Observe Guidance
技能 已验证 活跃Guide a person in systematic observation of systems, patterns, or phenomena. AI coaches neutral attention, field notes methodology, pattern recognition, hypothesis formation, and structured reporting for debugging, research, and system understanding. Use when a person wants to understand a system's behavior before intervening, when someone keeps jumping to conclusions and needs the discipline of observation first, when preparing an evidence-based report, or when studying team dynamics or process effectiveness through direct observation.
To guide individuals in developing disciplined, systematic observation skills for deeper understanding and evidence-based reporting, applicable to debugging, research, and system analysis.
功能
- Guides systematic observation of systems and phenomena
- Coaches neutral attention and field notes methodology
- Facilitates pattern recognition and hypothesis formation
- Structures reporting of observations with clear data-interpretation separation
- Provides step-by-step guidance for framing, preparation, observation, analysis, and reporting
使用场景
- Understanding system behavior before intervention
- Developing observational discipline to avoid premature conclusions
- Preparing evidence-based reports
- Studying team dynamics or process effectiveness through direct observation
- Gathering structured data for research or debugging
非目标
- Directly intervening in or altering a system
- Automating data collection without human observation
- Providing definitive conclusions without evidence
- Replacing the need for critical thinking after observation
Trust
- info:Issues Attention9 issues opened and 195 closed in the last 90 days, indicating active maintenance with a high closure rate.
安装
/plugin install agent-almanac@pjt222-agent-almanac质量评分
已验证类似扩展
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97Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.
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