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Get Started Free →Find the right doc before reading code. Use when asked "where is X documented", "is there a spec/ADR for X", "what's the roadmap", "open bugs", "read the docs about X", or "share the docs".
.claude/skills/hashgraph-online-router/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-07 | ✓→✓ | = Same ✓ | 24% | 0% |
| case-08 | ✓→✓ | = Same ✓ | 55% | 0% |
Project-agnostic. No hardcoded tree — discover, then route. Works in any repo.
Run a cheap scan to learn what this project has. Cache the result mentally for the session.
bash# docs roots + entry points ls README* CONTRIBUTING* 2>/dev/null fd -t d -d 2 -i 'docs?|spec|adr|decisions|wiki|reference' 2>/dev/null \ || find . -maxdepth 3 -type d \( -iname 'docs' -o -iname 'spec*' -o -iname 'adr' -o -iname 'decisions' \) -not -path '*/node_modules/*' # the markdown tree under the main docs dir (swap <DOCS> for what you found) fd -e md . <DOCS> 2>/dev/null | head -80 || find <DOCS> -name '*.md' | head -80
If a hand-maintained index exists (<DOCS>/README.md, SUMMARY.md, mkdocs.yml, docusaurus.config.*), read that — it's the authoritative map. Don't rebuild what the maintainer already wrote.
Match the question's shape to a folder by its conventional name, then Read that one doc. Don't bulk-read the tree.
| Question shape | Look in (by conventional name) | |----------------|-------------------------------| | "What does feature X do?" (user-facing) | product-spec/, docs/features/, top-level README | | "How is X implemented / data flow / API contract?" | specs/, docs/architecture/, design/ | | "Why did we choose X?" | decisions/, adr/, docs/adr/ (ADR files) | | "How do I do X / convention for X?" | references/, docs/guides/, CONTRIBUTING.md | | "What's planned / status of X?" | plans/, roadmap/, docs/roadmap/, project board | | "Known issues / open bugs?" | reviews/bugs/, ISSUES.md, GitHub issues | | "What shipped recently?" | CHANGELOG.md, changelog/, releases |
If names don't match these, fall back to the discovered index from step 1.
Three layers — do whichever the user asked for.
Project skills live at .claude/skills/<name>/SKILL.md and are checked into git — anyone who pulls + uses Claude Code gets them automatically, zero setup.
bashmkdir -p .claude/skills/router cp ~/.claude/skills/router/SKILL.md .claude/skills/router/SKILL.md # or author a project-tuned copy git add .claude/skills/router && git commit -m "docs: add docs router skill"
~/.claude/skills/) = only you, every project..claude/skills/ in repo) = whole team, this repo. Commit it to share.<DOCS>/README.md index with relative links to every doc — GitHub renders it; links are clickable in the web UI.README.md pointing at <DOCS>/.For a teammate who'll use Claude Code: create an ONBOARDING.md at repo root (point them at <DOCS>/ + the skills), then use the ShareOnboardingGuide tool to upload it and get a link they open in Claude Code. Generic and project-agnostic.
This skill is intentionally project-agnostic — it lives in ~/.claude/skills/ so it loads in every repo. To specialize it for one project, copy it into that repo's .claude/skills/ and replace step 1's discovery with the project's actual doc tree (like a hand-written router).
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 15,656 | 18,768 | +20% | 1 | 1 | 0% | 318 | 1,524 | +379% | 0 | 0 | — |
case-02 | fail→fail | 8,552 | 14,116 | +65% | 1 | 1 | 0% | 290 | 1,553 | +436% | 0 | 0 | — |
case-03 | fail→pass | 17,759 | 11,145 | -37% | 1 | 1 | 0% | 1,636 | 2,240 | +37% | 0 | 0 | — |
case-04 | fail→fail | 14,178 | 3,295 | -77% | 1 | 1 | 0% | 1,348 | 1,784 | +32% | 0 | 0 | — |
case-05 | fail→fail | 15,972 | 10,456 | -35% | 1 | 1 | 0% | 1,714 | 1,910 | +11% | 0 | 0 | — |
case-06 | fail→pass | 18,009 | 7,776 | -57% | 1 | 1 | 0% | 1,593 | 1,634 | +3% | 0 | 0 | — |
case-07 | pass→pass | 13,072 | 5,582 | -57% | 1 | 1 | 0% | 1,686 | 2,092 | +24% | 0 | 0 | — |
case-08 | pass→pass | 14,447 | 11,352 | -21% | 1 | 1 | 0% | 1,322 | 2,053 | +55% | 0 | 0 | — |
case-09 | pass→pass | 8,149 | 11,285 | +38% | 1 | 1 | 0% | 1,464 | 2,153 | +47% | 0 | 0 | — |
case-10 | pass→pass | 14,153 | 15,091 | +7% | 1 | 1 | 0% | 2,130 | 2,554 | +20% | 0 | 0 | — |
case-11 | fail→fail | 14,557 | 9,157 | -37% | 1 | 1 | 0% | 1,972 | 1,711 | -13% | 0 | 0 | — |
case-12 | fail→pass | 14,634 | 7,133 | -51% | 1 | 1 | 0% | 1,491 | 2,220 | +49% | 0 | 0 | — |
case-13 | pass→pass | 14,729 | 2,603 | -82% | 1 | 1 | 0% | 1,389 | 1,694 | +22% | 0 | 0 | — |
case-14 | fail→fail | 15,991 | 11,931 | -25% | 1 | 1 | 0% | 263 | 1,434 | +445% | 0 | 0 | — |
case-15 | fail→fail | 12,649 | 12,199 | -4% | 1 | 1 | 0% | 1,048 | 1,620 | +55% | 0 | 0 | — |
case-16 | pass→pass | 7,110 | 2,307 | -68% | 1 | 1 | 0% | 253 | 1,471 | +481% | 0 | 0 | — |
case-17 | fail→fail | 10,536 | 9,846 | -7% | 1 | 1 | 0% | 305 | 1,416 | +364% | 0 | 0 | — |
case-18 | pass→pass | 14,996 | 3,564 | -76% | 1 | 1 | 0% | 1,661 | 1,898 | +14% | 0 | 0 | — |
case-19 | pass→pass | 18,741 | 6,242 | -67% | 1 | 1 | 0% | 1,983 | 2,394 | +21% | 0 | 0 | — |
case-20 | pass→pass | 18,899 | 14,321 | -24% | 1 | 1 | 0% | 1,933 | 2,699 | +40% | 0 | 0 | — |
case-21 | pass→pass | 14,231 | 14,026 | -1% | 1 | 1 | 0% | 1,525 | 2,596 | +70% | 0 | 0 | — |
case-22 | pass→pass | 17,806 | 3,210 | -82% | 1 | 1 | 0% | 1,703 | 1,732 | +2% | 0 | 0 | — |
case-23 | fail→fail | 20,837 | 7,970 | -62% | 1 | 1 | 0% | 2,035 | 2,364 | +16% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 18 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +13 percentage points is the difference between those two pass rates over the 18 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.