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Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt.
by
TaiChangXieBuWan
· GitHub ↗
· v1.0.0
· MIT-0
148
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Install in OpenClaw
/install prompt-refiner
Description
Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt bef...
Usage Guidance
This skill appears coherent and low-risk, but check three implementation details before installing: (1) Confirm the session-learner implementation truly stores only compact preference signals (e.g., "prefers popup"), never full prompt contents or originals. (2) Verify the AskUserQuestion popup and any UI flow do not send refined/original prompts to third-party endpoints or logs accessible by others. (3) Decide whether you want autonomous invocations allowed for this skill (default is allowed); if you prefer manual control, disable autonomous use or require explicit confirmation. If these items are satisfied, the skill is appropriate for refining user prompts.
Capability Analysis
Type: OpenClaw Skill
Name: prompt-refiner
Version: 1.0.0
The 'prompt-refiner' skill is a utility designed to improve vague user prompts by adding structure, goals, and constraints. It follows a transparent workflow that requires user confirmation via a popup before executing refined prompts and explicitly instructs the agent not to record full prompt text when sending preference signals to the 'session-learner' component (SKILL.md).
Capability Assessment
Purpose & Capability
The name/description match the runtime instructions: refine vague prompts, offer confirmation, and optionally emit a compact preference signal. The skill does not request unrelated environment variables, binaries, or install steps — nothing appears excessive for a prompt-refiner.
Instruction Scope
SKILL.md confines actions to extracting the user's original prompt and session context, producing a refined prompt, asking the user to confirm, and emitting a short learning signal for a separate session-learner. This is appropriate for the purpose, but it relies on platform integrations (AskUserQuestion popup and session-learner) that are not declared in the metadata. The instructions explicitly forbid recording full prompt text, which is good practice; verify that implementations follow that rule.
Install Mechanism
No install spec and no code files — instruction-only skills have the lowest install risk because nothing is downloaded or written to disk.
Credentials
The skill declares no required environment variables, credentials, or config paths. No hidden requests for secrets are present in the instructions or reference material.
Persistence & Privilege
always:false and normal autonomous invocation are appropriate. The only persistence hint is the interaction with a session-learner that accumulates preference patterns; this raises privacy/retention considerations (see guidance) but does not itself indicate privilege escalation or incoherence.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install prompt-refiner - After installation, invoke the skill by name or use
/prompt-refiner - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of prompt-refiner.
- Refines vague user prompts into clear, actionable, verifiable instructions before execution.
- Provides popup confirmation if the refinement adds significant value, letting the user choose between refined and original prompts.
- Supports auto-apply and optimize-only modes based on user instructions.
- Integrates with session-learner to learn user preferences without storing full prompt texts.
- Bypasses refinement for well-specified tasks or when instructed by the user.
Metadata
Frequently Asked Questions
What is Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt.?
Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt bef... It is an AI Agent Skill for Claude Code / OpenClaw, with 148 downloads so far.
How do I install Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt.?
Run "/install prompt-refiner" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt. free?
Yes, Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt. is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt. support?
Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt. is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt.?
It is built and maintained by TaiChangXieBuWan (@lq434239); the current version is v1.0.0.
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