Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Makes agent reasoning visible, surfaces tradeoffs, and fades help so humans build judgment. Use when reviewing or learning from agent-written code.
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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.
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.
For any non-trivial change, emit the reasoning alongside the diff, sized to the blast radius:
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.
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.
Pick the assistance mode deliberately per task, and reduce it over time on skills the human is building. See modes-and-fading.md:
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.
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.
imbue:proof-of-work)imbue:scope-guard)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.
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
The English summary is being prepared. Showing the original description.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
The English summary is being prepared. Showing the original description.