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Trace

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oliver-kriska
trace

Trace Elixir call trees from entry points via mix xref. Use when debugging data flow, planning signature changes, or understanding how a bug reaches code.

Overview

Publisheroliver-kriska
Repositoryclaude-elixir-phoenix
Skill nametrace
Stars
555
Forks
40
Bundled files
3
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.

  • 3 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 Trace 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/trace .claude/skills/trace
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trace 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 Trace 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 Trace 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.

Call Tracing

Build call trees showing how functions are reached from entry points.

Iron Laws - Never Violate These

  1. Always use mix xref callers first - It's authoritative; grep is fallback only
  2. Stop at entry points - Controllers, LiveView callbacks, Oban workers, GenServer callbacks
  3. Track visited MFAs - Prevent infinite loops from circular calls
  4. Extract argument patterns - Just knowing "who calls" isn't enough; HOW they call matters
  5. Max depth 10 - Deeper trees indicate architectural issues, not useful traces

When to Build Call Tree (Use Proactively)

ConditionWhy Call Tree Helps
Unexpected nil/value at runtimeTrace where the value originates
Bug can't reproduce locallySee all entry points that reach the code
Changing function signatureFind all callers and their argument patterns
Incomplete stack traceGet full path context
"Where does X come from?"Visual answer to data flow question

Quick Trace

Run the caller query first, then inspect another function in the chain as needed:

bash
mix xref callers MyApp.Accounts.update_user/2
mix xref callers MyApp.Accounts.get_user/1

Read the reported locations to see argument patterns.

Entry Points (Stop Here)

PatternType
def mount/3, def handle_event/3LiveView
def index/2, def show/2, def create/2Controller
def perform(%Oban.Job{})Oban Worker
def handle_call/3, def handle_cast/2GenServer

Delegate to call-tracer Agent

For full recursive tree with argument extraction and parallel category tracing:

Determine the effective maximum nesting depth. Use an explicit positive-integer CLAUDE_CODE_MAX_SUBAGENT_SPAWN_DEPTH value first; when it is unset, inspect claude --version (the default is 1 in 2.1.217–2.1.218 and 3 in 2.1.219+). If the version is unavailable, conservatively use 1. At depth 2+, delegate to the orchestrator below. At depth 1, keep orchestration in this main session: spawn the applicable controller, LiveView, worker, and internal tracing prompts directly, then merge their results. Never spawn an orchestrator that cannot delegate.

Agent(subagent_type: "phx:call-tracer", prompt: "Build call tree for MyApp.Accounts.update_user/2")

The call-tracer agent uses parallel subagents for each entry point category:

  • Controllers subagent (HTTP paths)
  • LiveView subagent (WebSocket paths)
  • Workers subagent (Background jobs)
  • Internal subagent (Cross-context calls)

Each gets fresh 200k context for deep exploration.

Output Location

.claude/plans/{slug}/research/call-tree-{function}.md

References

For detailed patterns:

  • ${CLAUDE_SKILL_DIR}/references/mix-xref-usage.md - Full mix xref commands and options
  • ${CLAUDE_SKILL_DIR}/references/entry-points.md - All Phoenix/OTP entry point patterns
  • ${CLAUDE_SKILL_DIR}/references/argument-extraction.md - AST parsing for argument patterns

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 Trace AI skill do?

Trace Elixir call trees from entry points via mix xref. Use when debugging data flow, planning signature changes, or understanding how a bug reaches code.

Why use Trace on TypingMind?

Because you install it once and use it with any model. Trace 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 Trace 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/trace. 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 Trace?

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 Trace?

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

Is the Trace 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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