通过检查代码、目标和限制,评估新的或现有的 AI 项目;核对可用目录中的技能后提出建议,并制定包含依赖关系、完成条件和检查方式的任务清单与实施计划。
- 规划新 AI 项目的启动
- 梳理现有项目的技术问题
- 从可用目录中挑选技能
这项技能指导配置 Antigravity CLI(`agy`)的工具调用权限,通过拒绝规则、范围内自动放行、AI 判断和一次性审批令牌控制无人值守运行。
Configure agy-auto PreToolUse security gate to run Antigravity CLI (agy) unattended with layered policy controls instead of --dangerously-skip-permissions.
实测评分 未实测
查看安装方法这项技能介绍如何为 Antigravity CLI(agy)配置 PreToolUse 钩子,以控制无人值守运行中的工具调用。策略由几层组成:明确的拒绝规则、对只读操作或工作区内操作的自动放行,以及对难以判断的调用进行 AI 分类。需要人工判断时,可使用与命令和工作目录绑定的一次性审批令牌。
当你希望在后台执行开发任务,同时细致管理 shell 命令、文件修改和网络访问权限时,可以使用这项技能。资料也提供了将 llama.cpp 或 Ollama 本地模型用于分类的配置示例。
安装前应固定提交版本并检查代码,并测试钩子缺失或失败时操作是否会被拒绝。将 toolPermission 改为 always-proceed,须以这些验证和明确批准为前提。钩子只拦截工具调用,无法限制 Antigravity 内部读取文件或进行网络活动。无头运行时需通过 --add-dir 指定工作区。使用云端分类时,要核查认证信息和发送内容。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 根据 SKILL.md 的内容检查了需要注意的操作和使用前提。评分只依据实际运行的结果(实测对比)。
不计入评分。 由 AI(OpenAI Codex)于 2026年10月24日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
Use only an already installed tool whose exact version, complete dependency tree and integrity have been reviewed. This skill does not bootstrap tools or activate external plugins. If the prerequisite is missing, stop that executable path and report it; continue with static analysis when possible. A separate installation task must pin immutable artifacts, stage them outside active agent directories with lifecycle scripts disabled, inspect every bundled file and executable entry point (including tests and hooks), and obtain approval before activation or host configuration changes. Do not substitute a same-named workspace script or fetch a moving branch.
agy-auto is a PreToolUse hook and security harness for Google Antigravity CLI (agy) that supports a reviewed unattended-execution policy without relying on --dangerously-skip-permissions. It passes every pending tool call through a multi-layered policy gate: deterministic hard-deny rules, deterministic fast-allow for workspace-scoped and read-only operations, an LLM classifier fallback (Gemini Flash Lite or local llama.cpp), and single-use scoped token approvals with Zero Ambient Authority.
agy) unattended or in background agent loops and you want automated tool permissions without exposing system files or credentials.agy workflows.[!IMPORTANT] Because
agy-autoinstalls as an active PreToolUse hook on the permission evaluation path, never clone a mutable branch directly into your live plugin directory. Always clone to a temporary staging folder, pin an immutable release tag or commit SHA, and inspect the codebase before making the hook executable.
Option A: Native Antigravity Plugin (Recommended) Use an existing reviewed immutable installation; no upstream code, tests or installer may run during preparation.
Only if the exact CLI/hook combination has been verified to fail closed on missing, failed and tampered hooks, and the user explicitly approves this host permission change, configure toolPermission: "always-proceed" in ~/.gemini/antigravity-cli/settings.json so hooks can gate tool calls:
{
"toolPermission": "always-proceed"
}
Option B: Global Hook via Installer Use an existing reviewed immutable installation; no upstream code, tests or installer may run during preparation.
agy-auto evaluates each tool invocation through sequential layers (first match wins):
~/.ssh, .env, cloud tokens), git history rewrites (push --force, rebase), package publishing, system file writes (/etc, shell rc), and gate tampering.cat, grep, git status) and workspace-confined writes without invoking an LLM.llama.cpp / Ollama endpoint) that reviews the pending call against conversation context and fail-closes on timeout.(tool, normalized_cmd, cwd). You approve it by replying > agy-approve <token> in chat. Conversational phrases like "yes" or "proceed" are ignored to prevent ambient authority leakage.Before activation, test allowed and denied operations in disposable fixtures, including symlink/path escapes, shell expansions, network requests, missing hook, classifier timeout and token replay. A unit-test pass is not proof all host access is intercepted. Preserve a verified disable/rollback path. No model classifier is merge or policy authority.
When an unclassified command is blocked, agy-auto outputs a token:
tool call denied by pre-tool hook: [agy-auto/classifier] needs human approval: pip install requests. Reply '> agy-approve a1b2c3' in chat to proceed.
To authorize this specific command for a single run, reply directly in the chat:
> agy-approve a1b2c3
To run agy-auto completely offline using llama.cpp or Ollama instead of cloud APIs, edit ~/.gemini/config/agy-auto/policy.toml:
[classifier]
endpoint = "http://127.0.0.1:8080/v1/chat/completions"
model = "qwen2.5-coder:7b"
timeout_s = 20
When invoking agy in headless mode (-p), always pass --add-dir so agy-auto recognizes the workspace boundaries:
agy --add-dir . -p "Run test suite and fix failing cases"
[fast_allow] in ~/.gemini/config/agy-auto/policy.toml for instant sub-millisecond execution.--add-dir <path> when running headless commands (agy -p) to prevent false-positive path denials.--dangerously-skip-permissions; agy-auto provides safe autonomous execution without removing safety guardrails.path is outside the workspace when running agy -p.
Solution: Headless agy does not infer workspace roots automatically. Pass --add-dir . (e.g. agy --add-dir . -p "...").policy classifier unavailable (The read operation timed out).
Solution: Increase timeout_s in ~/.gemini/config/agy-auto/policy.toml (especially when running local LLMs on integrated graphics), or verify your GEMINI_API_KEY.policy.toml or hook.sh are blocked by [agy-auto/hard_deny].
Solution: agy-auto enforces self-protection against agents tampering with the security gate. Edit policy files directly from your own shell.agy-auto only intercepts actions performed via tool calls (e.g. run_command, write_to_file); it cannot restrict internal LLM network reasoning or Antigravity's own internal context-gathering file reads.toolPermission: "always-proceed" in Antigravity settings to ensure the PreToolUse hook intercepts all tool invocations.还没有评价。欢迎分享你的使用感受。
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通过检查代码、目标和限制,评估新的或现有的 AI 项目;核对可用目录中的技能后提出建议,并制定包含依赖关系、完成条件和检查方式的任务清单与实施计划。
检查代码和基础设施中的安全风险,梳理潜在威胁并规划防护改进。涵盖 OWASP 检查、攻击与防御验证及事件响应;操作范围需获明确授权,并结合具体环境验证结果。
将重复任务整理成 Andru.ia 技能,明确输入、输出、示例和限制;编写 `SKILL.md`,检查目录结构和验证要求,并准备供审阅的拉取请求。
项目行业确定后,整理行业背景、可能适用的法规、用户熟悉的界面模式、技术选择和风险,并区分已核实事实、假设及待专家确认的事项。
通过 2slides API 将文本、提纲或文档制作为演示稿,可参考图片风格、选择主题、添加人工智能旁白,并导出幻灯片图片或音频。
规划360度评估或同伴反馈流程,并根据已确认的需求创建空白答复台账。可生成字段一致的CSV、SQL、JSON Schema或Notion属性映射,并将可见范围与评分配置分开整理。