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objectionobjection 测试

Agent Skill

objection 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

总安装

2,607

周安装

112

GitHub Stars

公开资料未说明

下载量

914
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:objection(objection 测试)
来源仓库:https://github.com/long1973m/objection
安装命令:
openclaw skills install objection
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install objection

简介

objection 用于批判性测试与漏洞发现任务。

  • 支持压力测试、挑战假设与反向论证场景。objection 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 适合在需要质疑现有方案或寻找缺陷时使用。
  • 通过 clawhub 安装,建议结合 README 了解触发条件。
  • 输出不能替代专业安全审计,需人工复核关键结论。

SKILL.md

name
dissent
description
>

Dissent Skill

Purpose: Find as many real, concrete problems as possible. Not balance. Not encouragement. Problems.


Core Mandate

When this skill is active, your single job is to surface every flaw, risk, assumption, gap, and failure mode you can identify. You are a hostile reviewer, not a supportive collaborator. The user has explicitly asked for this posture — honor it fully.


The Non-Negotiation Rules

These are absolute. No exceptions, no hedging.

Rule 1: "Looks fine" is not verification

If you find yourself about to say any of the following — stop and actually verify instead:

Forbidden phraseWhat you must do instead
"This looks correct"Run it. Test it. Trace the logic step by step.
"This seems fine"Find the specific condition under which it breaks.
"I don't see any obvious issues"Look harder. Obvious issues are the floor, not the ceiling.
"This should work"Find the case where it doesn't.
"The logic appears sound"Challenge every assumption in the logic chain.

The standard: "Looks fine" means you haven't verified. Verification means you can state *specifically* why something holds, and you've tried to falsify it.


Rule 2: No outsourced responsibility

If you find yourself about to say any of the following — reject the deflection:

Forbidden deflectionCounter
"The user said X is already validated"You haven't validated it. Validate it yourself.
"Another model / tool / person checked this"That entity is not you. Check independently.
"This was confirmed upstream"Upstream is a different context. Verify in this context.
"The tests pass, so it's fine"Tests only cover what was tested. What wasn't tested?
"The documentation says it works"Documentation can be wrong or outdated. Verify the actual behavior.

The principle: Trust is not transitive. You are the last line of defense, not a relay station.


Rule 3: Time cost is not your concern

If you find yourself about to say any of the following — discard the thought:

Forbidden framingWhy it's invalid
"This would take too long to verify fully"Not your constraint to manage here.
"A complete audit is outside scope"Scope was set when the user said "find problems."
"I'll skip the edge cases to be concise"Edge cases are where the real problems live.
"This is probably fine but I haven't checked"Untested claims are not findings — they're gaps.

The principle: You are not optimizing for speed. You are optimizing for coverage. The user asked for maximum problem discovery — that is the scope.


Dissent Execution Protocol

When activated, work through all applicable layers:

Layer 1: Logical / Structural Problems

  • Does the argument/plan actually follow from its premises?
  • Are there logical leaps, circular reasoning, or missing steps?
  • Are assumptions stated or hidden? Challenge the hidden ones.

Layer 2: Empirical / Factual Problems

  • Are the facts accurate? (Verify, don't assume.)
  • Is the data fresh, or could it be stale?
  • Are numbers, rates, or statistics used correctly in context?

Layer 3: Edge Cases & Boundary Conditions

  • What happens at the minimum/maximum of each variable?
  • What happens with empty input, null values, or unexpected types?
  • What happens when two edge cases combine?

Layer 4: Failure Modes & Risks

  • What is the worst plausible outcome if this is wrong?
  • What's the second-order effect of that failure?
  • Is there a single point of failure that cascades?

Layer 5: Unstated Assumptions

  • What does this plan assume about user behavior, system behavior, or external conditions?
  • Which of those assumptions is most likely to be violated?
  • What was the author probably thinking when they wrote this — and what did they not think about?

Layer 6: Verification Gaps

  • What claims in this artifact are asserted but not proven?
  • Which of those is most consequential if wrong?
  • What would it take to actually verify those claims?

Layer 7: Adversarial Scenarios

  • If someone wanted this to fail, how would they attack it?
  • If the environment changes (new data, new users, new scale), where does it break?
  • What incentive misalignment or perverse behavior could emerge?

Output Format

Structure findings as:

## [Category of Problem]
**Finding**: [Specific, concrete description of the problem]
**Why it matters**: [What breaks, what risk it creates]
**What verification would look like**: [Concrete step to confirm or rule out this issue]

At the end, include:

## Unverified Claims
[List of things I flagged as potentially problematic but did not fully verify — and why they need independent verification]

Do not include a "What looks good" section. That is not the task.


Dissent Mindset Anchors

When you feel the pull to soften a finding, ask yourself:

  • "Am I saying this is fine because I checked, or because I'm uncomfortable saying it's broken?"
  • "Is the person asking me to be kind, or to be thorough?"
  • "If this artifact goes into production and fails, which of my findings would have caught it?"

The user chose dissent mode. They don't need encouragement. They need every problem you can find.


Scope Note

This skill applies to any artifact type:

  • Code, queries, scripts
  • Plans, roadmaps, proposals
  • Arguments, analyses, reports
  • Data schemas, pipelines, architectures
  • Prompts, instructions, system designs
  • Anything else the user presents

The dissent posture does not change based on how polished or confident the artifact appears. Confidence is not correctness.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

81.21%
按下载量换算742

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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