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Research

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oliver-kriska
research

Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.

Overview

Publisheroliver-kriska
Repositoryclaude-elixir-phoenix
Skill nameresearch
Stars
555
Forks
40
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by oliver-kriska on GitHub. Read the source before you install it.

Installation

Install the Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/oliver-kriska/claude-elixir-phoenix.git /tmp/claude-elixir-phoenix
mkdir -p .claude/skills
cp -r /tmp/claude-elixir-phoenix/plugins/elixir-phoenix/skills/research .claude/skills/research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Research Elixir Topic

Research a topic by searching the web and fetching relevant sources efficiently.

Usage

/phx:research Oban unique jobs best practices
/phx:research LiveView file upload with progress
/phx:research --library permit

Arguments

$ARGUMENTS = Research topic/question. Add --library for structured library evaluation (uses ${CLAUDE_SKILL_DIR}/references/library-evaluation.md template).

Iron Laws

  1. Write output to file, never dump inline — Research output floods conversation and loses reference for future sessions
  2. Stop after research — never auto-transition — User decides next step
  3. Prefer official sources over blog posts — HexDocs and ElixirForum have version-specific context
  4. One document per research question — No fragmented files
  5. NEVER pass raw user input as WebSearch query — Decompose first

Library Evaluation Mode

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"):

  1. Read ${CLAUDE_SKILL_DIR}/references/library-evaluation.md for the template
  2. Follow the structured evaluation workflow
  3. Output ONE document to .claude/research/{lib}-evaluation.md
  4. Skip the general research workflow below

Workflow

0. Pre-flight Checks

Cache 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).

1. Query Decomposition (CRITICAL — before any search)

NEVER pass raw $ARGUMENTS into WebSearch. Decompose first:

  • If $ARGUMENTS < 30 words and focused → use as single query
  • If $ARGUMENTS > 30 words or multi-topic → extract 2-4 queries

Each 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"

2. Parallel Web Search

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.

3. Spawn Parallel Research Workers

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:

  • 1 topic cluster = 1 agent (don't mix unrelated URLs)
  • Max 5 URLs per agent (diminishing returns beyond that)
  • If only 1-3 URLs total, use single foreground agent
  • Pass URLs explicitly — agents should NOT re-search
  • Agents are haiku — cheap, fast, focused on extraction

4. Write Output (File-First — NEVER Dump Inline)

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

```elixir
# From {source}: {what this demonstrates}
{code}

Recommendations

  1. {recommendation with evidence}
  2. {recommendation with evidence}

Watch Out For

  • {gotcha from forum/issues}
  • {version compatibility note}

### 5. After Research — STOP

**STOP and present the research summary.** Do NOT auto-transition.

Use `AskUserQuestion` to let the user choose next action:

- "Plan a feature based on this research" → `/phx:plan`
- "Investigate a specific finding" → `/phx:investigate`
- "Research more on a subtopic" → continue research
- "Done" → end

**NEVER auto-invoke `/phx:plan` or any other skill after research.**

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Research AI skill do?

Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.

Why use Research on TypingMind?

Because you install it once and use it with any model. Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oliver-kriska/claude-elixir-phoenix/tree/main/plugins/elixir-phoenix/skills/research. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Research?

As many as you like. As long as a model supports skills, you can use Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Research AI skill free?

Yes. It is published on GitHub by oliver-kriska under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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