A structured framework to convert technical confidence into a Cartesian verification artifact before trusting any plan or implementation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agora-doubt-list
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agora-doubt-list using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Doubt List?
The Doubt List skill is a rigorous analytical tool designed for the Openclaw Skills ecosystem. It enables AI agents and developers to generate a Cartesian verification artifact, ensuring that plans, claims, and releases are not trusted until they are systematically doubted. Instead of relying on rhetorical confidence, this skill forces a clear separation between facts, inferences, preferences, and guesses.
By incorporating this skill into your workflow, you create a culture of verifiable skepticism. It provides a standard method to identify what must be checked before a high-stakes decision is finalized. When using Openclaw Skills like the Doubt List, you ensure that every "obvious" success condition is actually backed by evidence, significantly reducing the risk of catastrophic failure in production environments.
Doubt List Use Cases
- Stress-testing a persuasive feature or release plan that has not yet been audited.
- Validating risky or governance-sensitive claims within a decision memo.
- Generating a mandatory verification checklist for high-consequence shipments.
- Auditing implementation notes and test results to identify unverified assumptions.
How Doubt List Works
- Identify the Object: Define exactly what plan, claim, or assertion set is being reviewed.
- Classify Claims: Break down major statements into specific categories: Fact, Inference, Preference, or Guess.
- Generate Doubt Categories: Analyze the object across five dimensions: Happy Path, Edge Case, Boundary, Ambiguity, and Evil Demon scenarios.
- Map to Verifications: Convert every identified doubt into a concrete, actionable verification step.
- Post-Review Posture: Assign a final release status, determining if the project should proceed, proceed with conditions, or stop.
Doubt List Setup
To deploy the Doubt List within your agent's environment, ensure the markdown definition is accessible in your skill library. Openclaw Skills are typically invoked via a system prompt or a skill-loading utility.
# Example of integrating the skill into your project directory
mkdir -p .agent/skills
curl -o .agent/skills/doubt-list.md https://raw.githubusercontent.com/openclaw/skills/main/doubt-list.md
Doubt List Data Schema & Taxonomy
The Doubt List skill organizes data into a structured hierarchy to ensure no assumption goes unchecked. The following taxonomy is applied to all generated artifacts:
| Section | Description |
|---|---|
| Object of Review | The specific entity (code, plan, or claim) being analyzed. |
| Claim Classification | A mapping of claims to Fact, Inference, Preference, or Guess. |
| Doubt Categories | Five distinct sections covering Happy Path to Evil Demon scenarios. |
| Verification Actions | Concrete tasks mapped 1:1 with identified doubts. |
| Release Posture | The final governance decision (Proceed/Conditional/Stop). |
Doubt List Advanced Features
- Cartesian Classification Rule: Automatically flags unclassified claims as "not ready for trust" to prevent premature execution.
- Multi-Category Stress Testing: Goes beyond simple QA to include Boundary and Ambiguity doubts, ensuring semantic clarity in technical promises.
- Evil Demon Scenario Modeling: A specialized failure-mode analysis that assumes your most confidence-inducing assumption is false.
- Clarity Gate Integration: Acts as a mandatory governance hook for CI/CD pipelines using Openclaw Skills.
SKILL.md
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