Diagnoses whether detail mining, signal detection, or structured observation is needed.
npx skills add https://github.com/human-avatar/skills-for-humanity --skill sensory
Please help me install this Agent Skill. Skill: sensory Source: https://github.com/human-avatar/skills-for-humanity/tree/main/skills/sensory Command: npx skills add https://github.com/human-avatar/skills-for-humanity --skill sensory
Sensory helps you observe a situation precisely when you suspect important details are being missed, you have too much information to parse, or you’re interpreting before you’ve fully looked.
Use case examples: diagnosing why a conversation seems to be going off track, auditing a messy situation to identify what actually matters, or reviewing evidence when you’re overwhelmed or rushing to interpret.
Use the sensory skill by telling the assistant: "sensory: Here’s what’s happening—help me separate observation from interpretation and identify what details matter most."
High-intent search queries and answers about installing and using this skill.
To separate observation from interpretation, structured observation builds a two-layer record that logs raw details first, preventing premature meaning-making. This method ensures concrete specifics are captured before any conclusions are drawn.
Signal detection is the best way to manage information overload, classifying data into strong signal, weak signal, and noise. This classification helps direct your attention toward actionable evidence and discards irrelevant data.
You compile a detail inventory through detail mining, which extracts dense, observable specifics such as exact words, behaviors, and outputs. This prevents rushed analysis by forcing a comprehensive review of evidence first.
You need structured observation when you suspect important details are being missed or you are rushing to interpret meaning prematurely. It diagnoses whether detail mining or signal detection is required to audit the conversation accurately.
Yes, this approach works for reviewing ambiguous evidence by diagnosing whether your investigation requires detail mining, signal detection, or structured observation. It produces a concrete inventory or signal classification to clarify the evidence.
A limitation of signal detection for attention management is that it requires a routing decision to determine whether a signal/noise classification or a detailed inventory is most appropriate. Without proper routing, it may not resolve highly ambiguous evidence effectively.
zjykzkIngest raw text data into an Obsidian wiki as searchable knowledge.
heymishyRecord a metric signal and evidence in pipeline-state.json.
bruinalphainvestmentExplore codebases and create OpenSpec artifacts for change proposals.
jshsakuraTriage performance metrics to distinguish regressions from noise and rank owner areas.
