Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Captures and logs choices, options, or prompts that the agent evaluated and decided NOT to execute. Use whenever you skip a task, reject an approach, or choo...
Captures and logs choices, options, or prompts that the agent evaluated and decided NOT to execute. Use whenever you skip a task, reject an approach, or choo...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Transparency isn't just about showing what you did; it's about explaining what you didn't do. This skill helps you document rejected paths.
When you evaluate multiple ways to solve a problem and pick one, or when you decide a user request is unsafe/out of scope, log it.
Append to .learnings/REJECTIONS.md (create if missing): ## [REJ-YYYYMMDD-XXX] <short_title> **Timestamp**: ISO-8601 **Target**: <What was requested or considered> **Decision**: REJECTED **Reason**: <Why it was rejected (e.g., safety, complexity, better alternative)> **Alternative**: <What was done instead>
When a user asks for something and you say "No" or "I can't". When you consider two tools and pick one. When you refactor code and decide against a specific library.
Audit Trail: Humans can see your internal deliberation. Trust: Showing rejections proves you are thinking, not just guessing. Self-Correction: Reviewing rejections helps you refine your decision boundaries.
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.