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To Callgraph

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elianiva
to-callgraph

Trace a code path end-to-end and render it as an annotated ASCII callgraph with a concurrency/serialization analysis.

Overview

Publisherelianiva
Repositorydotfiles
Skill nameto-callgraph
Stars
204
Forks
9
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by elianiva on GitHub. Read the source before you install it.

Installation

Install the To Callgraph 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/elianiva/dotfiles.git /tmp/dotfiles
mkdir -p .claude/skills
cp -r /tmp/dotfiles/agents/skills/to-callgraph .claude/skills/to-callgraph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable To Callgraph 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 To Callgraph 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 To Callgraph 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.

To-Callgraph

Trace a flow through the codebase end-to-end and hand back a callgraph: an ASCII diagram of every hop, annotated with where work runs concurrently and where it waits. When the user asks about a path — a feature, a flow, a worry like "can we parallelize this?" — the answer starts with the trace.

1. Scope the trace

Pin the entry (the event/message/command that starts the flow) and the exit (what completes it — a rendered frame, a blob URL, a persisted row). Find every path that carries the feature's name: a feature often has two (a CPU preview path and a GPU path, a fast path and a full path), and each gets traced.

Completion criterion: you can state, in one sentence, what enters and what exits each path.

2. Walk the hops

Each hop is one box of the callgraph: event → message → update → command → service → worker/GPU → message → update → model → view. Read the file that handles each hop before drawing the edge — a hop you haven't read is an edge you can't draw.

  • Recon the area first: fffind/grep for the feature's names, read what surfaces, then read the project's ADRs/docs for the decisions that shaped the path.
  • Before trusting an edge, grep for every caller of the exported symbols it crosses — missed callers are bugs.
  • Note the runtime's execution model: are commands forked (concurrent) or queued? That determines which hops are already parallel.

Completion criterion: every edge names a file you actually read; every exported symbol you cite has had its callers grepped.

3. Render the callgraph

ASCII, top-down, one line per hop, file path on the edge, in the house style:

<entry>
  └─ <dispatch> → <message>
       └─ <command.execute>        (forked — concurrent)
            ├─ <service>           ✓ memoized / parallel
            └─ <worker>            ⚠ serializes here — one thread, N requests

Mark every async boundary (postMessage/Deferred, Effect.runFork, GPU submission). Mark every hop that already runs concurrently (✓) and every hop where work queues up (⚠). If a hop spans multiple files, say so.

Completion criterion: a reader can point at any box, ask "where's the code?", and get a file:line.

4. Read the seams

For every ⚠, name the concrete cost and the fix shape: a pool (N workers/threads instead of 1), a cache (memoize a value recomputed per iteration), a move (off the main thread), a coalesce (dedupe identical requests). Also hunt wasted work: the same op repeated per item that could run once (a downscale per LUT instead of per photo). Every bottleneck gets a named fix direction — a complaint without a fix shape is an unfinished seam.

Completion criterion: every ⚠ and every repeated-op finding carries a named fix shape.

5. Deliver

Callgraph first, then: what's already parallel, where it serializes, options ranked by cost/benefit, and the recommended cut. When the user asks for a plan, add before/after callgraphs plus a file-by-file change list and how to verify (tests, typecheck, lint, manual smoke).

Completion criterion: the user can approve or reject the recommendation from your writeup alone.

Frequently asked questions

What does the To Callgraph AI skill do?

Trace a code path end-to-end and render it as an annotated ASCII callgraph with a concurrency/serialization analysis.

Why use To Callgraph on TypingMind?

Because you install it once and use it with any model. To Callgraph 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 To Callgraph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elianiva/dotfiles/tree/master/agents/skills/to-callgraph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use To Callgraph?

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 To Callgraph?

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

Is the To Callgraph AI skill free?

It is published on GitHub by elianiva. Check the repository for licensing terms. 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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