backprop

1Carry a lesson from the output back into the sources that produced it, then leave those sources clean.
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3## Goal
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5A correction says the output was wrong; the fix belongs in what produced it. Backpropagation in a network measures the error at the output and sends it back through every layer that contributed, each adjusting by its share. `backprop` does the same for a body of work: `re0-memo` measures the error and names its class, `elon` questions the requirements that let it through, `ssotize` puts the lesson in the one source that owns it, and `re0`, `debloat` and `re0-order` leave that source reading as its clean current version. The scope you name bounds every pass, each pass runs only when its condition holds, and the order stays fixed.
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7## Workflow
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91. **Pin the scope.** Name the output the lesson came from (the correction, the failed cycle, the review finding) and the sources it implicates: the docs, rules, skills or code that produced it. If neither is named, propose the user's last correction and the files it concerns, and wait for a yes before editing; with no correction to propose, ask for the output and stop. Every later step stays inside this scope.
102. **Measure.** Call the Skill tool with "re0-memo" on the output. Its lessons, each a class with its evidence and the owner it names, are what the remaining steps carry back. When it finds no lesson, report that and stop.
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