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Execution

Turn models into repository evidence.

These labs and projects connect market or interview signals to a concrete learner outcome and bounded output.

project · 105 min

Frame the learner failure with evidence

Translate calculation-process observations into a testable problem statement and interview plan.

Outputsdocs/product/USER_STORY_INTERVIEW.md
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lab · 120 min

Design deterministic validators

Specify reproducible validators for arithmetic, units, expressions, species, dependencies, significant figures, and mark allocation.

Outputsdocs/architecture/TOOLS.md
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project · 135 min

Localise the first invalid step and bound ECF

Align structured work to the solution graph, identify the earliest invalid step, classify misconceptions, and apply bounded error-carried-forward rules.

Outputsdocs/evals/EVAL_PLAN.md
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project · 120 min

Plan hints and reviewed follow-up practice

Turn bounded diagnoses into hint decisions, learner weakness updates, and reviewed next-problem selection.

Outputsdocs/product/REVIEW_QUEUE_SPEC.md
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lab · 120 min

Evaluate components and trajectories

Consolidate the vertical slice with component-level correctness checks and end-to-end trajectory evaluation before claiming readiness.

Outputsdocs/evals/EVAL_PLAN.mddocs/evals/EVAL_REPORT_TEMPLATE.md
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project · 150 min

Run bounded learner evidence sessions

Observe real use, separate learning evidence from product and review evidence, and permit only a bounded evidence-backed change.

Outputsdocs/implementation/FAILURE_ANALYSIS.md
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