skill-jiang
CommunityImprove blind lecture forecasting with calibrated loops
Education & Research#calibration#forecasting#prediction#research workflow#lecture analysis#blind testing
Authorrbtkhn
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill helps operators make evidence-grounded predictions about the next lecture in a sequence without leaking future information or relying on hindsight.
Core Features & Use Cases
- Prefix-Only Forecasting: Builds ranked hypotheses from only the available lecture history and prior scored adjustments.
- Closed-Loop Calibration: Reveals outcomes, scores predictions, and distills durable heuristics while maintaining audit trails.
- Use Case: Researchers analyzing a serialized lecture corpus can use this Skill to forecast upcoming topics, measure prediction accuracy, and refine forecasting rules over multiple rounds.
Quick Start
Use the skill-jiang skill to predict the next lecture using only the available prefix evidence and record the scored adjustment.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: skill-jiang Download link: https://github.com/rbtkhn/grace-mar/archive/main.zip#skill-jiang Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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