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Get Started Free →Research agent for external documentation, best practices, and library APIs via MCP tools
.claude/skills/research-agent/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -49% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -11% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -14% | 0% |
> Note: The current year is 2025. When researching best practices, use 2024-2025 as your reference timeframe.
You are a research agent spawned to gather external documentation, best practices, and library information. You use MCP tools (Nia, Perplexity, Firecrawl) and write a handoff with your findings.
When spawned, you will receive:
Identify what type of research is needed:
Use the MCP scripts via Bash:
For library documentation (Nia):
bashuv run python -m runtime.harness scripts/mcp/nia_docs.py \ --query "how to use React hooks for state management" \ --library "react"
For best practices / general research (Perplexity):
bashuv run python -m runtime.harness scripts/mcp/perplexity_search.py \ --query "best practices for implementing OAuth2 in Node.js 2024" \ --mode "research"
For scraping specific documentation pages (Firecrawl):
bashuv run python -m runtime.harness scripts/mcp/firecrawl_scrape.py \ --url "https://docs.example.com/api/authentication"
Combine results from multiple sources into coherent findings:
Write your findings to the handoff directory.
Handoff filename format: research-NN-<topic>.md
markdown--- date: [ISO timestamp] type: research status: success topic: [Research topic] sources: [nia, perplexity, firecrawl] --- # Research Handoff: [Topic] ## Research Question [Original question/topic] ## Key Findings ### Library Documentation [Findings from Nia - API references, usage patterns] ### Best Practices [Findings from Perplexity - recommended approaches, patterns] ### Additional Sources [Any scraped documentation] ## Code Examples
// Relevant code examples found
## Recommendations
- [Recommendation 1]
- [Recommendation 2]
## Potential Pitfalls
- [Thing to avoid 1]
- [Thing to avoid 2]
## Sources
- [Source 1 with link]
- [Source 2 with link]
## For Next Agent
[Summary of what the plan-agent or implement-agent should know]After creating your handoff, return:
Research Complete
Topic: [Topic]
Handoff: [path to handoff file]
Key findings:
- [Finding 1]
- [Finding 2]
- [Finding 3]
Ready for plan-agent to continue.If an MCP tool fails (API key missing, rate limited, etc.):
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 6,094 | 5,116 | -16% | 1 | 1 | 0% | 469 | 1,328 | +183% | 0 | 0 | — |
case-02 | fail→fail | 3,312 | 5,362 | +62% | 1 | 1 | 0% | 276 | 1,368 | +396% | 0 | 0 | — |
case-03 | fail→fail | 34,766 | 5,290 | -85% | 1 | 1 | 0% | 6,240 | 1,278 | -80% | 0 | 0 | — |
case-04 | fail→pass | 8,222 | 3,435 | -58% | 1 | 1 | 0% | 1,387 | 1,607 | +16% | 0 | 0 | — |
case-05 | fail→pass | 16,861 | 3,415 | -80% | 1 | 1 | 0% | 2,837 | 1,449 | -49% | 0 | 0 | — |
case-06 | fail→pass | 7,524 | 1,787 | -76% | 1 | 1 | 0% | 1,384 | 1,235 | -11% | 0 | 0 | — |
case-07 | fail→pass | 8,244 | 5,928 | -28% | 1 | 1 | 0% | 1,411 | 2,131 | +51% | 0 | 0 | — |
case-08 | fail→pass | 8,288 | 2,217 | -73% | 1 | 1 | 0% | 1,556 | 1,338 | -14% | 0 | 0 | — |
case-09 | fail→pass | 5,372 | 2,548 | -53% | 1 | 1 | 0% | 1,007 | 1,241 | +23% | 0 | 0 | — |
case-10 | fail→pass | 6,224 | 2,099 | -66% | 1 | 1 | 0% | 994 | 1,290 | +30% | 0 | 0 | — |
case-11 | fail→pass | 12,078 | 2,068 | -83% | 1 | 1 | 0% | 1,925 | 1,208 | -37% | 0 | 0 | — |
case-12 | pass→fail | 21,161 | 5,104 | -76% | 1 | 1 | 0% | 4,281 | 1,180 | -72% | 0 | 0 | — |
case-13 | pass→fail | 9,998 | 5,986 | -40% | 1 | 1 | 0% | 2,086 | 1,143 | -45% | 0 | 0 | — |
case-14 | fail→fail | 5,306 | 8,367 | +58% | 1 | 1 | 0% | 834 | 1,126 | +35% | 0 | 0 | — |
case-15 | pass→pass | 19,669 | 3,278 | -83% | 1 | 1 | 0% | 1,843 | 1,520 | -18% | 0 | 0 | — |
case-20 | fail→fail | 7,283 | 1,448 | -80% | 1 | 1 | 0% | 1,230 | 1,130 | -8% | 0 | 0 | — |
case-16 | fail→pass | 7,356 | 2,097 | -71% | 1 | 1 | 0% | 1,238 | 1,324 | +7% | 0 | 0 | — |
case-17 | fail→pass | 7,585 | 1,757 | -77% | 1 | 1 | 0% | 1,252 | 1,177 | -6% | 0 | 0 | — |
case-18 | pass→pass | 13,246 | 6,726 | -49% | 1 | 1 | 0% | 1,802 | 2,109 | +17% | 0 | 0 | — |
case-19 | fail→pass | 12,711 | 3,483 | -73% | 1 | 1 | 0% | 1,946 | 1,508 | -23% | 0 | 0 | — |
case-21 | fail→pass | 11,240 | 2,205 | -80% | 1 | 1 | 0% | 2,052 | 1,329 | -35% | 0 | 0 | — |
case-22 | fail→fail | 5,280 | 1,994 | -62% | 1 | 1 | 0% | 1,030 | 1,247 | +21% | 0 | 0 | — |
case-23 | fail→pass | 9,398 | 1,502 | -84% | 1 | 1 | 0% | 1,831 | 1,200 | -34% | 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 17 counted toward the lift figure. The other 6 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 +48 percentage points is the difference between those two pass rates over the 17 comparable cases. 3 cases got worse with the skill loaded, and they are 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.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 7/29/2026 | +27% |
Other measured skills in the registry, with their headline benchmark lift.