Generate immutable blind-prediction logs with YAML headers and hook-based locking.
npx skills add https://github.com/Jason5330/ai-self-eval --skill cheat-predict
Please help me install this Agent Skill. Skill: cheat-predict Source: https://github.com/Jason5330/ai-self-eval/tree/main/skills/cheat-predict Command: npx skills add https://github.com/Jason5330/ai-self-eval --skill cheat-predict
Writing task-specific predictions that are immutable once created, enabling reliable retrospectives and preventing post-hoc changes during calibration loops.
Provide a target script path and rubric notes to initiate an immutable blind-prediction cycle; the system will generate a draft for review and then finalize upon approval.
High-intent search queries and answers about installing and using this skill.
Immutable blind predictions are generated from a target script path and rubric notes, creating a draft log that is locked via a hook after submission to prevent post-hoc changes during calibration loops.
A blind-scoring sub-agent evaluates the prediction draft objectively and integrates with a user review to finalize and record the prediction, ensuring correctness through arbitration across phase-driven workflow steps.
Auditability is enforced by applying strict frontmatter constraints, including a YAML header with script path, script hash, calibration state, and a comprehensive BlindScore header for reliable retrospectives.
Yes, immutable blind-prediction logging applies to coding sessions, learning topics, and mission briefs, generating task-specific predictions through a phase-driven workflow from Phase 0.5 through Phase 7.
Predictions need to be locked after submission to prevent post-hoc changes, enabling reliable retrospectives and maintaining strict auditability during calibration loops.
The best way to finalize and record blind predictions is through a phase-driven workflow that integrates user reviews to produce final predictions and associated artifacts for archival.
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