A coding agent that always returns the finished answer is maximally helpful to throughput and quietly corrosive to skill. The learning-science evidence is consistent: instructional support that helps a novice actively harms an expert (the expertise reversal effect, Kalyuga et al. 2003), so help must fade as competence grows rather than stay constant. Struggling with a problem before being shown the solution produces deeper understanding and transfer than being handed the answer (productive failure, Kapur 2008). And offloading the thinking to a tool measurably reduces what the human retains (the cognitive offloading and critical-thinking correlation of r = -0.75, Gerlich 2025; the Google effect, Sparrow et al. 2011).

This is the assistance dilemma: the same help that speeds the output erodes the judgment needed to verify it. The danger compounds with automation bias: AI-assisted developers in a controlled study wrote less secure code while believing it was more secure (Perry et al. 2023). You cannot verify what you do not understand, and a fluent diff signals competence it has not earned.

This skill does not slow down throughput work. It makes the reasoning a first-class deliverable alongside the code, surfaces the tradeoffs before a design is locked in, and lets the human choose how much of the work to keep for themselves.

The Three Practices

1. Make the reasoning visible

For any non-trivial change, emit the reasoning alongside the diff, sized to the blast radius:

Assumptions: what the change takes for granted about the codebase, inputs, and environment.
Alternatives considered: the two or three approaches that were viable, and why each was rejected.
Ramifications: what this design makes easy later, what it makes hard, and what would have to change to reverse it.

A high-blast-radius change with no stated reasoning is treated as incomplete, the same way an apprentice who "just did it" without showing their thinking would be sent back. This mirrors cognitive apprenticeship: the expert's invisible reasoning must be externalized before anyone can supervise or learn from it.

2. Surface tradeoffs before choosing

Do not present a single design as inevitable. State the decision, the options, and the axis each option wins on, then make the call and say why. Record consequential decisions in the tradeoff ledger so the reasoning is auditable later and the human can challenge it now. This is how novices were always trained into experts: by working through the positives, negatives, and ramifications of a decision, not by copying the conclusion.

3. Choose the mode, and fade it

Pick the assistance mode deliberately per task, and reduce it over time on skills the human is building. See modes-and-fading.md:

Explain mode: the agent narrates the reasoning and the human writes the load-bearing code. Builds judgment. Use on unfamiliar territory, high-stakes paths, and skills the human wants to own.
Produce mode: the agent writes, the human reviews. Maximizes throughput. Use on boilerplate, well-understood patterns, and reversible low-stakes work.

Default to produce mode for commodity work and explain mode where understanding is the point. As the human's competence on a given area grows, fade from produce toward explain to manual: permanent scaffolding is the failure mode, not the goal.

When To Use

An agent produced code the human will have to maintain, review, or be accountable for.
The change touches an unfamiliar subsystem or a high-stakes path (auth, migrations, money, concurrency).
A design decision has more than one defensible answer.
The human is trying to build skill in an area, not just ship.

Skip it for trivial, reversible, well-understood edits where the reasoning is self-evident: forcing a ledger entry on a typo fix is ceremony, and ceremony trains people to ignore the gate.

When NOT To Use

You only need the change shipped and verified (use imbue:proof-of-work)
Deciding whether to build it at all (use imbue:scope-guard)

Constraints

A diff is correct to someone. State why it is correct, or it is a guess that happened to pass.
On work the human must own, the explanation is the deliverable, so the time it takes is the work, not overhead.
Name the alternatives dismissed. There is rarely one way to do it, and a reader who sees only the winner cannot judge it.
Do not defer understanding to the moment it breaks. Automation bias means the output is trusted most exactly when it is wrong.
Help that never fades builds dependence. The fade is the point.

Related Skills

imbue:graduated-implementation: the other direction of the same axis. This skill fades scaffolding; that one ramps the ambition of the next increment as understanding is demonstrated.
imbue:proof-of-work: evidence that the code works; this skill adds evidence that the human understands it.
imbue:rigorous-reasoning: anti-sycophancy when evaluating the agent's stated tradeoffs rather than deferring to them.
imbue:karpathy-principles: think-first and simplicity, the pre-implementation companion to visible reasoning.
leyline:decision-journal: the durable home for tradeoff-ledger entries that outlive the session.
leyline:risk-classification: choosing the automation tier from the task's risk, the input to mode selection.

The measured evidence for blind-trust failure, the learning-science basis for fading, and the six workflow principles are preserved in research-basis.md.

Exit Criteria

Non-trivial changes ship with stated assumptions, alternatives considered, and ramifications, sized to blast radius.
At least one consequential design decision in the session is recorded with its rejected alternatives.
The assistance mode (explain or produce) was chosen deliberately and stated, not defaulted to "produce" silently.
On a skill the human is building, assistance is lower than it would have been at constant scaffolding (fading is applied).

assisted-mastery

Assisted Mastery

Overview