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/tome

@35ffd55
by shingo imotasimota/agent-skills85 stars
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Converting technical knowledge into durable learning documents and publishable articles. Use for diff-based teaching, decision records, onboarding, note/Zenn/Qiita/dev.to posts, article series, retrospectives, and cross-platform repurposing.

Use this Skill: https://skilld.dev/gh/simota/agent-skills/tome

This session only. Nothing lands on disk.

referenceworked-example.md

≈1.4k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Worked Example Reference

Purpose: Author step-by-step problem-and-solution documents that make expert reasoning visible. Based on Sweller's Cognitive Load Theory (1988) — worked examples reduce extraneous load by replacing means-ends search with studied solutions. Includes faded-guidance progression so learners transition from studying to solving.

Scope Boundary

  • tome worked (this command): cognitive-scaffolded problem→reasoning→solution document. Annotates expert thinking and common mistakes; supports faded-guidance sequences.
  • tome learn (default): standard 5W1H+WhyNot learning doc derived from a real diff. Worked examples may be hypothetical or canonical.
  • tome diff: turns a specific diff into teaching material. Worked example is method-agnostic to source.
  • tome onboard: beginner-depth comprehensive intro. Worked examples are narrower — one problem, one expert mind.
  • tome record: ADR/decision record. Worked example teaches problem-solving steps, not architectural commitments.
  • vs Scribe (elsewhere): Scribe authors specs/PRDs/HLDs — formal documents. Worked example is pedagogical.
  • vs Quill (elsewhere): Quill writes inline JSDoc/TSDoc. Worked example is a standalone study document.
  • vs Stage (elsewhere): Stage produces slide decks for presentation. Worked example is for solo study.

Workflow

SELECT     →  pick a problem with one canonical expert solution
           →  confirm worth scaffolding (high-stakes, frequently confused, foundational)

DECOMPOSE  →  break solution into 4-9 numbered steps
           →  each step is one cognitive operation, not one keystroke

ANNOTATE   →  for each step add (a) expert reasoning, (b) common mistake, (c) why-this-works
           →  reasoning answers "what is the expert noticing here?"

SCAFFOLD   →  if a sequence: design fading guidance (full → partial → solo)
           →  faded variants share scaffolding template, drop annotations stepwise

VALIDATE   →  read as a novice — does each step land before the next demands it?
           →  hand off to Scribe if rule-set extraction or to Quill if inline-doc embedding

Structure Table

Section Purpose Required
Problem statement Concrete, bounded, one-paragraph Yes
Prerequisites Vocabulary, prior steps, assumed familiarity Yes
Expert quick-take 2-3 sentences: how an expert frames this Yes
Step-by-step solution Numbered ops with reasoning column Yes
Common mistakes Annotated near the step where each typically occurs Yes
Why this works Sidebar explaining the underlying principle Yes
Variations Edge cases / alt-approach pointers Optional
Faded next Pointer to the next, less-scaffolded variant Optional (sequences only)

Faded-Guidance Progression

Stage Worked steps Reasoning shown Common-mistake annotations Learner does
Full worked All All All Read and recreate
Partial-1 First half First half First half Complete remainder
Partial-2 First step only First step only None Drive remainder
Solo problem Statement only None None Solve from scratch

Span the four stages across 3-5 problems of equivalent type. Drop one scaffold per stage; do not skip stages.

Annotation Conventions

Annotation Marker Purpose
Expert reasoning # Expert: ... What the expert is noticing or recalling
Common mistake # Trap: ... A frequent novice error and why it fails
Why this works # Principle: ... Underlying rule or invariant
Inference [Inference: evidence] Anything not directly verifiable

Keep annotations adjacent to the step they describe. Do not collect them at the end — co-location is the entire pedagogical lever.

Anti-Patterns

  • Solution dump without reasoning column — strips the worked example of its teaching value, leaves only an answer key.
  • Steps that bundle multiple cognitive operations — overloads working memory exactly where the example was meant to relieve it.
  • Common mistakes listed in a footer, not co-located — readers pattern-match the trap only after they have already fallen in.
  • Faded sequence that skips the partial-guidance stages — jumping from full-worked to solo recreates the cognitive load wall.
  • Toy problems with no transfer — examples must reflect realistic structure, not "Hello World" stand-ins, or learners cannot generalize.
  • Missing prerequisites — assuming vocabulary the audience does not have makes expert reasoning unparseable.
  • Expert reasoning written as motivational commentary ("just trust the process") — must be falsifiable, mechanism-level explanations.
  • One worked example for a multi-method topic — single examples teach a single path; learners overfit. Pair at least two contrasting examples.
  • Final reveal hiding the answer — worked examples are not puzzles; visible solutions are the point.

Handoff

  • To Scribe: when expert reasoning patterns generalize into a rule set worth promoting to spec or guideline.
  • To Quill: when a worked-example annotation should travel into the codebase as inline JSDoc or comment near the actual implementation.
  • To Stage: when the worked example will be presented live — Stage adapts pacing, splits steps across slides, and adds speaker notes.
  • To Saga: when the problem is best framed as a customer story before becoming a worked example.
  • To Tome kata: when the worked example should graduate into a deliberate-practice exercise (remove solution, keep constraints).

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub13d

    The 'tome' skill is a technical documentation and article authoring tool designed to convert git changes and raw source material into structured learning assets. It incorporates robust safety standards, such as a strict 'Documents only' policy and mandatory masking of sensitive information like credentials and client details. While the skill processes external data, it includes explicit mechanisms to prevent the execution of untrusted instructions, ensuring a secure environment for technical writing.

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    Score: 93/100 · 2 sections analyzed

Signed by skilld at 35ffd55. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 days ago.

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