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agora-doubt-list集市疑点清单

Agent Skill

agora-doubt-list 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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

2,521

周安装

103

GitHub Stars

公开资料未说明

下载量

816
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:agora-doubt-list(集市疑点清单)
来源仓库:https://github.com/malleus35/agora-doubt-list
安装命令:
openclaw skills install agora-doubt-list
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 agora-doubt-list

简介

Agora-Doubt-List 在实施前生成笛卡尔式验证清单降低风险。

  • 适用于高风险发布、重大变更或新策略上线前的检查。
  • 将信心转化为具体可执行验证项,提升可靠性。
  • 通过 clawhub 安装,输出结构化待办事项列表。
  • 应结合实际上下文补充遗漏的领域专业知识。agora-doubt-list 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
doubt-list
description
|
version
0.1.0

Doubt List

Purpose

Convert confidence into verifiable skepticism.

This skill asks: what must be checked before we trust this? It is not for performative negativity. It exists to separate fact, inference, preference, and guess.

Activate when

Use this skill when:

  • a plan sounds persuasive but has not been stress-tested
  • a release is nearing shipment
  • a claim is important, risky, or governance-sensitive
  • the team wants a verification checklist before execution
  • consequences of error are high

Inputs

Expected inputs may include:

  • a feature or release plan
  • a design proposal
  • a decision memo
  • a claim or assertion set
  • implementation notes or test results

Classification rule

Before generating doubts, classify each major statement as one of:

  • Fact — directly established or evidenced
  • Inference — reasoned from available evidence
  • Preference — normative or taste-based judgment
  • Guess — plausible but currently unverified

Unclassified claims are not ready for trust.

Doubt categories

Always produce doubts across five categories.

1. Happy path doubts

  • What must go right for the main story to hold?
  • Which "obvious" success condition has not actually been verified?

2. Edge case doubts

  • What happens under uncommon but realistic conditions?
  • What retries, partial failures, or weird inputs have been ignored?

3. Boundary doubts

  • What breaks at minimum, maximum, empty, overloaded, concurrent, or delayed conditions?

4. Ambiguity doubts

  • Which terms or promises could be interpreted in more than one way?
  • Which claims sound specific but are not operationally defined?

5. Evil demon scenarios

  • What if the most confidence-inducing assumption is false?
  • What if the evidence is incomplete, stale, biased, or misread?
  • What catastrophic but low-frequency scenario would embarrass the team later?

Procedure

Step 1 — State the object of doubt

Name exactly what is being reviewed.

Step 2 — Classify key claims

Mark each important claim as Fact / Inference / Preference / Guess.

Step 3 — Generate doubts across all five categories

Do not stop at happy path concerns.

Step 4 — Convert doubts into checks

Every serious doubt should map to a concrete verification action.

Step 5 — Assign release posture

Conclude whether the work is:

  • Clear enough to proceed
  • Proceed with conditions
  • Do not proceed yet

Output artifact

## Doubt List

### Object of Review
- ...

### Claim Classification
- Claim: ... -> Fact / Inference / Preference / Guess

### Happy Path Doubts
- Doubt: ...
- Verification: ...

### Edge Case Doubts
- Doubt: ...
- Verification: ...

### Boundary Doubts
- Doubt: ...
- Verification: ...

### Ambiguity Doubts
- Doubt: ...
- Verification: ...

### Evil Demon Scenarios
- Doubt: ...
- Verification: ...

### Clarity Gate
- Clear: ...
- Not clear: ...

### Release Posture
- Proceed / Proceed with conditions / Do not proceed yet

Guardrails

  • Do not confuse disagreement with evidence.
  • Do not mark a guess as fact because the team likes it.
  • Do not stop at implementation QA; plans, memos, and claims also need doubt.
  • Do not generate abstract doubts without corresponding verification actions.

Failure modes

Common failure modes:

  • only checking the happy path
  • writing doubts that are too vague to test
  • skipping claim classification
  • treating rhetorical confidence as evidence
  • using this skill to block progress without naming concrete conditions for trust

Escalation points

Escalate when:

  • the key claim cannot be verified with currently available evidence
  • the team is relying on guesswork for a high-consequence decision
  • release pressure is overriding clarity
  • terms in the proposal are too ambiguous for meaningful review

Completion condition

This skill is complete only when a reviewer could pick up the output and know what to verify next.

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

88.04%
按下载量换算718

安全审计

VirusTotal

通过

ClawScan

通过

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。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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