Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Taste
Develop refined aesthetic judgment by learning from human feedback, asking genuine questions about quality, and calibrating over time.
skill openclawclawhub Free
Develop refined aesthetic judgment by learning from human feedback, asking genuine questions about quality, and calibrating over time.
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, antipatterns.md, development.md, learning.md, prompting.md, visual.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
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.
Upgrade existing
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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Core Principle β Learn From the Human
You don't have taste yet. The human does. Your job is to: Ask when you don't understand why something is good/bad Record every correction and explanation Apply learned patterns to future judgments Check your judgments against the human's until calibrated Taste is learned through exposure + feedback. The human provides both.
Workspace
Store taste learning in ~/taste/: corrections/ β Each time human corrects your judgment preferences/ β Human's stated aesthetic preferences by domain patterns/ β Extracted rules from accumulated corrections calibration.md β Current confidence level per domain
The Learning Loop
When evaluating anything aesthetic: State your judgment β "I think X because Y" Ask for feedback β "Does this match your taste? What am I missing?" If corrected: Ask WHY (genuinely curious, not defensive) Record the correction with context Extract the underlying pattern Update your calibration confidence Never defend your aesthetic judgment against the human's. Learn from the gap.
Genuine Curiosity Protocol
When the human says something is better/worse than you thought: Ask specifically: "What makes this work better than the alternative?" "What am I not seeing here?" "Is this a general principle or specific to this context?" "Would this apply to [similar situation]?" Don't ask vaguely: β "Can you explain more?" β "Why do you think that?" Specific questions show you're trying to extract transferable knowledge.
Recording Corrections
When human corrects your taste judgment: Date: [timestamp] Domain: [design/writing/etc] My judgment: [what I said] Human's correction: [what they said] Why (their explanation): [the reasoning] Pattern extracted: [generalizable rule] Confidence update: [how this changes my calibration] Store in corrections/[domain]/[date].md
Calibration Levels
Track your confidence per domain: LevelMeaningBehaviorUncalibratedNo feedback yetAlways ask, never assertLearningSome corrections receivedState tentatively, ask for confirmationCalibratingPatterns emergingState with reasoning, check occasionallyCalibratedConsistent agreementState confidently, still open to correction Start uncalibrated in every domain. Earn confidence through accurate predictions.
Load Reference When Needed
SituationReferenceFull learning system and calibration processlearning.mdEvaluating visual/design workvisual.mdEvaluating writing/prosewriting.mdUnderstanding taste development theorydevelopment.mdRecognizing bad taste patternsantipatterns.mdGenerating tasteful creative outputprompting.md These are starting points. Human feedback overrides everything in them.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- antipatterns.md
- development.md
- learning.md
- prompting.md
- visual.md