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Get Started Free →Create or rewrite exactly one LearnPrompt Starlight MDX tutorial to the repository's golden-sample standard, with bottom-of-page auditable sources instead of SourceCard, a research pack, evidence ledger, reproducible showcase, independent review, quality score, and successful site build. Use for one LearnPrompt article, tutorial, chapter, or MDX page; in batch goals, invoke once per topic in an isolated task.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 81% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 244% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 16% | 0% |
Produce one publishable tutorial and its auditable evidence. Do not batch several chapters inside one invocation.
Before editing, read:
research/golden-samples/README.mdresearch/golden-samples/quality-rubric.mdUse an Orange Book as a topic map when relevant. Verify current product behavior with primary sources and live tools.
Record the topic, target reader, one-sentence outcome, destination MDX path, research path, non-goals, source families, showcase question, and build command.
If a missing decision would materially change the article, ask at most three focused questions. In an unattended goal, make the smallest safe assumption and record it in brief.md.
git status --short.Create research/articles/<slug>/ unless the user supplies another path. Produce:
textbrief.md horizontal-research.md vertical-research.md evidence-ledger.md asset-ledger.md showcase/ review.md
Keep facts, inferences, and editorial synthesis separate. Put every time-sensitive claim behind a verification date.
Demonstrate the article's central decision with a real task, not a generic repository scan.
Save the exact input, environment, commands or code, actual output, failures, and reproduction limits. If comparing tools, hold the task and acceptance criteria constant, use isolated workspaces, compare workflows rather than model intelligence, and disclose configuration differences prominently.
Capture raw command output outside the repository first, using a temporary directory such as $TMPDIR. Inspect it, then commit only the smallest sanitized excerpt needed to prove the result. Remove local absolute paths, account identifiers, credentials, and session, thread, turn, item, or request identifiers.
Never stream a reviewer or agent's output into an untracked file inside the same worktree it is reviewing. Commands such as codex review --uncommitted may discover and reread their own growing log, producing a self-referential scan. Write the raw log outside the worktree, freeze it after the process exits, and only then create a sanitized Showcase artifact.
If a real run cannot be completed, set showcase_status: partial. Never invent a successful result.
Follow references/article-contract.md. Start from a real problem, explain the mechanism and trade-offs, show the evidence, state when not to use the method, and end with an exercise and sources.
Treat the depth floor as a release gate, not a target: a deep tutorial needs at least 5,000 body characters, at least 1,800 Chinese explanatory characters after fenced code is removed, and at least six H2 sections. Do not pad with repetition, oversized code dumps, source lists, or decorative markup. Navigation and index pages use a separate workflow and must not be forced through this deep-tutorial skill.
Include at least one local teaching image that explains a mechanism, decision, sequence, comparison, or verified result. Follow references/visual-assets.md: use a descriptive alt, an immediate 图注: line, a matching asset-ledger.md row, and source/license disclosure. A cover, logo, or decorative banner does not satisfy this requirement.
Never import or render SourceCard in a public tutorial. When rewriting a legacy page, remove the import and component only after its bottom source section accurately lists the material actually used and the applicable attribution or license.
Set showcase_status: partial while writing. Do not set quality_score until independent review passes.
Run the showcase checks and cd starlight && npm run build. Save the actual results.
Use a separate read-only model session or human reviewer. Record initial findings, fix every blocker, major, and minor finding, and run a follow-up review. If independent review is unavailable, leave the article partial and report the blocker.
Do not infer review success from process exit code alone. Require a final report that matches the requested scope and finding format, and archive only its sanitized evidence. In review.md, record reviewer identity, read-only isolation, an explicit statement that the reviewer used a separate session and the writer did not score or alter the verdict, zero blocker/major/minor counts, all six rubric scores, a PASS line whose score matches frontmatter, and an attestation that raw review output was captured outside the worktree. 最终状态:PASS must be the only final-status line and the last non-empty line.
After review passes with at least 85/100, no unresolved finding, and no release-gate failure:
showcase_status: verified.quality_score to the final score.最终状态:PASS in review.md.From the repository root:
bashnode "$CLAUDE_SKILL_DIR/scripts/validate-golden-mdx.mjs" \ --article <starlight-relative-mdx-path> \ --research <research-relative-directory>
Then rerun cd starlight && npm run build after the final metadata change.
For a multi-chapter goal, let the goal own the topic queue, isolated worktrees, concurrency, retries, and final summary. Give each worker one article card and invoke this skill once. Never let two workers edit the same MDX, research directory, sidebar block, or content schema simultaneously.
Do not commit, push, publish, or merge unless the user explicitly requests it.
Other measured skills in the registry, with their headline benchmark lift.