This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
日本語の概要は準備中です。原文の説明を表示しています。
Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Provide a clear, concise description of what this skill covers and when to use it. This description appears in skill discovery and should help agents (and humans) determine when this skill is relevant.
Important: Keep the total SKILL.md body under 500 lines for optimal performance. Move detailed reference material to separate files in the references/ directory.
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project-development.tool-design.Explain the fundamental concepts covered by this skill. These are the mental models, principles, or frameworks that the skill teaches.
Default assumption: Claude is already very smart. Only add context Claude does not already have. Challenge each piece of information:
Prefer behavior-changing mechanisms over general background. If a concept should be reusable across the corpus, add or update a record in researcher/mechanisms/registry.jsonl.
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Input: [describe input]
Output: [show expected output]
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Created: [Date] Last Updated: [Date] Author: [Author or Attribution] Version: [Version number]
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.
日本語の概要は準備中です。原文の説明を表示しています。
Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context.
日本語の概要は準備中です。原文の説明を表示しています。