/graduated-implementation

name: graduated-implementation
description: Ramps implementation ambition a notch only after the prior increment is understood. Use when building a feature you must understand, not just ship.
alwaysApply: false
category: workflow-methodology
tags:
- learning
- graduated-practice
- zone-of-proximal-development
- scope-ramp
- competence-gate
- automation-bias
dependencies: []
tools: []
usage_patterns:
- bounded-start
- competence-gated-ramp
- magenta-hand-fly-check
complexity: intermediate
estimated_tokens: 2300
modules:
- modules/advancement-gate.md
- modules/ramp-ledger.md
- modules/research-basis.md
role: library

> Start with the smallest slice you can fully understand. Earn the > next notch by proving you understood the last one. Ambition that > outruns understanding is how a fluent diff becomes an unverifiable > one.

Graduated Implementation

Overview

The sibling skill `imbue:assisted-mastery` fades scaffolding as competence grows. This skill ramps the other axis: the ambition of the next increment. They are the two directions of one move, the graduated practice that turned novices into experts long before agents existed. Not "ban the tool," but "couple the next challenge to demonstrated competence on the last one."

The learning sciences give the move a number. Wilson et al. (2019, *Nature Communications* 10:4646) derive the optimal training point for a learner at roughly 85% success: hard enough to learn from, not so hard that the signal is noise. The same band is what Vygotsky's zone of proximal development, Ericsson's edge of ability, and Csikszentmihalyi's flow channel all gesture at. Bloom's mastery learning (advance a unit at >=90% on a fresh check), Bayesian Knowledge Tracing (advance at p(mastery) >= 0.95), and competence-based curriculum learning (Platanios et al. 2019, only attempt tasks within the current competence) are the same rule at different resolutions.

The danger this guards against is specific. An agent that one-shots a large change is maximally helpful to throughput and quietly corrosive to verification: you cannot review what you did not watch get built, and automation bias means you will trust it precisely when it is wrong (Perry et al. 2023). Aviation named the endpoint "children of the magenta": ramp the operator's autonomy faster than their retained understanding and they can no longer hand-fly or override the automation when it misbehaves.

The Three Practices

1. Start at the smallest intentional increment

Do not design the whole system up front. Pick the smallest slice that is a real, end-to-end step and stop there. The default rung is about 40 added lines: a change a human can read and explain in one sitting. The bound is the point, not a nuisance: it keeps understanding in pace with output. The `guard_scope_ramp.py` hook makes this concrete by flagging an increment that jumps past the current rung.

2. Ramp a notch only on demonstrated understanding

The next increment may be more ambitious only after the prior one's understanding is demonstrated and recorded. The check is sized to blast radius, the [advancement gate](modules/advancement-gate.md):

• **Low-stakes increment**: ramp on an evidence gate. The prior

slice has green tests and a recorded tradeoff (what was chosen, what was rejected, why).

• **High-stakes increment** (auth, migrations, money, infra,

crypto): ramp only when the human explains the prior diff unaided. This is the magenta hand-fly check. If they cannot explain it, the rung drops rather than rises.

Recording the demonstration mints a ramp token (`touch .imbue/ramp-ok`), which the hook consumes to widen the rung one notch. You ramp by proving you understood the last slice, not by writing more. Each notch is appended to the [ramp ledger](modules/ramp-ledger.md) so a reviewer can later audit that the demonstration was real, not rubber-stamped.

3. Hold the 85% band in both directions

Advancing too fast is one failure; never advancing is the other.

• Below the band (the human is lost, the slice was too big): hold,

shrink the increment, re-scaffold. Do not ramp.

• In the band (clean demonstration, some genuine effort): ramp one

notch.

• Above the band (the human clears it trivially, repeatedly): ramp

faster. Drilling a mastered skill is over-practice, the boredom failure that gets spaced-repetition decks abandoned (Cen & Koedinger 2007).

When To Use

• An agent is building a feature across more than one increment and

the human will maintain or be accountable for it.

• The work touches an unfamiliar subsystem or a high-stakes path.
• The human is building skill in an area, not just shipping a

throwaway.

Skip it for a single bounded edit, a trivial reversible change, or generated and vendored code. Forcing a ramp ritual on a typo fix is ceremony, and ceremony trains people to ignore the gate.

When NOT To Use

• A trivial or mechanical fix, where ramping buys nothing
• You already understand the change and need evidence it works (use

`imbue:proof-of-work`)

Constraints

• Start with one slice. A whole thing built at once cannot be reviewed

by someone who did not watch it get built.

• Passing tests are completion, not understanding. A cheap signal

decouples from skill, as a Duolingo streak does from fluency.

• The producer does not grade its own readiness. Understanding is

demonstrated and recorded, not self-certified.

• A bigger increment is faster to write and slower to verify, and the

verification is the point.

• On work the human must own, the 85% band is the fast path to durable

skill, even when the rung feels slow.

Related Skills

• `imbue:assisted-mastery`: fades scaffolding as competence grows;

this skill ramps challenge. Two directions, one axis.

• `imbue:proof-of-work`: the evidence half of the low-stakes gate.
• `imbue:scope-guard`: bounds the branch; this bounds the

increment within it.

• `leyline:risk-classification`: the stakes tier that