[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DBKGDMVBEWEY9FB5S0Y75H":3,"skill-same:01M6DBKGDMVBEWEY9FB5S0Y75H":1239},{"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":29,"publishedAt":56,"linkOnly":57,"markdownContent":58,"markdownBody":59,"frontMatter":60,"refName":65,"commitSha":66,"skillDirectory":67,"forksCount":68,"repositoryHtmlUrl":69,"repositoryHomepageUrl":17,"ownerHtmlUrl":70,"ownerBlogUrl":71,"ownerDescription":72,"updatedAt":73,"install":74,"resources":86},"01M6DBKGDMVBEWEY9FB5S0Y75H","global-think-tank-analyst",null,"Strategic-risk analysis skill for AI agents producing decision-ready memos on geopolitics, sanctions, trade, regulation, and strategic risk with explicit evidence boundaries, uncertainty, scenarios, actor incentives, trade-offs, and watch-next indicators. Use for a country risk brief, policy memo, sanctions or export-control exposure assessment, trade or regulatory implications memo, geopolitical scenario brief, strategic implications note for leadership, stakeholder and incentives analysis, a red-team challenge to an existing policy or risk view, or a decision briefing pack for founders, operators, investors, NGOs, compliance teams, policy teams, or leadership. Do not use for a simple news recap, encyclopedia-style overview, academic literature review, legal advice, intelligence-style certainty, decorative analysis, or unsupported quantitative forecasting.","vassiliylakhonin\u002Fglobal-think-tank-analyst","vassiliylakhonin","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F260414237?v=4","01M6DBKD7AP8ZEAA2ZMYP2XMJ2","Vassiliy Lakhonin","01M6DBKDBKWNP9EM6X52DPXHXK",7,"MIT","https:\u002F\u002Fgithub.com\u002Fvassiliylakhonin\u002Fglobal-think-tank-analyst\u002Fblob\u002Fmain\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fvassiliylakhonin\u002Fglobal-think-tank-analyst\u002Fblob\u002Fmain\u002FSKILL.md",[19,24],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",{"categoryNo":25,"categoryName":26,"taxonomyType":27,"taxonomyTypeDesc":28},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55],"think tank","global think tank analyst","global","think","analyst","do","trade-offs","for","incentives","investors","literature","strategic risk","scenario","watch","operators","news","policy","legal","ready","use","view","challenge","decision","team","brief","assessment","2026-11-10 10:12:30",false,"---\nname: fastapi-uploadstream-binary-upload\ndescription: \"Guide for handling HTTP uploads of binary data in FastAPI using fastapi_uploadstream. Use when users ask about how to upload raw http bodies to a fastapi app without multipart encoding. Describes StreamBody, UploadStream, content-type validation, chunked reads, or OpenAPI binary requestBody setup.\"\nargument-hint: \"endpoint shape, accepted media types, and whether OpenAPI docs are needed\"\nuser-invocable: true\n---\n\n# FastAPI UploadStream Binary Uploads\n\nUse this skill when you need to implement or explain raw HTTP binary uploads in FastAPI with `fastapi_uploadstream`.\n\n## What This Skill Covers\n\n- Installing and importing the library\n- Defining a streaming endpoint with `StreamBody`\n- Reading upload bytes safely with `UploadStream`\n- Restricting accepted `Content-Type` values\n- Publishing correct OpenAPI `requestBody` schema for binary bodies\n\n## Core Pattern\n\nUse `Annotated[UploadStream, StreamBody(...)]` on a single endpoint parameter.\nCall `install_uploadstream_openapi(app)` once during app setup.\n\n```python\nfrom typing import Annotated\n\nfrom fastapi import FastAPI\nfrom fastapi_uploadstream import StreamBody, UploadStream, install_uploadstream_openapi\n\nimport hashlib  # For demo only - not required.\n\napp = FastAPI()\ninstall_uploadstream_openapi(app)\n\n\n@app.post(\"\u002Fupload\")\nasync def upload_binary(\n    body: Annotated[\n        UploadStream,\n        StreamBody(\n            media_types=[\"application\u002Foctet-stream\"],\n            title=\"Binary upload\",\n            description=\"Raw binary request body to hash and return metadata\",\n        ),\n    ],\n):\n    # Calculate SHA-256 hash of the uploaded data (for demo purposes)\n    sha256 = hashlib.sha256()\n    async for chunk in body.iter_chunks():\n        sha256.update(chunk)\n    return {\n        \"bytes\": body.size,\n        \"content_type\": body.content_type,\n        \"filename\": body.filename,\n        \"size\": body.size,\n        \"sha256\": sha256.hexdigest(),\n    }\n```\n\n## Practical Guidance\n\n1. Prefer `read(size)` or `iter_chunks()` for large payloads.\n2. Set `media_types` to explicit values like `application\u002Foctet-stream` or `image\u002F*`.\n3. Expect HTTP 415 when the request `Content-Type` is not allowed.\n4. Use only one `StreamBody(...)` per endpoint.\n5. Do not mix `StreamBody` with `Body`, `Form`, `File`, or `UploadFile` on the same endpoint.\n\n## Streaming Large Files\n\n```python\n@app.post(\"\u002Fupload-large\")\nasync def upload_large(\n    body: Annotated[UploadStream, StreamBody(media_types=[\"application\u002Foctet-stream\"])],\n):\n    total = 0\n    async for chunk in body.iter_chunks(chunk_size=64 * 1024):\n        total += len(chunk)\n        # Process each chunk here.\n    return {\"bytes\": total}\n```\n\n## Ingesting whole file in memory (only small files)\n\nYou can also read the entire upload into memory for small files:\nBut beware that this can lead to high memory usage for large uploads, and should be avoided.\n```python\n@app.post(\"\u002Fupload-small\")\nasync def upload_small(\n    body: Annotated[UploadStream, StreamBody(media_types=[\"application\u002Foctet-stream\"])],\n):\n    total = 0\n    # watch out! this loads the entire request body!\n    data = await body.read()\n    total = len(data)\n    return {\"bytes\": total}\n```\n\n## Client Example\n\n```bash\ncurl -X POST \"http:\u002F\u002Flocalhost:8000\u002Fupload\" \\\n  -H \"content-type: application\u002Foctet-stream\" \\\n  -H \"x-filename: payload.bin\" \\\n  --data-binary @payload.bin\n```\n\n## Request Handling Notes\n\n- `body.filename` comes from the `x-filename` header when present.\n- `body.size` is derived from `Content-Length` when available.\n- `body.content_type` is the incoming request content type.\n- `read(-1)` reads all remaining bytes.\n\n## More Examples\n\nSee [reference examples](.\u002Freferences\u002Fexamples.md) for common endpoint variants.","# FastAPI UploadStream Binary Uploads\n\nUse this skill when you need to implement or explain raw HTTP binary uploads in FastAPI with `fastapi_uploadstream`.\n\n## What This Skill Covers\n\n- Installing and importing the library\n- Defining a streaming endpoint with `StreamBody`\n- Reading upload bytes safely with `UploadStream`\n- Restricting accepted `Content-Type` values\n- Publishing correct OpenAPI `requestBody` schema for binary bodies\n\n## Core Pattern\n\nUse `Annotated[UploadStream, StreamBody(...)]` on a single endpoint parameter.\nCall `install_uploadstream_openapi(app)` once during app setup.\n\n```python\nfrom typing import Annotated\n\nfrom fastapi import FastAPI\nfrom fastapi_uploadstream import StreamBody, UploadStream, install_uploadstream_openapi\n\nimport hashlib  # For demo only - not required.\n\napp = FastAPI()\ninstall_uploadstream_openapi(app)\n\n\n@app.post(\"\u002Fupload\")\nasync def upload_binary(\n    body: Annotated[\n        UploadStream,\n        StreamBody(\n            media_types=[\"application\u002Foctet-stream\"],\n            title=\"Binary upload\",\n            description=\"Raw binary request body to hash and return metadata\",\n        ),\n    ],\n):\n    # Calculate SHA-256 hash of the uploaded data (for demo purposes)\n    sha256 = hashlib.sha256()\n    async for chunk in body.iter_chunks():\n        sha256.update(chunk)\n    return {\n        \"bytes\": body.size,\n        \"content_type\": body.content_type,\n        \"filename\": body.filename,\n        \"size\": body.size,\n        \"sha256\": sha256.hexdigest(),\n    }\n```\n\n## Practical Guidance\n\n1. Prefer `read(size)` or `iter_chunks()` for large payloads.\n2. Set `media_types` to explicit values like `application\u002Foctet-stream` or `image\u002F*`.\n3. Expect HTTP 415 when the request `Content-Type` is not allowed.\n4. Use only one `StreamBody(...)` per endpoint.\n5. Do not mix `StreamBody` with `Body`, `Form`, `File`, or `UploadFile` on the same endpoint.\n\n## Streaming Large Files\n\n```python\n@app.post(\"\u002Fupload-large\")\nasync def upload_large(\n    body: Annotated[UploadStream, StreamBody(media_types=[\"application\u002Foctet-stream\"])],\n):\n    total = 0\n    async for chunk in body.iter_chunks(chunk_size=64 * 1024):\n        total += len(chunk)\n        # Process each chunk here.\n    return {\"bytes\": total}\n```\n\n## Ingesting whole file in memory (only small files)\n\nYou can also read the entire upload into memory for small files:\nBut beware that this can lead to high memory usage for large uploads, and should be avoided.\n```python\n@app.post(\"\u002Fupload-small\")\nasync def upload_small(\n    body: Annotated[UploadStream, StreamBody(media_types=[\"application\u002Foctet-stream\"])],\n):\n    total = 0\n    # watch out! this loads the entire request body!\n    data = await body.read()\n    total = len(data)\n    return {\"bytes\": total}\n```\n\n## Client Example\n\n```bash\ncurl -X POST \"http:\u002F\u002Flocalhost:8000\u002Fupload\" \\\n  -H \"content-type: application\u002Foctet-stream\" \\\n  -H \"x-filename: payload.bin\" \\\n  --data-binary @payload.bin\n```\n\n## Request Handling Notes\n\n- `body.filename` comes from the `x-filename` header when present.\n- `body.size` is derived from `Content-Length` when available.\n- `body.content_type` is the incoming request content type.\n- `read(-1)` reads all remaining bytes.\n\n## More Examples\n\nSee [reference examples](.\u002Freferences\u002Fexamples.md) for common endpoint variants.",{"name":61,"description":62,"argument-hint":63,"user-invocable":64},"fastapi-uploadstream-binary-upload","Guide for handling HTTP uploads of binary data in FastAPI using fastapi_uploadstream. 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