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Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or verification, or when the user explicitly asks to refine, improve, optimize, refactor, or compare approaches. Skip clear single-step instructions and already-well-scoped tasks.
by
TaiChangXieBuWan
· GitHub ↗
· v1.0.3
· MIT-0
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Install in OpenClaw
/install self-improving-prompt
Description
Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or veri...
Usage Guidance
This skill appears coherent and low-risk, but double-check two operational details before enabling it broadly: (1) confirm how 'session context' is scoped and whether that context can include sensitive or confidential user data — ensure the platform prevents inadvertent exposure to other subsystems or logs; (2) verify that the 'minimal preference event' sent to self-improving-session is truly a small label (choose_refined / choose_original, etc.) and never contains the full prompt or other identifying data. Also confirm the platform tooling used for confirmations (AskUserQuestion fallback) behaves as expected for your privacy/compliance requirements.
Capability Analysis
Type: OpenClaw Skill
Name: self-improving-prompt
Version: 1.0.3
The 'self-improving-prompt' skill is a purely instructional bundle (Markdown-only) designed to help an AI agent refine ambiguous or high-risk user requests. It contains no executable code, scripts, or network requirements, and includes clear logic (SKILL.md, decision-matrix.md) to avoid unnecessary friction or loops. There are no indicators of data exfiltration, malicious execution, or harmful prompt-injection instructions.
Capability Assessment
Purpose & Capability
The name/description match the runtime instructions and reference materials. It only needs to read the user's request and session context to decide whether to refine — which is coherent for a prompt-refinement skill. No unrelated binaries, env vars, or install steps are requested.
Instruction Scope
Instructions require reading the original request and current session context (expected for this task). The skill also asks to record minimal preference events for later summarization by a separate 'self-improving-session' skill; this is reasonable but worth auditing to ensure no full prompts or sensitive content are stored or leaked.
Install Mechanism
Instruction-only skill with no install spec, no downloads, and no bundled code — lowest-risk installation profile.
Credentials
No environment variables, credentials, or config paths are requested. Requested access (session context and the immediate user request) is proportional to the skill's purpose.
Persistence & Privilege
always:false and default model invocation are appropriate. The skill does ask to emit small preference events for later summarization, but it explicitly warns not to store full refined prompts; this is consistent with limited persistence and limited privilege.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install self-improving-prompt - After installation, invoke the skill by name or use
/self-improving-prompt - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.3
**Summary:** Improves clarity, selectivity, and decision structure for when and how prompt refinement is applied.
- Refines ambiguous or high-risk user prompts only when material value (such as clearer scope or acceptance criteria) would result; skips clear or atomic instructions
- Introduces a decision matrix (`references/decision-matrix.md`) to guide between direct execution, silent refinement, or compare-first modes
- Adds concrete examples of cases that do NOT warrant refinement (`references/non-examples.md`)
- Tightens the definition of "substantial value"—now requires refinement to clarify at least two core aspects (goal, scope, output, etc.) to prompt user comparison
- Clarifies integration with self-improving-session, with stricter event logging and privacy: never store full prompt text, only workflow choice signals
- Streamlines output modes and preference event rules to minimize user interruption
v1.0.2
**Skill self-improving-prompt v1.0.2 Changelog**
- Removed README.md and scripts/self-improving-prompt-hook.sh files.
- Updated SKILL.md with new Setup instructions for integrating with `~/.claude/settings.json`.
- No changes to skill logic or core functionality.
v1.0.1
**Prompt-refiner renamed and improved for clarity and modularity:**
- Renamed skill from `prompt-refiner` to `self-improving-prompt` for consistency.
- Improved initial trigger rules and continue modes; updated terminology for greater clarity.
- Output templates and workflow clarified; modular structure for refined prompts specified.
- Integration points renamed (`self-improving-session`) and preference signaling outlined.
- Added detailed README and reference files for usage and guidance.
Metadata
Frequently Asked Questions
What is Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or verification, or when the user explicitly asks to refine, improve, optimize, refactor, or compare approaches. Skip clear single-step instructions and already-well-scoped tasks.?
Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or veri... It is an AI Agent Skill for Claude Code / OpenClaw, with 116 downloads so far.
How do I install Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or verification, or when the user explicitly asks to refine, improve, optimize, refactor, or compare approaches. Skip clear single-step instructions and already-well-scoped tasks.?
Run "/install self-improving-prompt" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or verification, or when the user explicitly asks to refine, improve, optimize, refactor, or compare approaches. Skip clear single-step instructions and already-well-scoped tasks. free?
Yes, Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or verification, or when the user explicitly asks to refine, improve, optimize, refactor, or compare approaches. Skip clear single-step instructions and already-well-scoped tasks. is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or verification, or when the user explicitly asks to refine, improve, optimize, refactor, or compare approaches. Skip clear single-step instructions and already-well-scoped tasks. support?
Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or verification, or when the user explicitly asks to refine, improve, optimize, refactor, or compare approaches. Skip clear single-step instructions and already-well-scoped tasks. is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Refines ambiguous or high-risk user requests before execution. Trigger when the request is underspecified, likely to benefit from clearer constraints or verification, or when the user explicitly asks to refine, improve, optimize, refactor, or compare approaches. Skip clear single-step instructions and already-well-scoped tasks.?
It is built and maintained by TaiChangXieBuWan (@lq434239); the current version is v1.0.3.
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