Product Hunt launch surface

Turn any repo into task-shaped context for coding agents.

Cartograph maps the repo, builds a typed task packet, and loads only the context the next agent needs.

The product is live now as an open-source package with a CLI, an optional MCP server, a Claude Code plugin path, OpenClaw skills, benchmark proof, and real sample artifacts on this site.

Install with npm See the 5-minute quickstart Benchmark proof
Free
open-source package
CLI
first, MCP optional
Live
npm, Claude, OpenClaw, MCP
Analyze -> Packet -> Context
npm install -g @anthony-maio/cartograph

cartograph analyze ./my-project --static
cartograph packet ./my-project --type bug-fix --task "fix auth refresh bug"
cartograph context ./my-project --task "trace the auth flow" --json

/plugin marketplace add anthony-maio/cartograph
/plugin install cartograph@making-minds-tools
Outputs: ranked files, dependency hubs, typed packets, minimal task context launch-ready and live now
Why this converts

Cartograph is not “AI reads your repo.” It is workflow infrastructure.

The point is not to stuff more files into context. The point is to hand the next agent a smaller, better artifact. That is why the product is organized around analyze, packet, and context.

What most tools do

Repo orientation becomes a context tax.

  • Read broad file trees just to get started.
  • Miss dependency hubs and actual edit surfaces.
  • Repeat the same orientation work in every host.
What Cartograph does

Reduce the repo to the useful shape.

  • Map the repo before the agent burns context.
  • Shape the work into a typed task packet.
  • Load only the minimum useful file set.
Small vs large repos

It should get out of the way on tiny repos and pay off on big ones.

Small repos

Stay compact by default.

Small projects should not get a 64 KB JSON dump just because a tool ran. Cartograph now keeps small-repo analysis compact by default and only embeds file contents when you ask for them.

Medium repos

Packets become a real time saver.

This is where the workflow starts to matter: analyze the repo, shape the concrete job, then hand the next agent a packet instead of a wall of text.

Large repos

Triage and handoff become the product.

On repos like llama.cpp, fastapi, and next.js, the value is not just summary. It is choosing what to read first and what to ignore.

Gallery storyboard

Six screenshot-ready slides for the Product Hunt listing.

These are designed to be lifted directly into the launch asset pack. Each frame has one job: make the product legible in seconds.

Slide 01 · Problem

Most coding agents waste context on repo orientation.

They read too many files, miss the real wiring, and spend the first half of the session figuring out what matters.

Show the pain clearly
Slide 02 · Workflow

Analyze -> Packet -> Context

Cartograph maps the repo, builds a typed task packet, and loads the smallest useful working set for the next agent.

This is the product
Slide 03 · Analyze

Start with a human-readable repo map.

Summary-first static analysis surfaces what matters, dependency hubs, and the next command to run instead of forcing raw internals on the first screen.

Use the summary-first output
Slide 04 · Packet

Build a reusable working artifact for the actual job.

Bug-fix, PR review, trace-flow, and change-request packets stay focused on likely edit surfaces, risks, and validation targets.

Use the llama.cpp packet
Slide 05 · Proof

Back it up with benchmark scorecards and real artifacts.

Use the public benchmark page plus the real llama.cpp task packet and DeepWiki-style brief to prove the product is not just a mockup.

Show examples, not claims
Slide 06 · Distribution

Live now across the surfaces people actually use.

npm, Claude Code plugin path, OpenClaw skills, GitHub, and the official MCP Registry all point back to the same product surface.

Close with availability
Listing copy

Use this for the Product Hunt submission itself.

Tagline

Turn any repo into task-shaped context for coding agents

Turn any repo into task-shaped context for coding agents
Short description

Live, specific, and product-shaped.

Open-source repo analysis for coding agents. Cartograph maps the repo, builds a typed task packet, and loads only the context the next agent needs. CLI first, MCP optional, with Claude Code and OpenClaw paths included.
Primary URL

Submit this page, not the GitHub repo.

  • https://cartograph.making-minds.ai/launch/
  • Use the homepage and example artifacts as supporting links.
Maker first comment

Open with the problem and the workflow.

I built Cartograph because every coding agent workflow I watched wasted context on repo orientation before doing useful work.

The goal is simple: map the repo, build a task packet for the actual job, then load the minimum context needed for that job.

This release is centered on analyze -> packet -> context. Cartograph is live today as a CLI, an optional MCP server, a Claude Code plugin path, and an OpenClaw skill path, all riding on the same core analysis engine.

The site also includes public benchmark scorecards plus a real llama.cpp task packet and DeepWiki-style brief so people can inspect the outputs instead of trusting screenshots.

I’m most interested in feedback on where packets still drift in large repos, what repo shapes should be benchmarked next, and what would make the handoff from packet to actual coding work tighter.
Proof links

Use public proof, not abstract claims.