[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7D7MABV176S7YVTDEVG6QF3":3,"skill-same:01M7D7MABV176S7YVTDEVG6QF3":76},{"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":19,"publishedAt":20,"linkOnly":21,"markdownContent":22,"markdownBody":23,"frontMatter":24,"refName":25,"commitSha":26,"skillDirectory":5,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":29,"ownerHtmlUrl":30,"ownerBlogUrl":31,"ownerDescription":32,"updatedAt":33,"install":34,"resources":47},"01M7D7MABV176S7YVTDEVG6QF3","hf-cloud-sagemaker-iam-preflight",null,"Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.","tangyixiao\u002Ftangyixiao-skills","tangyixiao","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F115929632?v=4","01M7D582YK6A4M63CAK4CSY4EW","Tang Yixiao","01M7D582Z64DMRSYR872SR34SA",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fhf-cloud-sagemaker-iam-preflight\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fhf-cloud-sagemaker-iam-preflight\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: hf-cloud-sagemaker-iam-preflight\ndescription: 'Ensure a usable SageMaker execution role exists before deploying or\n  training. Use this skill whenever about to create a SageMaker endpoint, model, training\n  job, or any resource that requires an execution role. Use it especially when the\n  user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`,\n  or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole`\n  — always check for existing roles first. This skill prevents the most common SageMaker\n  deployment failure: trying to create IAM resources from an SSO principal that has\n  no IAM write permissions.'\n---\n\n# SageMaker IAM Preflight\n\nEvery SageMaker resource needs an **execution role** — the IAM role SageMaker assumes to read model artifacts from S3, pull serving containers from ECR, and write logs. Most deployments fail here because the script tried to create a new role without checking if a usable one already existed, then blew up because the caller is an SSO principal.\n\nThis skill encodes the right order: discover, validate, only create if necessary.\n\n## Running the helpers (cross-platform)\n\nThe helpers are Python so they run identically on Windows, macOS, and Linux:\n\n```bash\npython3 scripts\u002Fcheck_role.py        # macOS \u002F Linux\npython  scripts\u002Fcheck_role.py        # Windows (PowerShell \u002F cmd)\n```\n\n**Run them from the shell where the AWS CLI already works** — i.e. wherever `aws sts get-caller-identity` succeeds. The script shells out to that same `aws` binary and inherits the shell's profile, region, SSO session, proxy, and credential chain.\n\n> **Windows \u002F WSL \u002F Git Bash caveat.** Do **not** invoke these through a Bash shim (WSL, Git Bash, MSYS) on Windows. Those Bash environments frequently do **not** share the Windows AWS config, credentials, SSO sessions, environment variables, or proxy settings — so `aws sts get-caller-identity` fails inside Bash even when it works natively in PowerShell. (This is exactly why the old `.sh` helpers failed on Windows and were replaced with Python.) If you're in PowerShell, run `python ...\\check_role.py` directly in PowerShell. If the helper still can't see your identity, run the same discovery natively (see \"Native AWS CLI equivalent\" below) in the shell where `aws sts get-caller-identity` returns your ARN.\n\n## Order of operations\n\n### Step 1 — Did the user provide a role?\n\nValidate that one specifically:\n\n```bash\npython3 scripts\u002Fcheck_role.py \"\u003Crole-name-or-arn>\"\n```\n\nOn success it prints the ARN to stdout (exit 0). On failure it logs why on stderr. Don't try to silently fix a broken role — surface the problem.\n\n### Step 2 — Discover existing roles\n\n```bash\npython3 scripts\u002Fcheck_role.py\n```\n\nLists roles matching common SageMaker patterns (`AmazonSageMaker-ExecutionRole-*`, `SageMakerExecutionRole*`, etc.), **ranks by last-used date** (most recent first), validates trust policy in that order, returns the first usable ARN. Most accounts that have used SageMaker before already have one.\n\nWhy rank by last-used: in accounts with multiple roles (auto-generated 2021 role + manual project role + etc.), the alphabetically-first one is rarely the actively-maintained one. The most-recently-used role is more likely to have current policies — including cross-account ECR pull. The script prints the ranking so you can see which got picked.\n\nIAM frequently reports **no** `RoleLastUsed` at all (tracking only covers recent activity). When every candidate ties at \"never used\", the script falls back to **newest creation date** — a newer role is more likely to have current policies than a 2021 leftover.\n\n### Step 3 — Create, only if discovery found nothing\n\n**If the user can create** (has IAM permissions):\n\n```bash\npython3 scripts\u002Fcreate_role.py \"\u003Crole-name>\" \"\u003Cmodel-bucket>\"\n```\n\nSecond arg scopes S3 access to a specific bucket. Omit if unknown; script warns and the user can update the policy later.\n\n**If the user cannot create** (SSO principal — `hf-cloud-aws-context-discovery` will have flagged this):\n\nStop and surface this clearly. Don't retry alternative IAM operations hoping one works:\n\n> I can't find an existing SageMaker execution role, and you're authenticated via SSO so you can't create one directly. Please either:\n>   - Ask your AWS admin for a SageMaker execution role ARN, or\n>   - Have them grant your SSO permission set `iam:CreateRole`, `iam:PutRolePolicy`\n\nSpecific instructions get unblocked fast; vague \"permission denied\" messages don't.\n\n## What \"validated\" means\n\nA role is usable when (1) it exists, (2) its trust policy allows `sagemaker.amazonaws.com` to `sts:AssumeRole`, and (3) its permissions grant only the actions and resources this deployment needs. See `references\u002Ftrust-policy.json` for the canonical trust policy.\n\n`check_role.py` verifies existence and trust because policy evaluation depends on the deployment's exact S3, ECR, logging, and optional output resources. Before deployment, inspect the selected role's policies and compare them with `references\u002Fminimum-permissions.json`; add only missing actions and scope them to the required resources. Do not attach `AmazonSageMakerFullAccess` or defer permission review until an `AccessDenied` failure.\n\n## Minimum permissions\n\n`references\u002Fminimum-permissions.json` is the standalone inline policy for endpoint execution:\n- `s3:GetObject` + `s3:ListBucket` on the model artifact bucket\n- ECR pull permissions\n- CloudWatch logs and metrics\n\n`create_role.py` installs this inline policy without attaching a managed FullAccess policy. Replace `REPLACE_WITH_MODEL_BUCKET` in the template with the actual bucket name — `create_role.py` does this automatically when given a bucket as its second argument. Add narrowly scoped permissions separately for optional features such as async output or data capture.\n\n## Native AWS CLI equivalent (fallback)\n\nIf the Python helper can't run or can't see your identity (rare — usually a broken PATH or running under a Bash shim that lacks AWS context), do the same preflight by hand in the shell where `aws sts get-caller-identity` works. The logic is just AWS CLI calls; the helper exists only to bundle and rank them.\n\nPowerShell:\n\n```powershell\n# 1. List candidate SageMaker roles\naws iam list-roles --query \"Roles[?contains(RoleName,'SageMaker') || contains(RoleName,'sagemaker')]\" --output json\n\n# 2. For each candidate, confirm the trust policy allows sagemaker.amazonaws.com\naws iam get-role --role-name \u003Crole-name> --query \"Role.AssumeRolePolicyDocument\" --output json\n\n# 3. Prefer the most-recently-used role with SageMaker-execution naming\n#    (LastUsedDate is often None for every role — then prefer newest CreateDate)\naws iam get-role --role-name \u003Crole-name> --query \"Role.[RoleLastUsed.LastUsedDate, CreateDate]\" --output text\n```\n\nPick the most-recently-used role whose trust policy contains `sagemaker.amazonaws.com`. Use the resulting ARN exactly as if `check_role.py` had returned it. Bash\u002FmacOS\u002FLinux use the same commands.\n","# SageMaker IAM Preflight\n\nEvery SageMaker resource needs an **execution role** — the IAM role SageMaker assumes to read model artifacts from S3, pull serving containers from ECR, and write logs. Most deployments fail here because the script tried to create a new role without checking if a usable one already existed, then blew up because the caller is an SSO principal.\n\nThis skill encodes the right order: discover, validate, only create if necessary.\n\n## Running the helpers (cross-platform)\n\nThe helpers are Python so they run identically on Windows, macOS, and Linux:\n\n```bash\npython3 scripts\u002Fcheck_role.py        # macOS \u002F Linux\npython  scripts\u002Fcheck_role.py        # Windows (PowerShell \u002F cmd)\n```\n\n**Run them from the shell where the AWS CLI already works** — i.e. wherever `aws sts get-caller-identity` succeeds. The script shells out to that same `aws` binary and inherits the shell's profile, region, SSO session, proxy, and credential chain.\n\n> **Windows \u002F WSL \u002F Git Bash caveat.** Do **not** invoke these through a Bash shim (WSL, Git Bash, MSYS) on Windows. Those Bash environments frequently do **not** share the Windows AWS config, credentials, SSO sessions, environment variables, or proxy settings — so `aws sts get-caller-identity` fails inside Bash even when it works natively in PowerShell. (This is exactly why the old `.sh` helpers failed on Windows and were replaced with Python.) If you're in PowerShell, run `python ...\\check_role.py` directly in PowerShell. If the helper still can't see your identity, run the same discovery natively (see \"Native AWS CLI equivalent\" below) in the shell where `aws sts get-caller-identity` returns your ARN.\n\n## Order of operations\n\n### Step 1 — Did the user provide a role?\n\nValidate that one specifically:\n\n```bash\npython3 scripts\u002Fcheck_role.py \"\u003Crole-name-or-arn>\"\n```\n\nOn success it prints the ARN to stdout (exit 0). On failure it logs why on stderr. Don't try to silently fix a broken role — surface the problem.\n\n### Step 2 — Discover existing roles\n\n```bash\npython3 scripts\u002Fcheck_role.py\n```\n\nLists roles matching common SageMaker patterns (`AmazonSageMaker-ExecutionRole-*`, `SageMakerExecutionRole*`, etc.), **ranks by last-used date** (most recent first), validates trust policy in that order, returns the first usable ARN. Most accounts that have used SageMaker before already have one.\n\nWhy rank by last-used: in accounts with multiple roles (auto-generated 2021 role + manual project role + etc.), the alphabetically-first one is rarely the actively-maintained one. The most-recently-used role is more likely to have current policies — including cross-account ECR pull. The script prints the ranking so you can see which got picked.\n\nIAM frequently reports **no** `RoleLastUsed` at all (tracking only covers recent activity). When every candidate ties at \"never used\", the script falls back to **newest creation date** — a newer role is more likely to have current policies than a 2021 leftover.\n\n### Step 3 — Create, only if discovery found nothing\n\n**If the user can create** (has IAM permissions):\n\n```bash\npython3 scripts\u002Fcreate_role.py \"\u003Crole-name>\" \"\u003Cmodel-bucket>\"\n```\n\nSecond arg scopes S3 access to a specific bucket. Omit if unknown; script warns and the user can update the policy later.\n\n**If the user cannot create** (SSO principal — `hf-cloud-aws-context-discovery` will have flagged this):\n\nStop and surface this clearly. Don't retry alternative IAM operations hoping one works:\n\n> I can't find an existing SageMaker execution role, and you're authenticated via SSO so you can't create one directly. Please either:\n>   - Ask your AWS admin for a SageMaker execution role ARN, or\n>   - Have them grant your SSO permission set `iam:CreateRole`, `iam:PutRolePolicy`\n\nSpecific instructions get unblocked fast; vague \"permission denied\" messages don't.\n\n## What \"validated\" means\n\nA role is usable when (1) it exists, (2) its trust policy allows `sagemaker.amazonaws.com` to `sts:AssumeRole`, and (3) its permissions grant only the actions and resources this deployment needs. See `references\u002Ftrust-policy.json` for the canonical trust policy.\n\n`check_role.py` verifies existence and trust because policy evaluation depends on the deployment's exact S3, ECR, logging, and optional output resources. Before deployment, inspect the selected role's policies and compare them with `references\u002Fminimum-permissions.json`; add only missing actions and scope them to the required resources. Do not attach `AmazonSageMakerFullAccess` or defer permission review until an `AccessDenied` failure.\n\n## Minimum permissions\n\n`references\u002Fminimum-permissions.json` is the standalone inline policy for endpoint execution:\n- `s3:GetObject` + `s3:ListBucket` on the model artifact bucket\n- ECR pull permissions\n- CloudWatch logs and metrics\n\n`create_role.py` installs this inline policy without attaching a managed FullAccess policy. Replace `REPLACE_WITH_MODEL_BUCKET` in the template with the actual bucket name — `create_role.py` does this automatically when given a bucket as its second argument. Add narrowly scoped permissions separately for optional features such as async output or data capture.\n\n## Native AWS CLI equivalent (fallback)\n\nIf the Python helper can't run or can't see your identity (rare — usually a broken PATH or running under a Bash shim that lacks AWS context), do the same preflight by hand in the shell where `aws sts get-caller-identity` works. The logic is just AWS CLI calls; the helper exists only to bundle and rank them.\n\nPowerShell:\n\n```powershell\n# 1. List candidate SageMaker roles\naws iam list-roles --query \"Roles[?contains(RoleName,'SageMaker') || contains(RoleName,'sagemaker')]\" --output json\n\n# 2. For each candidate, confirm the trust policy allows sagemaker.amazonaws.com\naws iam get-role --role-name \u003Crole-name> --query \"Role.AssumeRolePolicyDocument\" --output json\n\n# 3. Prefer the most-recently-used role with SageMaker-execution naming\n#    (LastUsedDate is often None for every role — then prefer newest CreateDate)\naws iam get-role --role-name \u003Crole-name> --query \"Role.[RoleLastUsed.LastUsedDate, CreateDate]\" --output text\n```\n\nPick the most-recently-used role whose trust policy contains `sagemaker.amazonaws.com`. Use the resulting ARN exactly as if `check_role.py` had returned it. Bash\u002FmacOS\u002FLinux use the same commands.\n",{"name":5,"description":7},"main","2a870f189175a48c39f5f7e26a6d93b0be9cd13b",0,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills","https:\u002F\u002Ftangyixiao.github.io\u002Ftangyixiao-skills\u002F","https:\u002F\u002Fgithub.com\u002Ftangyixiao","http:\u002F\u002Fwww.sxyz.net\u002F","I am Tang Yixiao, a student in Shaoxing No.1 High School. I am eager to learn on GitHub and contribute my part to this community.","2026-11-11 15:45:59",{"source":8,"skillName":5,"commands":35,"prompt":45,"zipAvailable":46},[36,39,42],{"tool":37,"command":38},"npx","npx skills add tangyixiao\u002Ftangyixiao-skills --skill hf-cloud-sagemaker-iam-preflight",{"tool":40,"command":41},"bunx","bunx skills add tangyixiao\u002Ftangyixiao-skills --skill hf-cloud-sagemaker-iam-preflight",{"tool":43,"command":44},"pnpm","pnpm dlx skills add tangyixiao\u002Ftangyixiao-skills --skill hf-cloud-sagemaker-iam-preflight","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fhf-cloud-sagemaker-iam-preflight\u002FSKILL.md",true,[48,54,58,62,66,72],{"path":49,"resourceType":50,"resourceTypeDesc":51,"sizeBytes":52,"sourceHtmlUrl":53},"INSTALL-SOURCE.json","REFERENCE","参考文档",555,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fhf-cloud-sagemaker-iam-preflight\u002FINSTALL-SOURCE.json",{"path":55,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":57,"sourceHtmlUrl":16},"LICENSE","许可证",11357,{"path":59,"resourceType":50,"resourceTypeDesc":51,"sizeBytes":60,"sourceHtmlUrl":61},"references\u002Fminimum-permissions.json",1043,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fhf-cloud-sagemaker-iam-preflight\u002Freferences\u002Fminimum-permissions.json",{"path":63,"resourceType":50,"resourceTypeDesc":51,"sizeBytes":64,"sourceHtmlUrl":65},"references\u002Ftrust-policy.json",197,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fhf-cloud-sagemaker-iam-preflight\u002Freferences\u002Ftrust-policy.json",{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"scripts\u002Fcheck_role.py","SCRIPT","脚本",6862,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fhf-cloud-sagemaker-iam-preflight\u002Fscripts\u002Fcheck_role.py",{"path":73,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":74,"sourceHtmlUrl":75},"scripts\u002Fcreate_role.py",3856,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fhf-cloud-sagemaker-iam-preflight\u002Fscripts\u002Fcreate_role.py",[77,85,94,102,110,118],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":81,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":20},"01M7D8JK8FYBFNNAJXWD89CFH7","xlsx","Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fxlsx\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fxlsx\u002FSKILL.md",[],[],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":90,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":20},"01M7D8JG8ERGGQQAMX0DWCG9GR","writing-shape","Writing, exploit: shape raw material into an article, paragraph by paragraph.","MIT","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-shape\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-shape\u002FSKILL.md",[],[],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":98,"sourceHtmlUrl":99,"categories":100,"keywords":101,"publishedAt":20},"01M7D8JE672GW3EHSE8EF185M4","writing-fragments","Writing, explore: mine raw fragments, no structure yet.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-fragments\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-fragments\u002FSKILL.md",[],[],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":106,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":20},"01M7D8JBK572YDFMND8Q6CEYJW","writing-for-agents","Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-for-agents\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-for-agents\u002FSKILL.md",[],[],{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":114,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":20},"01M7D8J5QPWEXX61EGBKYH73R8","writing-clearly-and-concisely","Use when writing prose humans will read—documentation, commit messages, error messages, explanations, reports, or UI text. Applies Strunk's timeless rules for clearer, stronger, more professional writing.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-clearly-and-concisely\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-clearly-and-concisely\u002FSKILL.md",[],[],{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":122,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":20},"01M7D8J36YWC8WZS5DK5AH03CD","writing-beats","Writing, exploit; assemble raw material into a journey of beats, grounding each term before a beat leans on it.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-beats\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-beats\u002FSKILL.md",[],[]]