web-access","Use Treg for public web or platform data only when existing search, browser and connected tools cannot cover the task, with explicit cost and data boundaries.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FSKILL.md",[],[103,104,105,106,107,108,109,110,111,112,113,18,114,115,116,117,118,119,120,121,122],"web","access","web access","official","development","browser","search","data","Tools","read","fetch","youtube","free 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budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FSKILL.md",[130],{"categoryNo":131,"categoryName":132,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[125,136,137,138,106,107,139,111,140,32,141,142,29,143,144,117,145,21,146,147,122],"MCP","cli","github","license","Budget","team","catalog","call","discover","external tools","scoped","blob",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":98,"licenseFileUrl":152,"sourceHtmlUrl":153,"categories":154,"keywords":160,"publishedAt":39},"01M6WN93MEAZ4E41DYAHJRPHQT","recruit","Turn a job description into an evidence-linked candidate shortlist through Treg, with job-related ranking and optional authorized business contact enrichment.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FSKILL.md",[155],{"categoryNo":156,"categoryName":157,"taxonomyType":158,"taxonomyTypeDesc":159},"01JBSEEDCAT000000000000038","人力资源","PROFESSION","适用职业",[150,138,106,139,161,162,163,164,165,166,117,167,168,169,21,19,170,171,172,147,125,122],"https","email","enrichment","business","evidence","phone","ranking","post","job description","person","candidate","reason",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":98,"licenseFileUrl":177,"sourceHtmlUrl":178,"categories":179,"keywords":180,"publishedAt":39},"01M6WN8R5RXSMHM9B4D9FAFX42","rea-growth","Analyze a company’s public growth and content through Treg, distinguish observations from hypotheses, and produce an evidence-linked playbook within an approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FSKILL.md",[],[181,182,183,138,106,139,184,185,161,186,140,187,188,189,165,117,190,191,147,125,122],"growth","rea","rea 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