Onramp

You open a repository you didn't write, or one your coding agent wrote for you, and you can't tell what does what.

Onramp reads the project and builds a guide you can actually learn from. It asks how much time you have and what you want to do, then plans a route through the code, stop by stop, from "what is this" down to the internals. Every technical claim links to the exact lines it comes from, and a script checks that those lines say what the guide says before you ever see it.

Install

npx skills add SanyamWadhwa07/onramp

The skills CLI finds the agents on your machine and installs Onramp into the ones you pick: Claude Code, Codex, GitHub Copilot, Cursor, Gemini CLI, Antigravity, Windsurf, OpenCode, Cline and 60+ more. Name them directly with -a codex -a cursor.

Claude Code plugin

/plugin marketplace add SanyamWadhwa07/onramp
/plugin install onramp-skill@onramp

Git

git clone https://github.com/SanyamWadhwa07/onramp
cp -r onramp/skills/onramp ~/.claude/skills/onramp      # Claude Code
cp -r onramp/skills/onramp ~/.agents/skills/onramp      # Codex, Cline, Zed and others

Download the skill folder as a zip from the latest release and unzip it into your agent's skills folder.

Onramp needs Python 3.8 or newer. There is nothing else to install.

Use

In Claude Code:

/onramp

In any other agent, ask in plain words: "use onramp to explain this repo".

Ask for	What you get
/onramp	The full guide for this repository, opened in your browser
/onramp quick	Only the 1-hour route: faster and cheaper
/onramp explain src/auth/session.ts	A deep lesson on one file, folder or function
/onramp trace "user resets their password"	One action followed through the code, step by step
/onramp update	The guide refreshed after the code changed
/onramp tutor	Your agent teaches you the route in chat, with checkpoint questions

Everything is written to .onramp/ in your repository. Your code is never modified.

File	What it is
index.html	The interactive guide. One file, opens from disk, no server.
GUIDE.md	The same guide as markdown, for GitHub and for other agents
guide.json	The source the page is built from

What you get

A route, not a pile. Pick your time (1 hour, 1 day, 3 days, 1 week), your goal (get the gist, ship a change, own it, rebuild it) and your background. Onramp plans a day-by-day route and draws it as a road with exits, rest stops, checkpoints and a "you are here" marker. Longer routes add hands-on stops: reading key files in full, doing exercises, building milestones yourself. Your progress is saved in the browser.

Every claim checked. A citation names a file, a line range and a short piece of text from those lines, like [[app/store.py:29-38|def place_order(]]. verify.py opens every cited file and checks the text is really there. Drifted line numbers are re-anchored; a claim whose text is nowhere in the file fails and is rewritten from the code. Click a citation and the code you see is copied from your file, never written by the model.

Why it is built this way. Onramp reads the git history, and with your permission your local Claude Code transcripts, so decisions come with evidence: a commit, a doc, a comment, the prompt that started it. Each decision is labelled documented or inferred.

Twelve chapters, from the gist to the internals. The gist · Run it · The stack · The map · Core ideas · How it flows · Data and state · File by file · How it was built · Why it's like this · Handle with care · Your first changes.

Live demos

Demo	What it is	Guide
PaperMind	A 31,575-line FastAPI + React research-paper app, built with an AI agent	open · 895/895 citations verified
nanoGPT	Andrej Karpathy's minimal GPT training repository	open · 496/496 citations verified
Flask	The Python web framework, at the commit the evaluation uses	open · 701/701 citations verified

The PaperMind guide also shows what an honest guide looks like: its "Handle with care" chapter cites real bugs in the code, such as upload retries that can never succeed and grounding results that are computed and then dropped.

Evaluation

Onramp is measured with three established methods. Every prompt, answer and grade is in evals/, and the method is written up in evals/README.md.

Question	Method	Result
Does the checker catch broken citations?	Fault injection on 2 real guides	525 / 525 planted faults caught, 0 false alarms on 200 real citations
Are the guide's statements about the code true?	FActScore (Min et al., EMNLP 2023), blind, evidence-checked	96.7% of sampled claims supported (baseline document: 100%)
Does the guide help answer real questions?	SWE-QA, 48 Flask questions (ACL Findings 2026), blind	67.1 / 100, level with the model's own knowledge of Flask (67.2), ahead of the README (63.6)

Flask is a codebase the models already know well, so the comparison is conservative. The full write-up, including what the numbers do not show, is in evals/README.md.

How it works

flowchart LR
  scan["scan.py<br/>inventory + import graph"] --> read["your agent reads the code<br/>in importance order"]
  read --> guide["guide.json<br/>lessons, flows, files"]
  guide --> verify["verify.py<br/>checks every citation"]
  verify -- "unsupported claim" --> read
  verify --> render["render.py"]
  render --> out["index.html + GUIDE.md"]

This is the beginning of the README. Read the rest at the original. Read the full README ↗