Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
日本語の概要は準備中です。原文の説明を表示しています。
Plans, drafts, and refines technical tutorials for developers. Use when writing step-by-step guides or getting-started walkthroughs backed by working code.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
A good technical tutorial has one goal: move a reader from not knowing how to do something to being able to do it. That requires working code, concrete steps, and honest acknowledgment of where things go wrong. This skill guides you through outlining, drafting, and verifying a tutorial that meets that standard.
scribe:doc-generator)scribe:slop-detector)Before writing a single line, answer these questions:
newcomer, practitioner,
expert, or a one-line persona. A tutorial defaults to
newcomer; if it does not, say what it assumes insteadWrite these answers down as a header block in the draft. If you cannot answer the "what will they accomplish" question in one sentence, the scope is too broad. If you cannot state the thesis in one sentence, the tutorial is not ready to draft.
The audience size and read frequency feed the reader-time
budget (see scribe:slop-detector module document-economy.md).
A tutorial that 500 developers will read once is a 40-hour
reader-budget asset. Spend the writing time accordingly.
The tier decides what the draft may contain. When the request
names no reader, ask, do not guess: scribe:slop-detector
module audience-targeting.md carries the tier table and the
Socratic set.
A newcomer tutorial keeps the one path that works, end to
end. Alternatives, internals, and the reasons the obvious
approach fails are expert material. They go to
docs/deep-dive/<topic>.md, linked from the lead in one line
that names who the deep dive is for. Never delete them to hit
the tier. A tutorial that pauses to compare three approaches
has stopped moving the reader from cannot to can.
Load: @modules/outline-structure.md
Produce a section-by-section outline before drafting prose. Each section entry must include a one-line description of what the reader does or learns in that section. See the outline module for the standard section order and length targets per section type.
Load: @modules/code-examples.md
Write the code before the prose. Each snippet must run against a real environment before it appears in the tutorial. Annotate only the non-obvious lines. See the code examples module for formatting and error-handling rules.
Prose exists to explain what the code does and why. Follow these rules:
Load: @modules/progressive-complexity.md
Start with the minimal working example. Introduce variations and edge cases only after the baseline works. See the progressive complexity module for the layering rules and pacing guidance.
After drafting, run:
Skill(scribe:slop-detector)
Fix all tier-1 findings before proceeding. Pay particular attention to:
scribe:slop-detector word lists)Verify the completed tutorial against this checklist:
Content:
Sentence-level:
Document-level (document-economy module):
Audience (audience-targeting module):
tech-tutorial:scope-defined - Audience, goal, and out-of-scope notedtech-tutorial:outline-approved - Section outline confirmedtech-tutorial:code-tested - All snippets verified against a real envtech-tutorial:prose-drafted - Walkthrough text writtentech-tutorial:slop-scanned - Slop detector passedtech-tutorial:quality-verified - Quality gate checklist clearedtech-tutorial:user-approved - Final approval receivedmodules/outline-structure.md for section order and length targetsmodules/code-examples.md for snippet formatting and annotation rulesmodules/progressive-complexity.md for pacing and layering guidance| Skill | When to Use |
|---|---|
| scribe:slop-detector | After drafting, before approval |
| scribe:doc-generator | For companion API reference sections |
| scribe:style-learner | To match an existing tutorial voice |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
日本語の概要は準備中です。原文の説明を表示しています。
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
日本語の概要は準備中です。原文の説明を表示しています。
Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
日本語の概要は準備中です。原文の説明を表示しています。
Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
日本語の概要は準備中です。原文の説明を表示しています。
Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
日本語の概要は準備中です。原文の説明を表示しています。