Convert content intuition into measurable rubric predictions and retrospectives.
npx skills add https://github.com/Jason5330/ai-self-eval --skill cheat-on-content
Please help me install this Agent Skill. Skill: cheat-on-content Source: https://github.com/Jason5330/ai-self-eval/tree/main Command: npx skills add https://github.com/Jason5330/ai-self-eval --skill cheat-on-content
Cheat-on-content provides a governance-friendly calibration loop that converts instinctive content decisions into measurable predictions across a dynamic rubric, enabling consistent improvement.
Initialize a new cheat-on-content project, describe your first draft, and start scoring and blind-predicting immediately.
High-intent search queries and answers about installing and using this skill.
To start content calibration, initialize a new project, describe your first draft, and immediately begin scoring and blind-predicting. You can evolve rubrics across cold-start and calibration phases using optional resources under scripts/ and references/.
Yes, you can use schema migration to safely upgrade rubrics in long-running projects. Schema migrations ensure safe, auditable evolution of cross-skill rubrics like daily-work and daily-learning without breaking existing data.
Blind-prediction in a content creation rubric retrospective is the process of forecasting content success before execution. Immutable predictions are recorded and later compared against actual outcomes to calibrate and improve future content decisions.
Yes, you need a root SKILL.md file to manage the content calibration lifecycle. It guides activation and lifecycle management, while optional assets and references support onboarding and parallel rubric execution.
The best way to maintain auditable parallel rubrics for content scoring is using a governance-friendly calibration loop. Immutable predictions and schema migrations ensure safe evolution across module-wide lifecycles for daily-work and AI coding contexts.
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