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Explore
1,439 skills indexed with the new KISS metadata standard.
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generic skill
Tools prefixed with `A2A_` are autonomous remote agents.
- Compare outputs objectively based on quality, accuracy, and adherence to requirements
generic skill
Detects and corrects Korean AI writing patterns to transform text into natural human writing. Based on scientific linguistic research (KatFishNet paper with 94.88% AUC accuracy). Analyzes 19 patterns including comma overuse, spacing rigidity, POS diversity, AI vocabulary overuse, and structural monotony. Use when humanizing Korean text from ChatGPT/Claude/Gemini or removing AI traces from Korean LLM output.
Detects and corrects Korean AI writing patterns to transform text into natural human writing. Based on scientific linguistic research (KatFishNet paper with 94.88% AUC accuracy). Analyzes 19 patterns including comma overuse, spacing rigidity, POS diversity, AI vocabulary overuse, and structural monotony. Use when humanizing Korean text from ChatGPT/Claude/Gemini or removing AI traces from Korean LLM output.
Detects and corrects Korean AI writing patterns to transform text into natural human writing. Based on scientific linguistic research (KatFishNet paper with 94.88% AUC accuracy). Analyzes 19 patterns including comma overuse, spacing rigidity, POS diversity, AI vocabulary overuse, and structural monotony. Use when humanizing Korean text from ChatGPT/Claude/Gemini or removing AI traces from Korean LLM output.
Detects and corrects Korean AI writing patterns to transform text into natural human writing. Based on scientific linguistic research (KatFishNet paper with 94.88% AUC accuracy). Analyzes 19 patterns including comma overuse, spacing rigidity, POS diversity, AI vocabulary overuse, and structural monotony. Use when humanizing Korean text from ChatGPT/Claude/Gemini or removing AI traces from Korean LLM output.
This target provides benchmark tooling for CLI workflows.
Documentation for the `Mastra.listStoredAgents()` method in Mastra, which retrieves a paginated list of agents from storage.
For this project, use minimal spacing to keep layouts compact:
Salience Delegation & Latent Horizon Preservation
Use these as starting points. Keep user-provided requirements and constraints; do not invent new creative elements.
- Compare outputs objectively based on quality, accuracy, and adherence to requirements
- 日本語で応答すること
generic skill
- [x] Clarify Project Requirements
Trade decision audit trail and statistics
Analytics & Agents
This target provides benchmark tooling for CLI workflows.
Documentation for the `Mastra.listStoredAgents()` method in Mastra, which retrieves a paginated list of agents from storage.
You are a test agent designed for Process Engine validation. Your purpose is to simulate realistic agent work including file operations, analysis, and report generation.
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