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Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.
This skill should be used for LLM-as-a-Judge evaluation techniques — direct scoring, pairwise comparison, rubric generation, reference-based grading, bias mitigation, calibration, and evaluator reliability measurement. Route deterministic evaluation checks and pipeline design to evaluation. Route agent harness design to harness-engineering.
This skill should be used for modeling agent cognitive states using formal Belief-Desire-Intention (BDI) ontology — transforming external RDF context into explicit mental states, designing deliberative reasoning pipelines, enabling explainable agent decisions, and implementing plan libraries. Route multi-agent coordination to multi-agent-patterns, memory architecture to memory-systems, and evaluation of agent reasoning to evaluation.
This skill should be used for designing and evaluating compression strategies for long-running agent sessions — hierarchical summarization, selective retention, compaction triggers, quality thresholds, and handoff summaries. Route foundational context concepts to context-fundamentals, failure diagnosis to context-degradation, and token-level efficiency tactics to context-optimization.
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. Route foundational conceptual work to context-fundamentals, token-efficiency tactics to context-optimization, and compression strategy design to context-compression.
This skill should be used to explain or reason about the foundational concepts of context engineering — what context is, the anatomy of a context window, how attention mechanics work, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. Use this for conceptual explanation, onboarding, and background reading. Route operational work to the specialized skills — debugging attention failures goes to context-degradation, token-efficiency work goes to context-optimization, conversation summarization goes to context-compression, and project-shape decisions go to project-development.
| name | skill-template |
| description | Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection. |
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.
Every skill body must make its ownership boundary explicit. The description and When to Activate section should say what the skill owns and which adjacent skills own nearby work. This prevents broad skills from stealing activation from narrower skills.
Describe specific situations, tasks, or contexts where this skill should be activated. Include both direct triggers (specific keywords or task types) and indirect signals (broader patterns that indicate skill relevance).
Write in third person. The description is injected into the system prompt, and inconsistent point-of-view can cause discovery problems.
Include a short "Do not activate" block for adjacent skills. Example:
Do not activate this skill for adjacent work owned by other skills:
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: the agent is already very smart. Only add context the agent 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.
Provide detailed explanation of the first major topic. Include specific techniques, patterns, or approaches. Use examples to illustrate concepts.
Provide detailed explanation of the second major topic.
List experience-derived failure modes and counterintuitive behaviors. Gotchas are the highest-signal content in any skill — they encode hard-won knowledge that prevents agents from repeating known mistakes.
Describe how this skill connects to other skills in the collection. Use explicit cross-references:
context-fundamentals provides the foundational mental models this skill builds on.context-optimization provides the operational tactics this skill's concepts inform.List any external references, papers, or documentation that informed this skill. Use the claim system for volatile facts:
claim-<skill-name>-<topic>: Description of the claim and its source.Move detailed reference material to the references/ directory:
references/topic-name.md — Deep-dive on specific topics that exceed the 500-line budget.