[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MMZGA4SJ8WN43H9N2YA4ZW":3,"skill-same:01M6MMZGA4SJ8WN43H9N2YA4ZW":68},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":49,"ownerHtmlUrl":50,"ownerBlogUrl":51,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6MMZGA4SJ8WN43H9N2YA4ZW","simulink-simulation",null,"Use this skill whenever simulating a Simulink model, running the sim command, setting up SimulationInput objects, passing input signals via timeseries or datasets, configuring model or block parameters programmatically, or accessing logged output data from SimulationOutput. Trigger for any request involving sim(), Simulink.SimulationInput, Simulink.SimulationOutput, logsout, setExternalInput, or setModelParameter.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1559,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fsummer521521\u002FMATLAB_Simulink_plugin\u002Fskills\u002Fsimulink-simulation\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"simulation","simulink simulation","trigger","running","request","for","sim","block","output","via","input","timeseries","signals","objects","datasets","up","use","command","model","data","2026-11-10 10:12:30",false,"---\nname: simulink-simulation\ndescription: Use this skill whenever simulating a Simulink model, running the sim command, setting up SimulationInput objects, passing input signals via timeseries or datasets, configuring model or block parameters programmatically, or accessing logged output data from SimulationOutput. Trigger for any request involving sim(), Simulink.SimulationInput, Simulink.SimulationOutput, logsout, setExternalInput, or setModelParameter.\n---\n\n# Simulating Simulink Models with the sim Command\n\n## Minimal working pattern\n\nAlways simulate using `Simulink.SimulationInput` and `Simulink.SimulationOutput`:\n\n```matlab\nin = Simulink.SimulationInput('MyModel');\nin = in.setModelParameter('StopTime', '10');\nout = sim(in);\n```\n\nNever use `set_param`, `load_system`, or `open_system` to drive simulation — the `SimulationInput` API replaces all of these.\n\nIf the MATLAB MCP tool is unavailable but the user explicitly asks for real artifacts such as `.slx`, exported figures, or `.mat` results, use local MATLAB execution such as `matlab -batch` as a fallback. Give MATLAB generous timeouts because startup alone can take minutes.\n\nFor MATLAB MCP core server v0.9.0 and newer, this personal plugin launcher can pass through optional session\u002Flogging settings via environment variables:\n- `MATLAB_SESSION_MODE` -> `--matlab-session-mode`\n- `MATLAB_EXTENSION_FILE` -> `--extension-file`\n- `MATLAB_EXTENSION_FILES` -> repeated `--extension-file` values, separated by the OS path separator (`;` on Windows)\n- `MATLAB_LOG_FOLDER` -> `--log-folder`\n- `MATLAB_LOG_LEVEL` -> `--log-level`\n\nMATLAB MCP core server v0.9.2 improves `--initialize-matlab-on-startup=true` so the MCP server is not blocked while MATLAB starts, and fixes `shareMATLABSession()` failures on Windows. Keep the launcher default lazy startup unless the user explicitly wants startup-time initialization.\n\nMATLAB MCP core server v0.10.0 makes `auto` the default session mode. Leave `MATLAB_SESSION_MODE` unset for normal use, set it to `existing` only when the user has run `shareMATLABSession()` in the target MATLAB session, and use multiple extension files only when the current MCP server exposes those custom tools.\n\nKeep `MATLAB_SETUP_MATLAB` unset for normal MCP sessions; set it only for a one-time upstream setup run.\n\n## Setting parameters\n\nUse `SimulationInput` methods to configure the simulation:\n\n```matlab\n% Model-level parameters (StopTime, SolverType, SimulationMode, etc.)\nin = in.setModelParameter('StopTime', '10', 'SolverType', 'Fixed-step');\n\n% Block parameters\nin = in.setBlockParameter('MyModel\u002FGain', 'Gain', '5');\n\n% MATLAB workspace variables used by the model\nin = in.setVariable('Kp', 1.2);\n```\n\n## Input signals\n\nPass input signals through Inport blocks using a `Simulink.SimulationData.Dataset`. Each timeseries `Name` must match the corresponding Inport block's signal name, otherwise the signal won't be routed correctly.\n\n```matlab\ndt = 0.01; % sample time\nN = 1000; % Number of points\nt = dt*(0:N)';\nu = sin(2*pi*t);\n\nts = timeseries(u, t);\nts.Name = 'mySignal';   % must match the Inport signal name in the model\n\nds = Simulink.SimulationData.Dataset;\nds{1} = ts;\n\nin = in.setExternalInput(ds);\nout = sim(in);\n```\n\n## Discovering logged signals\n\nBefore accessing logged data by name, discover what signals the model actually logs by running a simulation and inspecting `logsout`:\n\n```matlab\nin = Simulink.SimulationInput('MyModel');\nout = sim(in);\ndisp('List of logged signals:');\ndisp(out.logsout.getElementNames);\n```\n\nThis is especially useful when working with an unfamiliar model — the names returned here are exactly the names to use when calling `out.logsout.get(...)`.\n\n## Accessing logged data\n\nLogged signals are available through `out.logsout`. Access them directly by name — no intermediate variables needed:\n\n```matlab\n% Plot a logged signal\nplot(out.logsout.get('signalName').Values)\n\n% Get time and data separately\nsig = out.logsout.get('signalName').Values;\nplot(sig.Time, sig.Data)\n```\n\nDo not validate `out` with try-catch or `isfield` — `sim` either returns a valid `SimulationOutput` or throws an error. `Simulink.SimulationOutput` has no `isfield` method.\n\n## Accessing To Workspace Outputs\n\nIf the model uses `To Workspace` blocks and `ReturnWorkspaceOutputs` is on, retrieve those outputs directly from the `SimulationOutput` object:\n\n```matlab\nin = Simulink.SimulationInput('MyModel');\nin = in.setModelParameter('ReturnWorkspaceOutputs', 'on');\nout = sim(in);\n\nT = out.simout_T;\nQ = out.simout_Qbase;\n```\n\nNormalize the exported value before post-processing because `To Workspace` may return a `timeseries`, a `Simulink.SimulationData.Signal`, or a numeric matrix depending on block settings:\n\n```matlab\nif isa(T, 'timeseries')\n    t = T.Time(:);\n    y = T.Data(:);\nelseif isa(T, 'Simulink.SimulationData.Signal')\n    t = T.Values.Time(:);\n    y = T.Values.Data(:);\nelse\n    t = T(:,1);\n    y = T(:,2);\nend\n```\n\n## Multiple simulations\n\nWhen running many simulations, create an array of `Simulink.SimulationInput` objects using the `repmat` function instead of looping over `sim`:\n\n```matlab\nin = repmat(Simulink.SimulationInput('MyModel'),N,1);\nfor k = 1:N\n    in(k) = Simulink.SimulationInput('MyModel');\n    in(k) = in(k).setVariable('gain', gains(k));\nend\nout = sim(in);\n```\n\nWhen collecting results from multiple cases into a struct array, preallocate a homogeneous struct shape before the loop. MATLAB will throw \"subscripted assignment between dissimilar structures\" if later cases add fields or nested layouts that the first element did not have.\n\n## Parallel simulation (parsim)\n\nTo run multiple simulations, use `parsim` instead of looping over `sim`:\n\n```matlab\nfor k = 1:N\n    in(k) = Simulink.SimulationInput('MyModel');\n    in(k) = in(k).setVariable('gain', gains(k));\nend\nout = parsim(in);\n```\n\n## Rebuild-and-Simulate Pitfalls\n\nIf a script repeatedly rebuilds and reruns a model:\n\n- Save or clear the dirty flag before `close_system`, otherwise MATLAB can warn that the changed model cannot be closed.\n- Avoid model names that shadow scripts or functions on the MATLAB path; model\u002Fscript name collisions can produce confusing warnings during `sim` and `run`.\n- When a generated model relies on MATLAB Function blocks with workspace parameters, make sure those symbols are declared as block parameters during model construction; otherwise simulation can fail before `sim` starts with output-size\u002Ftype inference errors.\n","# Simulating Simulink Models with the sim Command\n\n## Minimal working pattern\n\nAlways simulate using `Simulink.SimulationInput` and `Simulink.SimulationOutput`:\n\n```matlab\nin = Simulink.SimulationInput('MyModel');\nin = in.setModelParameter('StopTime', '10');\nout = sim(in);\n```\n\nNever use `set_param`, `load_system`, or `open_system` to drive simulation — the `SimulationInput` API replaces all of these.\n\nIf the MATLAB MCP tool is unavailable but the user explicitly asks for real artifacts such as `.slx`, exported figures, or `.mat` results, use local MATLAB execution such as `matlab -batch` as a fallback. Give MATLAB generous timeouts because startup alone can take minutes.\n\nFor MATLAB MCP core server v0.9.0 and newer, this personal plugin launcher can pass through optional session\u002Flogging settings via environment variables:\n- `MATLAB_SESSION_MODE` -> `--matlab-session-mode`\n- `MATLAB_EXTENSION_FILE` -> `--extension-file`\n- `MATLAB_EXTENSION_FILES` -> repeated `--extension-file` values, separated by the OS path separator (`;` on Windows)\n- `MATLAB_LOG_FOLDER` -> `--log-folder`\n- `MATLAB_LOG_LEVEL` -> `--log-level`\n\nMATLAB MCP core server v0.9.2 improves `--initialize-matlab-on-startup=true` so the MCP server is not blocked while MATLAB starts, and fixes `shareMATLABSession()` failures on Windows. Keep the launcher default lazy startup unless the user explicitly wants startup-time initialization.\n\nMATLAB MCP core server v0.10.0 makes `auto` the default session mode. Leave `MATLAB_SESSION_MODE` unset for normal use, set it to `existing` only when the user has run `shareMATLABSession()` in the target MATLAB session, and use multiple extension files only when the current MCP server exposes those custom tools.\n\nKeep `MATLAB_SETUP_MATLAB` unset for normal MCP sessions; set it only for a one-time upstream setup run.\n\n## Setting parameters\n\nUse `SimulationInput` methods to configure the simulation:\n\n```matlab\n% Model-level parameters (StopTime, SolverType, SimulationMode, etc.)\nin = in.setModelParameter('StopTime', '10', 'SolverType', 'Fixed-step');\n\n% Block parameters\nin = in.setBlockParameter('MyModel\u002FGain', 'Gain', '5');\n\n% MATLAB workspace variables used by the model\nin = in.setVariable('Kp', 1.2);\n```\n\n## Input signals\n\nPass input signals through Inport blocks using a `Simulink.SimulationData.Dataset`. Each timeseries `Name` must match the corresponding Inport block's signal name, otherwise the signal won't be routed correctly.\n\n```matlab\ndt = 0.01; % sample time\nN = 1000; % Number of points\nt = dt*(0:N)';\nu = sin(2*pi*t);\n\nts = timeseries(u, t);\nts.Name = 'mySignal';   % must match the Inport signal name in the model\n\nds = Simulink.SimulationData.Dataset;\nds{1} = ts;\n\nin = in.setExternalInput(ds);\nout = sim(in);\n```\n\n## Discovering logged signals\n\nBefore accessing logged data by name, discover what signals the model actually logs by running a simulation and inspecting `logsout`:\n\n```matlab\nin = Simulink.SimulationInput('MyModel');\nout = sim(in);\ndisp('List of logged signals:');\ndisp(out.logsout.getElementNames);\n```\n\nThis is especially useful when working with an unfamiliar model — the names returned here are exactly the names to use when calling `out.logsout.get(...)`.\n\n## Accessing logged data\n\nLogged signals are available through `out.logsout`. Access them directly by name — no intermediate variables needed:\n\n```matlab\n% Plot a logged signal\nplot(out.logsout.get('signalName').Values)\n\n% Get time and data separately\nsig = out.logsout.get('signalName').Values;\nplot(sig.Time, sig.Data)\n```\n\nDo not validate `out` with try-catch or `isfield` — `sim` either returns a valid `SimulationOutput` or throws an error. `Simulink.SimulationOutput` has no `isfield` method.\n\n## Accessing To Workspace Outputs\n\nIf the model uses `To Workspace` blocks and `ReturnWorkspaceOutputs` is on, retrieve those outputs directly from the `SimulationOutput` object:\n\n```matlab\nin = Simulink.SimulationInput('MyModel');\nin = in.setModelParameter('ReturnWorkspaceOutputs', 'on');\nout = sim(in);\n\nT = out.simout_T;\nQ = out.simout_Qbase;\n```\n\nNormalize the exported value before post-processing because `To Workspace` may return a `timeseries`, a `Simulink.SimulationData.Signal`, or a numeric matrix depending on block settings:\n\n```matlab\nif isa(T, 'timeseries')\n    t = T.Time(:);\n    y = T.Data(:);\nelseif isa(T, 'Simulink.SimulationData.Signal')\n    t = T.Values.Time(:);\n    y = T.Values.Data(:);\nelse\n    t = T(:,1);\n    y = T(:,2);\nend\n```\n\n## Multiple simulations\n\nWhen running many simulations, create an array of `Simulink.SimulationInput` objects using the `repmat` function instead of looping over `sim`:\n\n```matlab\nin = repmat(Simulink.SimulationInput('MyModel'),N,1);\nfor k = 1:N\n    in(k) = Simulink.SimulationInput('MyModel');\n    in(k) = in(k).setVariable('gain', gains(k));\nend\nout = sim(in);\n```\n\nWhen collecting results from multiple cases into a struct array, preallocate a homogeneous struct shape before the loop. MATLAB will throw \"subscripted assignment between dissimilar structures\" if later cases add fields or nested layouts that the first element did not have.\n\n## Parallel simulation (parsim)\n\nTo run multiple simulations, use `parsim` instead of looping over `sim`:\n\n```matlab\nfor k = 1:N\n    in(k) = Simulink.SimulationInput('MyModel');\n    in(k) = in(k).setVariable('gain', gains(k));\nend\nout = parsim(in);\n```\n\n## Rebuild-and-Simulate Pitfalls\n\nIf a script repeatedly rebuilds and reruns a model:\n\n- Save or clear the dirty flag before `close_system`, otherwise MATLAB can warn that the changed model cannot be closed.\n- Avoid model names that shadow scripts or functions on the MATLAB path; model\u002Fscript name collisions can produce confusing warnings during `sim` and `run`.\n- When a generated model relies on MATLAB Function blocks with workspace parameters, make sure those symbols are declared as block parameters during model construction; otherwise simulation can fail before `sim` starts with output-size\u002Ftype inference errors.\n",{"name":5,"description":7},"main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002Fsummer521521\u002FMATLAB_Simulink_plugin\u002Fskills\u002Fsimulink-simulation",474,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-11 14:40:14",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill simulink-simulation",{"tool":60,"command":61},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill simulink-simulation",{"tool":63,"command":64},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill simulink-simulation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fsummer521521\u002FMATLAB_Simulink_plugin\u002Fskills\u002Fsimulink-simulation\u002FSKILL.md",true,[],[69,77,84,91,118,143],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":76},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"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":76},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\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":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":76},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\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":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":39},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":39},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[125,126,127,113,128,129,130,131,132,133,134,135,136,137,103,138,139,140,141,142],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":160,"publishedAt":39},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[149,154,157],{"categoryNo":150,"categoryName":151,"taxonomyType":152,"taxonomyTypeDesc":153},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":155,"categoryName":156,"taxonomyType":152,"taxonomyTypeDesc":153},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":158,"categoryName":159,"taxonomyType":152,"taxonomyTypeDesc":153},"01JBSEEDCAT000000000000010","后端与接口",[145,161,162,163,164,165,166,139,167,104,168,169,170,171,109,172,173,174,175,176],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]