[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M789NFKS7S1MES4RQYY3B81T":3,"skill-same:01M789NFKS7S1MES4RQYY3B81T":75},{"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":34,"commitSha":35,"skillDirectory":36,"forksCount":14,"repositoryHtmlUrl":37,"repositoryHomepageUrl":38,"ownerHtmlUrl":39,"ownerBlogUrl":40,"ownerDescription":41,"updatedAt":42,"install":43,"resources":56},"01M789NFKS7S1MES4RQYY3B81T","earth2studio-data-fetch",null,"Fetch weather\u002Fclimate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.","Solizardking\u002Fskillhub-main","Solizardking","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F108096810?v=4","01M786KDB10AYV042N0F9BD5B5","Clawd","01M786NYYXSZK1D05WM8JQPC48",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Fearth2studio-data-fetch\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: earth2studio-data-fetch\nversion: 0.16.0\nlicense: Apache-2.0\nmetadata:\n  author: NVIDIA Earth-2 Team\n  tags:\n    - earth2studio\n    - earth2\n    - python\n    - data-fetch\n    - weather-data\n    - xarray\ndescription: >\n  Fetch weather\u002Fclimate data via Earth2Studio data sources for specific variables\n  and times. Do NOT use for inference pipelines, model discovery, or installation.\n---\n\n# Earth2Studio Data Fetch Skill\n\n## Purpose\n\nGuide a user through downloading weather\u002Fclimate data via Earth2Studio data source\nAPIs. Identifies compatible sources by checking the lexicon, verifies variable\nsupport, and produces a working fetch script outputting an xarray DataArray.\n\n## Prerequisites\n\n- Earth2Studio installed (`uv pip install earth2studio` or equivalent)\n- Network access to remote data stores (GCS, S3, CDS API, etc.)\n- For CDS-based sources: valid CDS API key configured (`~\u002F.cdsapirc`)\n- Python 3.10+\n\n## Instructions\n\nYou are helping a user download specific weather\u002Fclimate data using\nEarth2Studio's data source APIs. Your job is to identify which data source(s)\ncan provide the requested variables, verify compatibility via the lexicon\nsystem, and produce a working fetch script.\n\n### Core principle: live docs and lexicon are the source of truth\n\nData source APIs, available variables, and the lexicon evolve between releases.\nBefore recommending a data source or writing a fetch script:\n\n1. **Fetch the relevant data source doc page** to confirm the API signature\n   and constructor arguments.\n2. **Check the lexicon** to verify the requested variable is supported by\n   that data source.\n\nLive doc references (fetch only what the user's request requires):\n\n- **Analysis data sources:**\n  \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_analysis.html>\n- **Forecast data sources:**\n  \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_forecast.html>\n- **DataFrame data sources:**\n  \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_dataframe.html>\n- **Lexicon base:**\n  \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Fblob\u002Fmain\u002Fearth2studio\u002Flexicon\u002Fbase.py>\n- **Lexicon per-source:**\n  \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Ftree\u002Fmain\u002Fearth2studio\u002Flexicon>\n\n### Interaction protocol\n\n#### Step 1. Understand the user's request\n\nExtract from what the user has said (ask follow-ups if needed, cap at 3\nquestions):\n\n- **Variables** — what do they want? Use Earth2Studio variable names\n  (e.g. `t2m`, `u500`, `z850`, `tp`, `msl`). If the user uses plain language\n  (\"500 hPa geopotential height\"), map it to the E2Studio name by checking\n  the live `base.py` E2STUDIO_VOCAB.\n- **Time** — what date\u002Ftime range? A single timestamp, a range, or multiple\n  discrete times?\n- **Data type** — analysis\u002Freanalysis (historical state) or forecast (lead-time based)?\n- **Lead time** (forecast only) — how far ahead? Which initialization time?\n- **Region** — global or regional (e.g. North America for HRRR)?\n- **Output format** — xarray DataArray (default), save to file (NetCDF\u002FZarr)?\n\n#### Step 2. Identify candidate data sources\n\nBased on the request type, narrow candidates:\n\n**Analysis\u002Freanalysis** (historical state at a specific time):\n\n- Use analysis data source page to identify options\n- Common choices: GFS (operational, recent), HRRR (NA, hourly),\n  IFS\u002FIFS_ENS (ECMWF), ARCO\u002FCDS\u002FWB2ERA5\u002FNCAR_ERA5 (ERA5 reanalysis),\n  GOES\u002FMRMS\u002FJPSS (observational)\n\n**Forecast** (predictions from an initialization time with lead times):\n\n- Use forecast data source page to identify options\n- Common choices: GFS_FX, GEFS_FX, HRRR_FX, IFS_FX, IFS_ENS_FX,\n  AIFS_FX, CFS_FX\n\nKey differentiators to surface:\n\n- **Temporal coverage** — operational sources (GFS, HRRR) have limited\n  history; reanalysis (ERA5 via ARCO\u002FCDS\u002FWB2) goes back decades\n- **Spatial resolution** — HRRR is 3km NA-only; GFS is 0.25° global;\n  WB2ERA5_32x64 is 5.625° global\n- **Update frequency** — some are real-time, some have multi-day lag\n\n#### Step 3. Verify variable support via lexicon\n\nThis is critical. Each data source has a lexicon file that defines which\nE2Studio variables it can provide.\n\nTo verify:\n\n1. Fetch the source's lexicon file from\n   `https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Fblob\u002Fmain\u002Fearth2studio\u002Flexicon\u002F\u003Csource>.py`\n   (e.g. `gfs.py`, `hrrr.py`, `cds.py`, `arco.py`, `wb2.py`)\n2. Check that the user's requested variable(s) appear as keys in the\n   source's `VOCAB` dict\n3. If a variable is NOT in a source's lexicon, that source cannot provide\n   it — try another\n\nThe lexicon VOCAB maps Earth2Studio variable names → source-specific\nidentifiers. If a variable key exists in the VOCAB, the source supports it.\n\nPresent the results clearly: *\"GFS supports `t2m`, `u500`, `z850`. HRRR also\nsupports these but is limited to North America. ARCO (ERA5) supports all\nthree and has data back to 1959.\"*\n\n#### Step 4. Confirm data source selection with user\n\nPresent the viable options with tradeoffs:\n\n| Source | Variables | Coverage | Resolution | Time Range |\n|--------|-----------|----------|------------|------------|\n| ... | ... | ... | ... | ... |\n\nLet the user pick. If there's one obvious choice, recommend it and ask for\nconfirmation.\n\n#### Step 5. Generate fetch script\n\nWrite a Python script that uses the selected data source to fetch the\nrequested data. The script structure depends on whether it's an analysis or\nforecast source.\n\n**Analysis source pattern:**\n\n```python\nimport datetime\nfrom earth2studio.data import \u003CSourceClass>\n\n# Initialize data source\nds = \u003CSourceClass>()\n\n# Fetch data\n# Analysis sources use: ds(time, variable) -> xr.DataArray\ntime = [datetime.datetime(YYYY, M, D, H)]  # or array of times\nvariable = [\"var1\", \"var2\"]  # E2Studio variable names\n\ndata = ds(time, variable)\n```\n\n**Forecast source pattern:**\n\n```python\nimport datetime\nfrom earth2studio.data import \u003CSourceClass>\n\n# Initialize data source\nds = \u003CSourceClass>()\n\n# Forecast sources use: ds(time, lead_time, variable) -> xr.DataArray\ntime = [datetime.datetime(YYYY, M, D, H)]  # initialization time\nlead_time = [datetime.timedelta(hours=H)]   # or array of lead times\nvariable = [\"var1\", \"var2\"]\n\ndata = ds(time, lead_time, variable)\n```\n\nAlways fetch the specific data source's API doc page to confirm the exact\nconstructor arguments and call signature before writing the script — they can\nvary (some need auth tokens, cache paths, specific parameters).\n\nInclude in the script:\n\n- Appropriate imports\n- Clear comments explaining each step\n- How to inspect the result (`print(data)`, `data.shape`, `data.coords`)\n- Optional: saving to file if the user requested it\n\n#### Step 6. Offer next steps\n\nAfter delivering the script, mention:\n\n- How to change variables\u002Ftimes without rewriting the whole thing\n- If they might want to feed this into a model, point them to the\n  discover skill\n- Cache behavior (data is cached locally after first fetch via\n  `EARTH2STUDIO_CACHE`)\n\n### Ownership and out-of-scope\n\n**Owns:** identifying data sources for a user's variable\u002Ftime request,\nverifying variable support via lexicon, generating data fetch scripts,\nexplaining analysis vs. forecast source differences.\n\n**Does not own:** installation (earth2studio-install), model selection\n(earth2studio-discover), inference pipelines, custom data source creation\n(point to extend examples), data source authentication setup beyond what\nthe docs describe.\n\n## Examples\n\nTypical invocation:\n\n> \"I need 500 hPa geopotential height and 2m temperature from ERA5\n> for January 1, 2020 at 00Z.\"\n\nThe skill would:\n\n1. Map plain language → `z500`, `t2m`\n2. Check ARCO\u002FCDS\u002FWB2ERA5 lexicons for support\n3. Recommend ARCO (free, no API key) or CDS (official, needs key)\n4. Generate a fetch script using the selected source\n\n## Limitations\n\n- **Network required** — all data sources fetch from remote stores\n  (GCS, S3, CDS API)\n- **No local file loading** — for local NetCDF\u002FZarr, use\n  `DataArrayFile`\u002F`DataSetFile` directly\n- **One source type per script** — cannot mix analysis and forecast\n  sources in a single call\n- **Variable availability varies** — not all sources provide all\n  variables; always verify via lexicon\n- **Rate limits** — CDS API has queue-based throttling; GCS\u002FS3 sources\n  are generally faster\n\n## Troubleshooting\n\n| Error | Cause | Solution |\n|-------|-------|----------|\n| `KeyError: '\u003Cvar>'` | Not in lexicon | Check lexicon; try another source |\n| `FileNotFoundError` \u002F 404 | Time not available | Verify temporal coverage |\n| `CDS API timeout` | Queue congestion | Retry or use ARCO for ERA5 |\n| `ModuleNotFoundError` | Not installed | `uv pip install earth2studio` |\n| Empty DataArray | Time\u002Fvar mismatch | Check datetime and variable name |\n","# Earth2Studio Data Fetch Skill\n\n## Purpose\n\nGuide a user through downloading weather\u002Fclimate data via Earth2Studio data source\nAPIs. Identifies compatible sources by checking the lexicon, verifies variable\nsupport, and produces a working fetch script outputting an xarray DataArray.\n\n## Prerequisites\n\n- Earth2Studio installed (`uv pip install earth2studio` or equivalent)\n- Network access to remote data stores (GCS, S3, CDS API, etc.)\n- For CDS-based sources: valid CDS API key configured (`~\u002F.cdsapirc`)\n- Python 3.10+\n\n## Instructions\n\nYou are helping a user download specific weather\u002Fclimate data using\nEarth2Studio's data source APIs. Your job is to identify which data source(s)\ncan provide the requested variables, verify compatibility via the lexicon\nsystem, and produce a working fetch script.\n\n### Core principle: live docs and lexicon are the source of truth\n\nData source APIs, available variables, and the lexicon evolve between releases.\nBefore recommending a data source or writing a fetch script:\n\n1. **Fetch the relevant data source doc page** to confirm the API signature\n   and constructor arguments.\n2. **Check the lexicon** to verify the requested variable is supported by\n   that data source.\n\nLive doc references (fetch only what the user's request requires):\n\n- **Analysis data sources:**\n  \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_analysis.html>\n- **Forecast data sources:**\n  \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_forecast.html>\n- **DataFrame data sources:**\n  \u003Chttps:\u002F\u002Fnvidia.github.io\u002Fearth2studio\u002Fmodules\u002Fdatasources_dataframe.html>\n- **Lexicon base:**\n  \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Fblob\u002Fmain\u002Fearth2studio\u002Flexicon\u002Fbase.py>\n- **Lexicon per-source:**\n  \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Ftree\u002Fmain\u002Fearth2studio\u002Flexicon>\n\n### Interaction protocol\n\n#### Step 1. Understand the user's request\n\nExtract from what the user has said (ask follow-ups if needed, cap at 3\nquestions):\n\n- **Variables** — what do they want? Use Earth2Studio variable names\n  (e.g. `t2m`, `u500`, `z850`, `tp`, `msl`). If the user uses plain language\n  (\"500 hPa geopotential height\"), map it to the E2Studio name by checking\n  the live `base.py` E2STUDIO_VOCAB.\n- **Time** — what date\u002Ftime range? A single timestamp, a range, or multiple\n  discrete times?\n- **Data type** — analysis\u002Freanalysis (historical state) or forecast (lead-time based)?\n- **Lead time** (forecast only) — how far ahead? Which initialization time?\n- **Region** — global or regional (e.g. North America for HRRR)?\n- **Output format** — xarray DataArray (default), save to file (NetCDF\u002FZarr)?\n\n#### Step 2. Identify candidate data sources\n\nBased on the request type, narrow candidates:\n\n**Analysis\u002Freanalysis** (historical state at a specific time):\n\n- Use analysis data source page to identify options\n- Common choices: GFS (operational, recent), HRRR (NA, hourly),\n  IFS\u002FIFS_ENS (ECMWF), ARCO\u002FCDS\u002FWB2ERA5\u002FNCAR_ERA5 (ERA5 reanalysis),\n  GOES\u002FMRMS\u002FJPSS (observational)\n\n**Forecast** (predictions from an initialization time with lead times):\n\n- Use forecast data source page to identify options\n- Common choices: GFS_FX, GEFS_FX, HRRR_FX, IFS_FX, IFS_ENS_FX,\n  AIFS_FX, CFS_FX\n\nKey differentiators to surface:\n\n- **Temporal coverage** — operational sources (GFS, HRRR) have limited\n  history; reanalysis (ERA5 via ARCO\u002FCDS\u002FWB2) goes back decades\n- **Spatial resolution** — HRRR is 3km NA-only; GFS is 0.25° global;\n  WB2ERA5_32x64 is 5.625° global\n- **Update frequency** — some are real-time, some have multi-day lag\n\n#### Step 3. Verify variable support via lexicon\n\nThis is critical. Each data source has a lexicon file that defines which\nE2Studio variables it can provide.\n\nTo verify:\n\n1. Fetch the source's lexicon file from\n   `https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio\u002Fblob\u002Fmain\u002Fearth2studio\u002Flexicon\u002F\u003Csource>.py`\n   (e.g. `gfs.py`, `hrrr.py`, `cds.py`, `arco.py`, `wb2.py`)\n2. Check that the user's requested variable(s) appear as keys in the\n   source's `VOCAB` dict\n3. If a variable is NOT in a source's lexicon, that source cannot provide\n   it — try another\n\nThe lexicon VOCAB maps Earth2Studio variable names → source-specific\nidentifiers. If a variable key exists in the VOCAB, the source supports it.\n\nPresent the results clearly: *\"GFS supports `t2m`, `u500`, `z850`. HRRR also\nsupports these but is limited to North America. ARCO (ERA5) supports all\nthree and has data back to 1959.\"*\n\n#### Step 4. Confirm data source selection with user\n\nPresent the viable options with tradeoffs:\n\n| Source | Variables | Coverage | Resolution | Time Range |\n|--------|-----------|----------|------------|------------|\n| ... | ... | ... | ... | ... |\n\nLet the user pick. If there's one obvious choice, recommend it and ask for\nconfirmation.\n\n#### Step 5. Generate fetch script\n\nWrite a Python script that uses the selected data source to fetch the\nrequested data. The script structure depends on whether it's an analysis or\nforecast source.\n\n**Analysis source pattern:**\n\n```python\nimport datetime\nfrom earth2studio.data import \u003CSourceClass>\n\n# Initialize data source\nds = \u003CSourceClass>()\n\n# Fetch data\n# Analysis sources use: ds(time, variable) -> xr.DataArray\ntime = [datetime.datetime(YYYY, M, D, H)]  # or array of times\nvariable = [\"var1\", \"var2\"]  # E2Studio variable names\n\ndata = ds(time, variable)\n```\n\n**Forecast source pattern:**\n\n```python\nimport datetime\nfrom earth2studio.data import \u003CSourceClass>\n\n# Initialize data source\nds = \u003CSourceClass>()\n\n# Forecast sources use: ds(time, lead_time, variable) -> xr.DataArray\ntime = [datetime.datetime(YYYY, M, D, H)]  # initialization time\nlead_time = [datetime.timedelta(hours=H)]   # or array of lead times\nvariable = [\"var1\", \"var2\"]\n\ndata = ds(time, lead_time, variable)\n```\n\nAlways fetch the specific data source's API doc page to confirm the exact\nconstructor arguments and call signature before writing the script — they can\nvary (some need auth tokens, cache paths, specific parameters).\n\nInclude in the script:\n\n- Appropriate imports\n- Clear comments explaining each step\n- How to inspect the result (`print(data)`, `data.shape`, `data.coords`)\n- Optional: saving to file if the user requested it\n\n#### Step 6. Offer next steps\n\nAfter delivering the script, mention:\n\n- How to change variables\u002Ftimes without rewriting the whole thing\n- If they might want to feed this into a model, point them to the\n  discover skill\n- Cache behavior (data is cached locally after first fetch via\n  `EARTH2STUDIO_CACHE`)\n\n### Ownership and out-of-scope\n\n**Owns:** identifying data sources for a user's variable\u002Ftime request,\nverifying variable support via lexicon, generating data fetch scripts,\nexplaining analysis vs. forecast source differences.\n\n**Does not own:** installation (earth2studio-install), model selection\n(earth2studio-discover), inference pipelines, custom data source creation\n(point to extend examples), data source authentication setup beyond what\nthe docs describe.\n\n## Examples\n\nTypical invocation:\n\n> \"I need 500 hPa geopotential height and 2m temperature from ERA5\n> for January 1, 2020 at 00Z.\"\n\nThe skill would:\n\n1. Map plain language → `z500`, `t2m`\n2. Check ARCO\u002FCDS\u002FWB2ERA5 lexicons for support\n3. Recommend ARCO (free, no API key) or CDS (official, needs key)\n4. Generate a fetch script using the selected source\n\n## Limitations\n\n- **Network required** — all data sources fetch from remote stores\n  (GCS, S3, CDS API)\n- **No local file loading** — for local NetCDF\u002FZarr, use\n  `DataArrayFile`\u002F`DataSetFile` directly\n- **One source type per script** — cannot mix analysis and forecast\n  sources in a single call\n- **Variable availability varies** — not all sources provide all\n  variables; always verify via lexicon\n- **Rate limits** — CDS API has queue-based throttling; GCS\u002FS3 sources\n  are generally faster\n\n## Troubleshooting\n\n| Error | Cause | Solution |\n|-------|-------|----------|\n| `KeyError: '\u003Cvar>'` | Not in lexicon | Check lexicon; try another source |\n| `FileNotFoundError` \u002F 404 | Time not available | Verify temporal coverage |\n| `CDS API timeout` | Queue congestion | Retry or use ARCO for ERA5 |\n| `ModuleNotFoundError` | Not installed | `uv pip install earth2studio` |\n| Empty DataArray | Time\u002Fvar mismatch | Check datetime and variable name |\n",{"name":5,"license":15,"version":24,"metadata":25,"description":7},"0.16.0",{"tags":26,"author":33},[27,28,29,30,31,32],"earth2studio","earth2","python","data-fetch","weather-data","xarray","NVIDIA Earth-2 Team","main","be3de0244dc1a57e323d8160787ebb40fb64930e","public\u002Fapi\u002Fskills\u002Fnvidia\u002Fearth2studio-data-fetch","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main","https:\u002F\u002Fskills.x402.wtf","https:\u002F\u002Fgithub.com\u002FSolizardking","https:\u002F\u002Fmusebook.trade","Got a mile high and never came down","2026-10-08 23:20:13",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add Solizardking\u002Fskillhub-main --skill earth2studio-data-fetch",{"tool":49,"command":50},"bunx","bunx skills add Solizardking\u002Fskillhub-main --skill earth2studio-data-fetch",{"tool":52,"command":53},"pnpm","pnpm dlx skills add Solizardking\u002Fskillhub-main --skill earth2studio-data-fetch","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Fearth2studio-data-fetch\u002FSKILL.md",true,[57,63,67,71],{"path":58,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":61,"sourceHtmlUrl":62},"BENCHMARK.md","REFERENCE","参考文档",3702,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Fearth2studio-data-fetch\u002FBENCHMARK.md",{"path":64,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":65,"sourceHtmlUrl":66},"metadata.json",379,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Fearth2studio-data-fetch\u002Fmetadata.json",{"path":68,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":69,"sourceHtmlUrl":70},"skill-card.md",3953,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Fearth2studio-data-fetch\u002Fskill-card.md",{"path":72,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":73,"sourceHtmlUrl":74},"verification.json",1370,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Fearth2studio-data-fetch\u002Fverification.json",[76,84,91,98,105,113],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":80,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M78E0TGCAMNPS207BA3G07S7","zkrouter","Self-hosted OpenAI-compatible LLM router — smart tier-based routing, ZK-stamped routing decisions, mode overrides, free OpenRouter at install via the Birth bot. Save 60-80% on inference costs by routing to the cheapest capable model across 12+ OpenRouter models.","MIT","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fzkrouter\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":80,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M78E0R1Z8BDT6EDTHY17MTNB","zk","For custom ZK Solana programs and privacy-preserving applications to prevent double spending. Guide to integrate rent-free nullifier PDAs for double-spend prevention.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fzk\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":80,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":19},"01M78E0P3ZTZ1YWF8MD5MM23QE","zk-omni-messaging","Zero-knowledge omnichain messaging between Robinhood Chain and Solana using CheshireZkOmniMessenger (msgType 4) with nullifier anti-replay and the zk-omni-relayer service.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fzk-omni-messaging\u002FSKILL.md",[],[],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":80,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":19},"01M78E0JXZ2PNB1896VXAGDBWD","zerion-wallet-data","Use when the user asks to check a wallet's portfolio, token balances, DeFi positions, PnL, transaction history, or NFTs. Provides interpreted on-chain data across 40+ EVM chains and Solana via x402 pay-per-request or API key — no raw RPC decoding needed.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fzerion-wallet-data\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":80,"licenseFileUrl":109,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":19},"01M78E0G14FH9GRRTQYMKD5E59","youtube-clipper","YouTube 视频智能剪辑工具。下载视频和字幕，AI 分析生成精细章节（几分钟级别）， 用户选择片段后自动剪辑、翻译字幕为中英双语、烧录字幕到视频，并生成总结文案。 使用场景：当用户需要剪辑 YouTube 视频、生成短视频片段、制作双语字幕版本时。 关键词：视频剪辑、YouTube、字幕翻译、双语字幕、视频下载、clip video","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-clipper\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-clipper\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":80,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":19},"01M78E0C2ZYA99CFFDNB7ZMK5S","yield-optimization","Multi-chain yield optimization via yield.xyz (StakeKit). Discover 2,988+ yield opportunities across 75+ blockchains, build deposit\u002Fwithdrawal transactions with shell scripts, and sign them with a MoonPay wallet. Use when the user wants to earn yield, find the best lending or staking rate, enter\u002Fexit a position, or check portfolio balances.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fyield-optimization\u002FSKILL.md",[],[]]