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Get Started Free →Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.
.claude/skills/loulanyue-search-first/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-13 | ✗→✓ | ▲ Improved | 166% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 59% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-22 | ✓→✗ | ▼ Worse | -22% | 0% |
| case-09 | ✓→✓ | = Same ✓ | 35% | 0% |
Systematizes the "search for existing solutions before implementing" workflow.
Use this skill when:
┌─────────────────────────────────────────────┐
│ 1. NEED ANALYSIS │
│ Define what functionality is needed │
│ Identify language/framework constraints │
├─────────────────────────────────────────────┤
│ 2. PARALLEL SEARCH (researcher agent) │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ npm / │ │ MCP / │ │ GitHub / │ │
│ │ PyPI │ │ Skills │ │ Web │ │
│ └──────────┘ └──────────┘ └──────────┘ │
├─────────────────────────────────────────────┤
│ 3. EVALUATE │
│ Score candidates (functionality, maint, │
│ community, docs, license, deps) │
├─────────────────────────────────────────────┤
│ 4. DECIDE │
│ ┌─────────┐ ┌──────────┐ ┌─────────┐ │
│ │ Adopt │ │ Extend │ │ Build │ │
│ │ as-is │ │ /Wrap │ │ Custom │ │
│ └─────────┘ └──────────┘ └─────────┘ │
├─────────────────────────────────────────────┤
│ 5. IMPLEMENT │
│ Install package / Configure MCP / │
│ Write minimal custom code │
└─────────────────────────────────────────────┘| Signal | Action | |--------|--------| | Exact match, well-maintained, MIT/Apache | Adopt — install and use directly | | Partial match, good foundation | Extend — install + write thin wrapper | | Multiple weak matches | Compose — combine 2-3 small packages | | Nothing suitable found | Build — write custom, but informed by research |
Before writing a utility or adding functionality, mentally run through:
rg through relevant modules/tests first~/.claude/settings.json and search~/.claude/skills/For non-trivial functionality, launch the researcher agent:
Task(subagent_type="general-purpose", prompt="
Research existing tools for: [DESCRIPTION]
Language/framework: [LANG]
Constraints: [ANY]
Search: npm/PyPI, MCP servers, Claude Code skills, GitHub
Return: Structured comparison with recommendation
")eslint, ruff, textlint, markdownlintprettier, black, gofmtjest, pytest, go testhusky, lint-staged, pre-commitunstructured, pdfplumber, mammothhttpx (Python), ky/got (Node)zod (TS), pydantic (Python)remark, unified, markdown-itsharp, imageminThe planner should invoke researcher before Phase 1 (Architecture Review):
The architect should consult researcher for:
Combine for progressive discovery:
Need: Check markdown files for broken links
Search: npm "markdown dead link checker"
Found: textlint-rule-no-dead-link (score: 9/10)
Action: ADOPT — npm install textlint-rule-no-dead-link
Result: Zero custom code, battle-tested solutionNeed: Resilient HTTP client with retries and timeout handling
Search: npm "http client retry", PyPI "httpx retry"
Found: got (Node) with retry plugin, httpx (Python) with built-in retry
Action: ADOPT — use got/httpx directly with retry config
Result: Zero custom code, production-proven librariesNeed: Validate project config files against a schema
Search: npm "config linter schema", "json schema validator cli"
Found: ajv-cli (score: 8/10)
Action: ADOPT + EXTEND — install ajv-cli, write project-specific schema
Result: 1 package + 1 schema file, no custom validation logic| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-09 | pass→pass | 12,301 | 6,604 | -46% | 1 | 1 | 0% | 1,893 | 2,551 | +35% | 0 | 0 | — |
case-01 | fail→fail | 13,177 | 7,734 | -41% | 1 | 1 | 0% | 2,051 | 2,692 | +31% | 0 | 0 | — |
case-02 | pass→pass | 18,140 | 10,627 | -41% | 1 | 1 | 0% | 3,140 | 3,266 | +4% | 0 | 0 | — |
case-03 | pass→pass | 20,929 | 9,540 | -54% | 1 | 1 | 0% | 3,933 | 3,151 | -20% | 0 | 0 | — |
case-04 | pass→pass | 13,680 | 11,498 | -16% | 1 | 1 | 0% | 2,179 | 3,416 | +57% | 0 | 0 | — |
case-05 | pass→pass | 10,882 | 6,940 | -36% | 1 | 1 | 0% | 1,845 | 2,632 | +43% | 0 | 0 | — |
case-06 | pass→pass | 16,269 | 12,652 | -22% | 1 | 1 | 0% | 2,711 | 3,513 | +30% | 0 | 0 | — |
case-07 | pass→pass | 12,290 | 9,703 | -21% | 1 | 1 | 0% | 2,043 | 2,996 | +47% | 0 | 0 | — |
case-08 | pass→pass | 14,813 | 11,305 | -24% | 1 | 1 | 0% | 2,382 | 3,400 | +43% | 0 | 0 | — |
case-10 | pass→pass | 12,423 | 8,176 | -34% | 1 | 1 | 0% | 2,027 | 2,802 | +38% | 0 | 0 | — |
case-11 | pass→pass | 10,878 | 8,494 | -22% | 1 | 1 | 0% | 1,825 | 2,901 | +59% | 0 | 0 | — |
case-12 | pass→pass | 11,311 | 6,794 | -40% | 1 | 1 | 0% | 1,793 | 2,581 | +44% | 0 | 0 | — |
case-13 | fail→pass | 4,559 | 2,110 | -54% | 1 | 1 | 0% | 682 | 1,815 | +166% | 0 | 0 | — |
case-14 | pass→pass | 9,263 | 5,290 | -43% | 1 | 1 | 0% | 1,532 | 2,342 | +53% | 0 | 0 | — |
case-20 | pass→pass | 16,817 | 16,665 | -1% | 1 | 1 | 0% | 2,896 | 4,465 | +54% | 0 | 0 | — |
case-15 | pass→pass | 4,152 | 2,525 | -39% | 1 | 1 | 0% | 558 | 1,836 | +229% | 0 | 0 | — |
case-16 | pass→pass | 11,176 | 4,374 | -61% | 1 | 1 | 0% | 1,704 | 2,231 | +31% | 0 | 0 | — |
case-17 | fail→pass | 12,403 | 10,812 | -13% | 1 | 1 | 0% | 2,013 | 3,202 | +59% | 0 | 0 | — |
case-18 | pass→pass | 16,458 | 11,321 | -31% | 1 | 1 | 0% | 2,542 | 3,293 | +30% | 0 | 0 | — |
case-19 | fail→pass | 12,205 | 9,419 | -23% | 1 | 1 | 0% | 1,947 | 2,882 | +48% | 0 | 0 | — |
case-21 | fail→fail | 2,451 | 4,947 | +102% | 1 | 1 | 0% | 378 | 1,733 | +358% | 0 | 0 | — |
case-22 | pass→fail | 12,509 | 3,038 | -76% | 1 | 1 | 0% | 2,324 | 1,823 | -22% | 0 | 0 | — |
case-23 | fail→fail | 13,178 | 15,309 | +16% | 1 | 1 | 0% | 1,714 | 2,869 | +67% | 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 22 counted toward the lift figure. The other 1 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 +9 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
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.