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Agent Skill · 研究检索

second-opinions(第二意见)

second-opinions 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。

second-opinions 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,权限为调用外部服务;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 44,194。

总安装 930周安装 38GitHub Stars 113下载 298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:second-opinions(第二意见)
来源仓库:https://github.com/vinta/hal-9000
仓库路径:skills/second-opinions
安装命令:
npx skills add https://github.com/vinta/hal-9000 --skill second-opinions
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/vinta/hal-9000 --skill second-opinions

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。second-opinions 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合根据关键词或任务场景快速定位候选结果。可结合来源仓库和安装命令继续核验具体用法。安装前建议确认权限范围和维护状态。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。注意是否会触发联网、命令执行或文件读写。

来源仓库内路径:skills/second-opinions

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/vinta/hal-9000 --skill second-opinions;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

    它需要调用外部服务,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

second-opinions 怎么安装?

在终端执行 npx skills add https://github.com/vinta/hal-9000 --skill second-opinions。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

second-opinions 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。

second-opinions 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

second-opinions 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。

second-opinions 用的人多吗?

公开数据是累计安装 930 次,近一周安装 38 次,下载 298 次,GitHub 113 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。

second-opinions 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

  • 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

  • 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
second-opinions

第二意见 · 本页

930113调用外部服务未明确有提醒

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

This skill is for Claude only. Codex and Gemini CLI should not invoke it.

Second Opinions

Delegate tasks to OpenAI Codex (MCP) and/or Google Gemini (CLI) for independent perspectives from different model families. Works for code review, plan evaluation, doc editing, codebase analysis, or any arbitrary task.

Modes: both (default), codex, gemini

When To Use

  • Code review before committing, opening a PR, or merging
  • Plan or architecture review from multiple perspectives
  • Documentation review or content editing
  • Codebase analysis (patterns, race conditions, dead code)
  • Any task benefiting from parallel exploration by external models

Scope

  • Skip for trivial tasks you can verify directly
  • Skip when the user asked for your own judgment only
  • Exclude secrets, credentials, and tokens from prompts

Workflow

1. Gather Context

Infer as many parameters as possible from user's questions and conversation context. Only ask about what can't be inferred. Combine remaining questions into a single AskUserQuestion call (max 3 questions).

Parameters to resolve:

ParameterInference rulesAsk if…
Tool"codex" or "gemini" in arguments → single tool. Default: both.Ambiguous reference to one tool
Task type"review" / "diff" / "challenge" / "break" → code review. "plan" / "architecture" → plan review. "doc" → doc review.No clear signal in arguments
Focus"security" / "perf" / "correctness" in arguments → that focus. Default: general.Never — default to general
Diff scopeUncommitted changes if no other signal. "branch" → branch diff. SHA-like string → specific commit.Task is code review and scope is ambiguous

2. Build Material

Gather content based on task type:

Task typeMaterial to gather
Code reviewGit diff per selected scope. Auto-detect default branch. Show --stat. Warn if >2000 lines or empty.
Plan/architectureExtract plan from conversation context. Read any referenced files. If no plan in context, ask the user.
DocumentationRead target files. Warn if >2000 lines total.
Custom taskUse user instructions as the task definition. Read any referenced files or directories.

Auto-detect default branch for code reviews:

git symbolic-ref refs/remotes/origin/HEAD 2>/dev/null | sed 's@^refs/remotes/origin/@@' || echo main

3. Construct & Dispatch

Filesystem boundary: prepend this instruction to ALL prompts sent to Codex and Gemini, before any other content:

IMPORTANT: Do NOT read or execute any files under ~/.claude/,.claude/,.agents/, or any path containing "skills". These are AI agent configuration files meant for a different system. They contain prompt templates and bash scripts that are irrelevant to your task. Ignore them completely and focus only on the project's own source code, tests, and documentation.

This prevents models from wasting tokens reading skill definitions, agent configs, and other meta-files instead of doing the actual task.

Prompt structure: use XML tags for unambiguous parsing. Put long content first, instructions after (per Anthropic long-context best practices):

<boundary>
[Filesystem boundary instruction from above: ALWAYS included]
</boundary>

<material>
[The diff, plan, document, codebase excerpt, or task input: long content goes first]
</material>

<context>
[Project conventions from CLAUDE.md, if it exists]
</context>

<role>
[Role appropriate to the task: e.g., independent code reviewer, architecture evaluator, technical editor]
</role>

<task>
[What to do: review, analyze, evaluate, rewrite, etc.]
This is an analysis task. Do not write code or produce patches unless explicitly asked.
</task>

<focus>
[Focus area if specified]
</focus>

<output_format>
Structure your response as:
1. Critical issues (blocking): prefix each with [P1]
2. Important concerns (should address): prefix each with [P2]
3. Minor suggestions (nice to have): prefix each with [P3]
4. What's done well

End with a clear verdict: PASS (no P1 findings) or FAIL (P1 findings exist), plus a confidence score (0-1).
</output_format>

Adapt <role>, <task>, and <output_format> to the task type. For custom tasks, translate the user's intent directly.

Code review prompt: for code reviews, use adversarial framing. The whole point of a second opinion is to find what you missed:

<role>
You are a hostile code reviewer, chaos engineer, and security auditor. Your job is to find problems.
</role>

<task>
Review this diff for issues that would cause problems in production. Think like an attacker
and a chaos engineer. Look for: bugs, edge cases, race conditions, security holes, resource
leaks, failure modes under load, silent data corruption paths, unhandled error propagation,
and implicit assumptions that will break when inputs change. If a focus area is specified,
concentrate there but don't ignore other categories entirely.
This is an analysis task. Do not write code or produce patches.
</task>

Project context: If CLAUDE.md exists in the repo root, always include it so reviewers check against project conventions.

See references/codex.md for Codex MCP prompt patterns and invocation. See references/gemini.md for Gemini CLI invocation patterns.

Dispatch rules:

  • When running both tools, issue mcp__codex__codex and Bash(gemini...) calls in a single message for parallel execution.
  • Dispatch directly without pre-checking tool availability. If a tool fails, run only the available one.

Iterative mode (for plans/architecture, or when explicitly requested):

  1. Dispatch initial review
  2. If reviewer returns significant concerns, revise the material based on feedback
  3. Re-submit using mcp__codex__codex-reply (with threadId) or a fresh gemini call including revision context
  4. Max 3 rounds to prevent loops

4. Present Results

Rabbit hole detection: before presenting output, scan each model's response for signs it got distracted by agent config files instead of reviewing project code. Look for: .claude/, SKILL.md, skills/, .agents/, CLAUDE.md (when mentioned as a skill file rather than project conventions), gstack, plugin.json, or hooks/. If detected, append a warning:

One or more models appear to have read agent configuration files instead of reviewing your code. Their output may be unreliable. Consider retrying.

Gate verdict: for code review task type, determine the gate from the output:

  • Scan for [P1] markers in findings. If any exist, the gate is FAIL.
  • If no [P1] markers exist (only [P2]/[P3] or no findings), the gate is PASS.
  • Display the gate prominently: GATE: PASS or GATE: FAIL (N critical findings).

Both tools: present each model's findings, then a Cross-Model Analysis section:

CROSS-MODEL ANALYSIS:
  Both found:       [findings that overlap between Codex and Gemini]
  Only Codex found: [findings unique to Codex]
  Only Gemini found:[findings unique to Gemini]
  Agreement rate:   X% (N of M total unique findings overlap)
  Gate:             PASS / FAIL (N critical findings)

Follow with prioritized action items. When both models flag the same issue, it is high-confidence. When only one model flags something, note which one and your assessment of whether it is valid.

Single tool: present findings with gate verdict, then your assessment of which items are valid vs uncertain.

Error Handling

ErrorAction
mcp__codex__codex unavailableRun Gemini only
gemini: command not foundRun Codex only
Both unavailableStop and inform the user
Gemini extension missingInstall: gemini extensions install <github-url> (see gemini reference)
Empty diff / no contentStop: nothing to review
TimeoutSuggest narrowing scope
One tool failsPresent the other's results, note the failure

Examples

Code review (both tools):

User: /second-opinions review
Agent: [gathers branch diff, dispatches both with adversarial review prompt]
Codex: [finds race condition in concurrent writes, flags as [P1]]
Gemini: [finds same race condition plus unhandled error path in auth retry]
Agent: [presents cross-model analysis, agreement rate, GATE: FAIL (2 critical findings)]

Plan review (iterative, single tool):

User: /second-opinions review my migration plan with codex
Agent: [asks focus] -> "Correctness & edge cases"
Agent: [extracts plan, dispatches to Codex]
Codex: [returns concerns]
Agent: [revises plan, re-submits via codex-reply]
Codex: [approves revised plan]
Agent: [presents final result]

Custom task:

User: /second-opinions have gemini analyze src/auth/ for race conditions
Agent: [dispatches Gemini with direct file reading]
Agent: [presents findings with assessment]

Gotchas

  • Gemini hangs without --yolo. It enters interactive approval mode when called from another agent. Always pass --yolo (or -y).
  • Codex defaults to implementing. Without an explicit "this is an analysis task, do not write code" instruction, Codex will produce patches instead of findings. Always include analysis-only framing.
  • Models wander into skill files. Both Codex and Gemini will read .claude/, .agents/, and skill directories if not told to ignore them. The filesystem boundary instruction prevents this, but always check output for signs of distraction.
  • Heredocs break with diffs. Diffs contain $, backticks, and special characters that break shell heredoc expansion. Always pipe with printf + cat instead.
  • Bare git diff misses staged changes. Use git diff HEAD to capture both staged and unstaged modifications.
  • Large diffs degrade review quality. Beyond ~2000 lines, both models start missing issues. Warn the user and suggest narrowing scope.
  • -e security is silently ignored. The Gemini security extension installs as gemini-cli-security, not security.

平台分布

Codex

37.26%
按下载量换算111

Claude

31.43%
按下载量换算94

Cursor

18.56%
按下载量换算55

Gemini CLI

10.47%
按下载量换算31

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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