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Graduated Implementation

athola/graduated-implementation

Ramps implementation ambition a notch only after the prior increment is understood. Use when building a feature you must understand, not just ship.

5k tokens
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the whole folder, loaded on every use
4
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instructions only
0
copies elsewhere
how many repositories repackaged it
324
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/athola/claude-night-market --skill graduated-implementation

The instruction itself

11 sections, as written by the author

> 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:

  • 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 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)

Red Flags

| Thought | Reality |

|---------|---------|

| "I'll just build the whole thing, then review" | You cannot review what you did not watch get built. Start with one slice. |

| "Tests pass, so it is understood" | Completion is not understanding. Duolingo streaks prove a cheap signal decouples from skill. |

| "I can self-certify I get it" | The producer may not grade its own readiness. Demonstrate it, record it. |

| "Bigger increments are faster" | Faster to write, slower to verify, and the verification is the point. |

| "The rung is slowing me down" | On work you must own, staying in the 85% band is the fast path to durable skill. |

  • 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 selects which

gate (evidence vs explanation) applies.

  • leyline:decision-journal: the durable home for the recorded

tradeoff that mints a ramp token.

The empirical basis for the 85% band, the failure modes, and the

cross-domain gate design is preserved in

research-basis.md.

Exit Criteria

  • [ ] The first increment of the feature was bounded to roughly the

start rung, not the whole design.

  • [ ] Each ramp to a more ambitious increment was preceded by a

recorded demonstration of the prior increment (a tradeoff

entry; for high-stakes paths, the human explaining the diff

unaided).

  • [ ] The stakes tier was used to choose the evidence gate vs the

explanation gate, not defaulted silently.

  • [ ] An increment that put the human below the understanding band

triggered a hold and a smaller next slice, not a ramp.

How to use it

Copy the folder

Take athola/graduated-implementation from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.