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Assisted Mastery

athola/assisted-mastery

Makes agent reasoning visible, surfaces tradeoffs, and fades help so humans build judgment. Use when reviewing or learning from agent-written code.

5k tokens
context cost
the whole folder, loaded on every use
4
files
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 assisted-mastery

The instruction itself

11 sections, as written by the author

> A finished diff hides the thinking that produced it. The

> thinking is what the human needs to keep. Show the work, surface

> the choices, and hand back the parts worth struggling with.

Assisted Mastery

Overview

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)

Red Flags

| Thought | Reality |

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

| "The diff is obviously correct" | Correct to whom? State why, or you are guessing. |

| "Explaining slows me down" | On work you must own, the explanation is the deliverable. |

| "There was only one way to do it" | There is rarely one way. Name the alternatives you dismissed. |

| "I'll understand it later if it breaks" | Automation bias: you will trust it precisely when it is wrong. |

| "More agent help is always better" | Help that never fades builds dependence, not skill. |

  • 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).

How to use it

Copy the folder

Take athola/assisted-mastery 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.