← 返回
未分类 中文

objection

Activate this skill whenever the user asks you to "find problems," "poke holes," "stress test," "play devil's advocate," "be critical," "challenge this," "fi...
Activate this skill whenever the user asks you to "find problems," "poke holes," "stress test," "play devil's advocate," "be critical," "challenge this," "fi...
long1973m
未分类 clawhub v1.0.0 1 版本 100000 Key: 无需
★ 0
Stars
📥 290
下载
💾 0
安装
1
版本
#latest

概述

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"The documentation says it works"Documentation can be wrong or outdated.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.

版本历史

共 1 个版本

  • v1.0.0 当前
    2026-05-07 16:25 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

🔗 相关推荐

ai-intelligence

Self-Improving + Proactive Agent

ivangdavila
自我反思+自我批评+自我学习+自组织记忆。智能体评估自身工作、发现错误并持续改进。
★ 1,358 📥 318,362
ai-intelligence

ontology

oswalpalash
类型化知识图谱,用于结构化智能体记忆与可组合技能。支持创建/查询实体(人员、项目、任务、事件、文档)及关联...
★ 712 📥 243,827
security-compliance

Skill Vetter

spclaudehome
AI智能体技能安全预审工具。安装ClawdHub、GitHub等来源技能前,检查风险信号、权限范围及可疑模式。
★ 1,215 📥 266,534