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Get Started Free →Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.
.claude/skills/oliver-kriska-research/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-20 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-12 | ✓→✗ | ▼ Worse | -23% | 0% |
| case-22 | ✓→✗ | ▼ Worse | 442% | 0% |
| case-13 | ✓→✓ | = Same ✓ | 7% | 0% |
| case-17 | ✓→✓ | = Same ✓ | 53% | 0% |
Research a topic by searching the web and fetching relevant sources efficiently.
/phx:research Oban unique jobs best practices
/phx:research LiveView file upload with progress
/phx:research --library permit$ARGUMENTS = Research topic/question. Add --library for structured library evaluation (uses ${CLAUDE_SKILL_DIR}/references/library-evaluation.md template).
If $ARGUMENTS contains --library or the topic is clearly about evaluating a Hex dependency (e.g., "should we use permit", "evaluate sagents", "compare oban vs exq"):
${CLAUDE_SKILL_DIR}/references/library-evaluation.md for the template.claude/research/{lib}-evaluation.mdCache check: Check if .claude/research/{topic-slug}.md already exists. If recent (<24 hours): present existing summary, ask "Refresh or use existing?"
Tidewave shortcut: If the topic is about an existing dependency (library already in mix.exs), prefer Tidewave over web search:
mcp__tidewave__get_docs(module: "LibraryModule")This returns docs matching your exact mix.lock version — faster, more accurate, zero web tokens. Only fall through to web search if Tidewave is unavailable or the topic needs community discussion (gotchas, real-world patterns, comparisons).
NEVER pass raw $ARGUMENTS into WebSearch. Decompose first:
$ARGUMENTS < 30 words and focused → use as single query$ARGUMENTS > 30 words or multi-topic → extract 2-4 queriesEach query: max 10 words, targets ONE specific aspect.
Example:
Input: "detect files, export to md, feed database with embeddings,
use ReqLLM for OpenAI API..."
Queries:
1. "Elixir PDF text extraction library hex"
2. "Ecto pgvector embeddings setup"
3. "ReqLLM OpenAI embeddings Elixir"Search ALL decomposed queries in a SINGLE response (parallel):
WebSearch(query: "{query1} site:elixirforum.com OR site:hexdocs.pm OR site:github.com")
WebSearch(query: "{query2} site:hexdocs.pm OR site:elixirforum.com")Deduplicate URLs across results. Discard clearly irrelevant hits.
Group URLs by topic cluster. Spawn 1-3 web-researcher agents in parallel (one per topic cluster):
Agent(subagent_type: "phx:web-researcher", prompt: """
Research focus: {specific aspect from decomposed query}
Fetch these URLs:
- {url1}
- {url2}
- {url3}
Extract: code examples, patterns, gotchas, version compatibility.
Return 500-800 word summary.
""", run_in_background: true)Rules:
After ALL agents complete, synthesize summaries into ONE file. Target: ~5KB for topic research, ~3KB for library evaluations.
Create .claude/research/{topic-slug}.md:
markdown# Research: {topic} ## Summary {2-3 sentence answer combining all worker findings} ## Sources ### {Category} - [{title}]({url}) - {key insight} ### Code Examples
{code}
## Recommendations
1. {recommendation with evidence}
2. {recommendation with evidence}
## Watch Out For
- {gotcha from forum/issues}
- {version compatibility note}
STOP and present the research summary. Do NOT auto-transition.
Use AskUserQuestion to let the user choose next action:
/phx:plan/phx:investigateNEVER auto-invoke /phx:plan or any other skill after research.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 14,368 | 6,510 | -55% | 1 | 1 | 0% | 2,487 | 1,777 | -29% | 0 | 0 | — |
case-02 | fail→fail | 6,004 | 12,844 | +114% | 1 | 1 | 0% | 286 | 2,290 | +701% | 0 | 0 | — |
case-03 | fail→fail | 5,277 | 10,249 | +94% | 1 | 1 | 0% | 282 | 1,843 | +554% | 0 | 0 | — |
case-04 | fail→fail | 23,098 | 9,116 | -61% | 1 | 1 | 0% | 3,505 | 2,046 | -42% | 0 | 0 | — |
case-05 | fail→fail | 19,235 | 13,918 | -28% | 1 | 1 | 0% | 3,547 | 1,794 | -49% | 0 | 0 | — |
case-06 | fail→fail | 20,944 | 10,499 | -50% | 1 | 1 | 0% | 3,289 | 1,922 | -42% | 0 | 0 | — |
case-07 | fail→fail | 22,160 | 6,968 | -69% | 1 | 1 | 0% | 3,272 | 1,474 | -55% | 0 | 0 | — |
case-08 | fail→fail | 13,744 | 5,505 | -60% | 1 | 1 | 0% | 1,566 | 1,633 | +4% | 0 | 0 | — |
case-09 | fail→fail | 17,253 | 7,907 | -54% | 1 | 1 | 0% | 2,728 | 1,566 | -43% | 0 | 0 | — |
case-10 | fail→fail | 17,564 | 10,673 | -39% | 1 | 1 | 0% | 2,611 | 1,889 | -28% | 0 | 0 | — |
case-11 | fail→fail | 22,740 | 7,498 | -67% | 1 | 1 | 0% | 4,088 | 1,851 | -55% | 0 | 0 | — |
case-12 | pass→fail | 12,665 | 36,413 | +188% | 1 | 1 | 0% | 2,332 | 1,803 | -23% | 0 | 0 | — |
case-13 | pass→pass | 14,980 | 6,724 | -55% | 1 | 1 | 0% | 2,359 | 2,532 | +7% | 0 | 0 | — |
case-14 | fail→fail | 15,746 | 7,854 | -50% | 1 | 1 | 0% | 2,581 | 1,598 | -38% | 0 | 0 | — |
case-15 | fail→fail | 22,022 | 7,906 | -64% | 1 | 1 | 0% | 3,746 | 1,814 | -52% | 0 | 0 | — |
case-16 | fail→fail | 19,744 | 6,009 | -70% | 1 | 1 | 0% | 2,947 | 1,569 | -47% | 0 | 0 | — |
case-17 | pass→pass | 9,517 | 6,040 | -37% | 1 | 1 | 0% | 1,442 | 2,206 | +53% | 0 | 0 | — |
case-18 | fail→fail | 19,253 | 9,893 | -49% | 1 | 1 | 0% | 3,024 | 2,055 | -32% | 0 | 0 | — |
case-19 | fail→fail | 26,888 | 8,681 | -68% | 1 | 1 | 0% | 3,996 | 1,859 | -53% | 0 | 0 | — |
case-20 | fail→pass | 15,661 | 16,426 | +5% | 1 | 1 | 0% | 2,994 | 3,969 | +33% | 0 | 0 | — |
case-21 | fail→fail | 4,206 | 9,141 | +117% | 1 | 1 | 0% | 678 | 2,064 | +204% | 0 | 0 | — |
case-22 | pass→fail | 3,187 | 6,424 | +102% | 1 | 1 | 0% | 407 | 2,206 | +442% | 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. 22 cases were attempted, and 4 counted toward the lift figure. The other 18 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 -5 percentage points is the difference between those two pass rates over the 4 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.
Other measured skills in the registry, with their headline benchmark lift.