Automate iterative retrospectives after each bug fix to update K/RW forecasts.
npx skills add https://github.com/Jason5330/ai-self-eval --skill cheat-retro
Please help me install this Agent Skill. Skill: cheat-retro Source: https://github.com/Jason5330/ai-self-eval/tree/main/skills/cheat-retro Command: npx skills add https://github.com/Jason5330/ai-self-eval --skill cheat-retro
Iterative retrospectives after each bug fix prevent uncalibrated forecasts and promote concrete learning from outcomes.
Run a retro immediately after every bug fix to log results and update K/RW.
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Automating retrospectives after a bug fix uses a phase-driven workflow to structure reflections and log insights. This process ensures concrete learning from outcomes and updates K/RW targets for future development cycles.
Calibrating forecasts during an iterative development cycle requires continuous updates to K/RW targets after each bug fix. Logging iterative analyses ensures predictions remain immutable while appending new retrospective learnings.
A phase-driven retro workflow structures bug-fix reflections across Phase 0 to Phase 5. This framework organizes iterative analyses and decisions to prevent uncalibrated forecasts and promote concrete learning from outcomes.
You can preserve historical predictions by maintaining immutable forecast sections. Retrospectives are appended to these sections, allowing you to log iterative analyses and update learnings without altering original K/RW forecasts.
You need to run a retro immediately after every bug fix to calibrate K/RW targets. This rapid feedback loop updates predictions and automatically logs insights for future iterative development cycles.
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