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Agent Skill · 研究检索

modellix(modellix 视频)

modellix 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

modellix 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 45,181。

总安装 865周安装 35GitHub Stars 1下载 272核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:modellix(modellix 视频)
来源仓库:https://github.com/modellix/modellix-skill
仓库路径:skills/modellix
安装命令:
npx skills add https://github.com/modellix/modellix-skill --skill modellix
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/modellix/modellix-skill --skill modellix

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。通过 npx skills add 命令从指定仓库安装,需确认权限和维护状态。涉及联网、命令执行或文件读写时,应先评估安全风险和操作边界。建议结合原始 README 核验具体用法和功能细节。

来源仓库内路径:skills/modellix

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/modellix/modellix-skill --skill modellix;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

modellix 怎么安装?

在终端执行 npx skills add https://github.com/modellix/modellix-skill --skill modellix。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

modellix 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

modellix 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

modellix 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

modellix 用的人多吗?

公开数据是累计安装 865 次,近一周安装 35 次,下载 272 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

modellix 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
modellix

modellix 视频 · 本页

8651接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Modellix Skill

Modellix is a Model-as-a-Service (MaaS) platform with async image/video generation APIs. The invariant flow is: submit task -> get task_id -> poll until success or failed.

Official Docs

Execution Policy (CLI-first)

Always choose execution path in this order:

  1. Use CLI when modellix-cli is available and authenticated.
  2. Fall back to REST when CLI is unavailable, unsuitable, or missing capability.
  3. Prefer machine-readable outputs (--json) in CLI flows.

For CLI mode, use these two commands as the default command set:

  • Create task: modellix-cli model invoke --model-slug <provider/model> --body|--body-file...
  • Get result: modellix-cli task get <task_id>

Do not guess or invent deprecated flags (for example --model-type). Use --help only as an assistive fallback when command behavior is unclear.

API Key Lifecycle Policy

Always handle MODELLIX_API_KEY with this lifecycle: discover -> request -> use-session -> (optional) persist-user-env.

1) Discover existing key first

Before asking the user for credentials, check in this order:

  1. Current session environment variable MODELLIX_API_KEY.
  2. Existing user-level environment variable MODELLIX_API_KEY if already configured.
  3. If both are unavailable, treat as first-use and request key from user.

Never ask for a key again when a valid key is already discoverable.

2) Request key only when missing

If no usable key is found:

  • Ask user to provide a Modellix API key.
  • Do not print or echo key values in logs/output.
  • Use the key for current authentication flow in session scope by default.

3) Optional persistence for future sessions

Default behavior: do not persist automatically.

If and only if the user explicitly asks for persistence, write to user-level environment settings:

  1. Preferred and allowed persistent target: user-level MODELLIX_API_KEY.
  2. Do not write system-level environment variables by default.
  3. Do not write credentials into other coding agents' local config files.

4) Replace key when user provides a new one

If the user provides a new API key, treat it as a key rotation event:

  1. Replace current session value first.
  2. Only if the user explicitly requested persistence, also replace the user-level env value.
  3. Do not keep old and new keys active in parallel in this skill workflow.
  4. Re-run scripts/preflight.py --json after replacement and continue only if the new key is valid.

When replacement fails validation, keep the flow blocked, report the validation failure, and request a corrected key.

Preflight and Deterministic Execution

Default execution path is the CLI command pair (model invoke -> task get).

Bundled scripts are optional helpers for automation:

  1. scripts/preflight.py

- Validates CLI availability and API key presence. - Returns recommended mode (cli or rest).

  1. scripts/invoke_and_poll.py

- Executes CLI-first with REST fallback support. - Handles exponential backoff polling and retryable submit errors. - Emits normalized JSON result output.

When preflight reports missing credentials, apply the lifecycle policy above:

  1. Try discover flow (session env -> existing user env).
  2. Request key from user only if still missing.
  3. Use session value and retry.
  4. Persist only when explicitly requested by the user.

When preflight reports cli_available=false:

  1. Offer optional CLI install (npm i -g modellix-cli) and ask user consent first.
  2. If user declines or install fails, continue with REST fallback (supported path).

Quick commands:

python scripts/preflight.py --json
python scripts/invoke_and_poll.py --model-slug bytedance/seedream-4.5-t2i --body '{"prompt":"A cinematic portrait of a fox in a misty forest at sunrise"}'

Core Workflow

1) Discover or request API key

  • Run key discovery first (session env, then existing user-level env).
  • If not found, ask user for key created in Modellix Console.
  • Use key in session scope by default (no automatic persistence).
  • Persist only on explicit user consent:

- Allowed persistent target: user-level MODELLIX_API_KEY. - Not allowed by default: system-level env writes or other agent config writes.

  • If user provides a new key later, replace the existing stored key and re-run preflight validation.
  • Retry preflight and continue only after key is discoverable.

2) Select model

Read references/REFERENCE.md to find model docs and parameters.

3) Run invocation and poll

  • Preferred default: CLI command pair from references/cli-playbook.md

- modellix-cli model invoke... - modellix-cli task get <task_id>

  • Manual REST flow: references/rest-playbook.md
  • Optional helper: scripts/invoke_and_poll.py for auto polling/normalized output

4) Consume resources

Output media URLs are under result.resources. Persist assets promptly; results expire in 24 hours.

Progressive Reference Routing

Read only what the task needs:

  • references/cli-playbook.md

- CLI install/auth/command flow and retry guidance

  • references/rest-playbook.md

- REST endpoints, headers, status model, retry policy

  • references/capability-matrix.md

- CLI command <-> REST endpoint mapping and fallback rules

Bundled Assets

  • Output schema:

- assets/output/task-result.schema.json

Credential and Data Egress

  • Primary credential: MODELLIX_API_KEY.
  • Required env vars: MODELLIX_API_KEY.
  • This skill does not require any other secret.
  • Network egress: sends requests to https://api.modellix.ai.
  • User payload handling: prompts and user-provided inputs (including media URLs or file-derived content) may be sent to Modellix endpoints during invocation.
  • Result handling: generated resource URLs come from Modellix response payloads and should be downloaded before expiry (about 24 hours).
  • Secret hygiene:

- Never expose API keys in terminal output, logs, screenshots, transcripts, or commit content. - Mask sensitive values when showing command examples. - Default to session-only credential usage. - Any persistent write requires explicit user approval and must be user-level env only. - Do not write system-level env or other agent config files as part of this skill.

Error/Retry Policy

CodeAction
400Do not retry. Fix parameters or request body format.
401Do not retry. Verify API key.
402Do not retry. Insufficient balance.
404Do not retry. Verify task_id or model-slug.
429Retry with exponential backoff.
500/503Retry with exponential backoff (max 3 times).

Verification Checklist

  • Preflight executed and mode selected (cli or rest)
  • API key configured (MODELLIX_API_KEY or CLI --api-key)
  • Model parameters verified against model doc from references/REFERENCE.md
  • Task submit returns task_id with success code
  • Polling handles pending, processing, success, failed
  • Retry behavior implemented for 429/500/503
  • Result URLs persisted before 24-hour expiration
  • REST fallback validated when CLI path is unavailable

平台分布

Codex

35.76%
按下载量换算97

Claude

31.66%
按下载量换算86

Cursor

20.63%
按下载量换算56

Gemini CLI

8.62%
按下载量换算23

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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