[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7ECFZGCQRVJSCT1RWR7S8S2":3,"skill-same:01M7ECFZGCQRVJSCT1RWR7S8S2":62},{"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":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":6,"ownerHtmlUrl":32,"ownerBlogUrl":6,"ownerDescription":33,"updatedAt":34,"install":35,"resources":47},"01M7ECFZGCQRVJSCT1RWR7S8S2","spaces-data",null,"Design Gymnasium action and observation spaces, validate samples, flatten\u002Funflatten data, serialize JSONable batches, and debug dtype\u002Fshape mismatches.","lza6\u002FArmor-piercing-system-prompts","lza6","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F128880206?v=4","01M7EB3K7WPX5T28EJ2NVHE6EG","Chinese-tingfeng","01M7EB3K97E1KQ16QWKJMFNGX8",11,"MIT","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F03-逆向工程\u002Fgymnasium\u002Fsub-skills\u002Fspaces-data\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: spaces-data\ndescription: \"Design Gymnasium action and observation spaces, validate samples,\n  flatten\u002Funflatten data, serialize JSONable batches, and debug dtype\u002Fshape\n  mismatches.\"\ndisable-model-invocation: true\nmetadata:\n  disco-role: operating\nlicense: MIT\n---\n\n# Gymnasium Spaces and Data Contracts\n\nUse this sub-skill when a task involves choosing Gymnasium action or observation spaces, validating samples with `contains`, seeding or sampling spaces, flattening nested data for model inputs, unflattening model vectors back to structured observations\u002Factions, or serializing space samples.\n\n## Start Here\n\n- For constructor choices and data contracts, use `references\u002Fspace-design.md` before modifying an environment's `action_space` or `observation_space`.\n- For `flatdim`, `flatten_space`, `flatten`, `unflatten`, `to_jsonable`, and `from_jsonable`, use `references\u002Fflattening-and-json.md`.\n- For `Box.contains` surprises, integer starts, nested flatten shape mismatches, dynamic spaces, and wrapper space updates, use `references\u002Ftroubleshooting.md`.\n- To smoke-test the local Gymnasium install and demonstrate the core contract, run `python sub-skills\u002Fspaces-data\u002Fscripts\u002Fspace_contract_smoke.py --help` and then `python sub-skills\u002Fspaces-data\u002Fscripts\u002Fspace_contract_smoke.py` from the root Gymnasium skill directory.\n\n## Scope Boundaries\n\n- Use `..\u002Fenvironment-api\u002FSKILL.md` for custom `Env` lifecycle, `reset`\u002F`step`, registration, `check_env`, and seeding an environment through `reset(seed=...)`.\n- Use `..\u002Fwrappers-recording\u002FSKILL.md` when a wrapper changes `observation_space`, `action_space`, observations, actions, rewards, rendering, or recording behavior.\n- Use `..\u002Fvectorization\u002FSKILL.md` for batched\u002Fvector spaces, `gym.vector` utilities, `single_observation_space`, `single_action_space`, and vector wrapper space transforms.\n\n## Minimum Correct Patterns\n\n```python\nimport numpy as np\nfrom gymnasium import spaces\n\nobservation_space = spaces.Dict({\n    \"position\": spaces.Box(low=-1.0, high=1.0, shape=(2,), dtype=np.float32),\n    \"inventory\": spaces.Tuple((spaces.Discrete(5), spaces.MultiBinary(3))),\n})\n\nobservation = observation_space.sample()\nassert observation_space.contains(observation)\n```\n\n```python\nfrom gymnasium.spaces import utils as space_utils\n\nflat = space_utils.flatten(observation_space, observation)\nrestored = space_utils.unflatten(observation_space, flat)\nassert observation_space.contains(restored)\n```\n\nKeep runtime guidance self-contained. Do not depend on Gymnasium source, tests, examples, or documentation paths at runtime.\n","# Gymnasium Spaces and Data Contracts\n\nUse this sub-skill when a task involves choosing Gymnasium action or observation spaces, validating samples with `contains`, seeding or sampling spaces, flattening nested data for model inputs, unflattening model vectors back to structured observations\u002Factions, or serializing space samples.\n\n## Start Here\n\n- For constructor choices and data contracts, use `references\u002Fspace-design.md` before modifying an environment's `action_space` or `observation_space`.\n- For `flatdim`, `flatten_space`, `flatten`, `unflatten`, `to_jsonable`, and `from_jsonable`, use `references\u002Fflattening-and-json.md`.\n- For `Box.contains` surprises, integer starts, nested flatten shape mismatches, dynamic spaces, and wrapper space updates, use `references\u002Ftroubleshooting.md`.\n- To smoke-test the local Gymnasium install and demonstrate the core contract, run `python sub-skills\u002Fspaces-data\u002Fscripts\u002Fspace_contract_smoke.py --help` and then `python sub-skills\u002Fspaces-data\u002Fscripts\u002Fspace_contract_smoke.py` from the root Gymnasium skill directory.\n\n## Scope Boundaries\n\n- Use `..\u002Fenvironment-api\u002FSKILL.md` for custom `Env` lifecycle, `reset`\u002F`step`, registration, `check_env`, and seeding an environment through `reset(seed=...)`.\n- Use `..\u002Fwrappers-recording\u002FSKILL.md` when a wrapper changes `observation_space`, `action_space`, observations, actions, rewards, rendering, or recording behavior.\n- Use `..\u002Fvectorization\u002FSKILL.md` for batched\u002Fvector spaces, `gym.vector` utilities, `single_observation_space`, `single_action_space`, and vector wrapper space transforms.\n\n## Minimum Correct Patterns\n\n```python\nimport numpy as np\nfrom gymnasium import spaces\n\nobservation_space = spaces.Dict({\n    \"position\": spaces.Box(low=-1.0, high=1.0, shape=(2,), dtype=np.float32),\n    \"inventory\": spaces.Tuple((spaces.Discrete(5), spaces.MultiBinary(3))),\n})\n\nobservation = observation_space.sample()\nassert observation_space.contains(observation)\n```\n\n```python\nfrom gymnasium.spaces import utils as space_utils\n\nflat = space_utils.flatten(observation_space, observation)\nrestored = space_utils.unflatten(observation_space, flat)\nassert observation_space.contains(restored)\n```\n\nKeep runtime guidance self-contained. Do not depend on Gymnasium source, tests, examples, or documentation paths at runtime.\n",{"name":5,"license":15,"metadata":24,"description":7,"disable-model-invocation":26},{"disco-role":25},"operating",true,"main","d69096e239f2a89aabc2cd078c47664a9d3b1e11","03-逆向工程\u002Fgymnasium\u002Fsub-skills\u002Fspaces-data",3,"https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts","https:\u002F\u002Fgithub.com\u002Flza6","个人vx：Tf00798\r\n个人✈：@TFGY999","2026-10-08 14:29:22",{"source":8,"skillName":5,"commands":36,"prompt":46,"zipAvailable":26},[37,40,43],{"tool":38,"command":39},"npx","npx skills add lza6\u002FArmor-piercing-system-prompts --skill spaces-data",{"tool":41,"command":42},"bunx","bunx skills add lza6\u002FArmor-piercing-system-prompts --skill spaces-data",{"tool":44,"command":45},"pnpm","pnpm dlx skills add lza6\u002FArmor-piercing-system-prompts --skill spaces-data","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F03-逆向工程\u002Fgymnasium\u002Fsub-skills\u002Fspaces-data\u002FSKILL.md",[48,54,58],{"path":49,"resourceType":50,"resourceTypeDesc":51,"sizeBytes":52,"sourceHtmlUrl":53},"references\u002Fflattening-and-json.md","REFERENCE","参考文档",7963,"https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F03-逆向工程\u002Fgymnasium\u002Fsub-skills\u002Fspaces-data\u002Freferences\u002Fflattening-and-json.md",{"path":55,"resourceType":50,"resourceTypeDesc":51,"sizeBytes":56,"sourceHtmlUrl":57},"references\u002Fspace-design.md",9101,"https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F03-逆向工程\u002Fgymnasium\u002Fsub-skills\u002Fspaces-data\u002Freferences\u002Fspace-design.md",{"path":59,"resourceType":50,"resourceTypeDesc":51,"sizeBytes":60,"sourceHtmlUrl":61},"references\u002Ftroubleshooting.md",7876,"https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F03-逆向工程\u002Fgymnasium\u002Fsub-skills\u002Fspaces-data\u002Freferences\u002Ftroubleshooting.md",[63,70,77,84,92,99],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":19},"01M7EXRM12046G0GM1WGMMFXCN","ci-cd-and-automation","Automates CI\u002FCD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fci-cd-and-automation\u002FSKILL.md",[],[],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M7EXRFYYSJ3Y15AGPDDEWWQD","chrome-devtools","Uses Chrome DevTools via MCP for efficient debugging, troubleshooting and browser automation. Use when debugging web pages, automating browser interactions, analyzing performance, or inspecting network requests. This skill does not apply to `--slim` mode (MCP configuration).","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fchrome-devtools\u002FSKILL.md",[],[],{"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":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M7EXR6EYPE4X5X1BTAE6QJRV","chrome-cdp","Drive a real Chrome via the Chrome DevTools Protocol (CDP). Ensures a\ndebuggable Chrome is running (launching it if needed) so the agent can\nnavigate, read page content, inspect the DOM, run JavaScript, and take\nscreenshots — without the user typing any launch command. Includes a\nfirst-class \"review a live running app\" flow: point Chrome at the app under\ndevelopment and inspect\u002Fscreenshot the REAL page (what a static-artifact tool\ncannot do). Backed by Glimpse and the chrome-devtools MCP server. Use when\nasked to \"use chrome\", \"open chrome\", \"drive my browser\", \"read the page in\nchrome\", \"review the running app\", \"screenshot the app\", or \"\u002Fchrome-cdp\".","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fchrome-cdp\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":88,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":19},"01M7EXR1Y7E79KQKG4J798XGH6","check-identity-pack","Run an AFP 100-point or AUSTRAC safe-harbour identity check over a set of documents, and report exactly what's missing. Use when the user asks to check identity documents, verify someone's ID for onboarding, or assess whether a document pack satisfies Australian identity requirements.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fcheck-identity-pack\u002FSKILL.md",[],[],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":19},"01M7EXQXW7C7PGREJW9ZZTZ16H","changelog","Auto-generates a changelog from git commits, sprint data, and design documents. Produces both internal and player-facing versions.","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fchangelog\u002FSKILL.md",[],[],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":19},"01M7EXQMH2ZME3ANN8Q8MEWPFB","cellxgene-census","Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs\u002Fdownloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.","https:\u002F\u002Fgithub.com\u002Flza6\u002FArmor-piercing-system-prompts\u002Fblob\u002Fmain\u002F05-技能与代理开发\u002Fcellxgene-census\u002FSKILL.md",[],[]]