Program · v1.0.0
Standard Trainer Job-First Roadmap
A role-aligned route from product boundary through calculation diagnosis, evaluation evidence, and interview transfer without a ten-day constraint.
- 01ai product managementOpen
Orient to the calculation-path coach
Define the active Standard Trainer direction, its learner outcome, and the authority boundary between deterministic tools and AI assistance.
- 02ai product managementOpen
Frame the learner failure with evidence
Translate calculation-process observations into a testable problem statement and interview plan.
- 03calculation trainerOpen
Model solution graphs and student steps
Represent the canonical Kp calculation path, structured student work, example tracing, and model tracing as separate inspectable objects.
- 04calculation trainerOpen
Freeze authority, policy, and provenance
Bind a curated problem, solution graph, marking policy, and review state before making correctness or ECF claims.
- 05agent engineeringOpen
Design deterministic validators
Specify reproducible validators for arithmetic, units, expressions, species, dependencies, significant figures, and mark allocation.
- 06calculation trainerOpen
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.
- 07agent engineeringOpen
Route tools with inspectable traces
Design constrained runtime routing across validators, diagnosis, marking, and hint paths without granting the model authority it does not own.
- 08calculation trainerOpen
Plan hints and reviewed follow-up practice
Turn bounded diagnoses into hint decisions, learner weakness updates, and reviewed next-problem selection.
- 09product delivery evalsOpen
Evaluate components and trajectories
Consolidate the vertical slice with component-level correctness checks and end-to-end trajectory evaluation before claiming readiness.
- 10product delivery evalsOpen
Run bounded learner evidence sessions
Observe real use, separate learning evidence from product and review evidence, and permit only a bounded evidence-backed change.
- 11interview preparationOpen
Turn delivery evidence into interview stories
Translate the calculation-trainer work into concise role-relevant stories, portfolio proof, and application evidence without overstating claims.