Reverse Engineer Anything Self Learning Loop

Reverse Engineer Anything with a Self-Learning Loop: examine software you are authorised to analyse, build your own working equivalent, then review it against the original with evidence, plan the gaps, improve, and repeat until a written definition of 100% is met, learning from every iteration so the next product starts further along. Use for: rebuilding a product whose source is lost, a compatible replacement for a tool you license, a port to a new platform, finishing a reconstruction that stalled at 80%. Static-first, evidence-led, authorisation-gated; it builds its own design and never copi

120 stars 9 forksLearning & Education

Install

skills CLI (Claude Code, Cursor, Codex and more)
npx skills add Speedrunlab/reverse-engineer-anything-self-learning-loop --skill reverse-engineer-anything-self-learning-loop
Manual install
git clone --depth 1 https://github.com/Speedrunlab/reverse-engineer-anything-self-learning-loop.git /tmp/reverse-engineer-anything-self-learning-loop
mkdir -p ~/.claude/skills
cp -r /tmp/reverse-engineer-anything-self-learning-loop/. ~/.claude/skills/reverse-engineer-anything-self-learning-loop

Personal skills live in ~/.claude/skills. Use .claude/skills in a project folder to share a skill with your team.

From the SKILL.md

# Reverse Engineer Anything with a Self-Learning Loop

Most reconstructions stop at "80% there". The agent examines a shipped product,
builds something that looks right, and nobody ever wrote down what 100% means,
so nobody can say what is missing. This skill closes that gap with two loops:

- **The completion loop, inside one run:** define 100% in writing before
  building, build, review the build against the original with evidence, plan
  the gaps, improve, and repeat until every must-have passes twice in a row.
- **The self-learning loop, across runs:** after every iteration the agent
  reviews what it learned (a route, a check, a fix, a pitfall) and saves it
  into this skill, staged for the operator, so the next product starts further
  along and the same mistake is never made twice.

It carries what it needs from two Speedrun AI Labs skills: the examination
method of **Reverse Engineer Anything** and the learning loop of **Hermes Agent
as a Skill File**. Neither has to be installed; both go deeper when they are
(see **Companions**). It is **agent-agnostic**: it names behaviours, never a
particular tool, hook or path.

This skill is for **authorised** work: software your organisation owns, ships,
licenses, or is explicitly cleared to examine and rebuild (a lost-source
product, interoperability, a replacement for a licensed tool, a port). It is not
for cloning a third party's product, defeating access controls, or lifting
Read the full skill on GitHub

Details

Skill name
reverse-engineer-anything-self-learning-loop
License
MIT
Repo updated
25 October 2026

FAQ

What does the Reverse Engineer Anything Self Learning Loop skill do?

Reverse Engineer Anything with a Self-Learning Loop: examine software you are authorised to analyse, build your own working equivalent, then review it against the original with evidence, plan the gaps, improve, and repeat until a written definition of 100% is met, learning from every iteration so the next product starts further along. Use for: rebuilding a product whose source is lost, a compatible replacement for a tool you license, a port to a new platform, finishing a reconstruction that stalled at 80%. Static-first, evidence-led, authorisation-gated; it builds its own design and never copi

How do I install the Reverse Engineer Anything Self Learning Loop skill?

skills CLI (Claude Code, Cursor, Codex and more): npx skills add Speedrunlab/reverse-engineer-anything-self-learning-loop --skill reverse-engineer-anything-self-learning-loop. Skills go in ~/.claude/skills (personal) or .claude/skills (project), and Claude loads them automatically when a task matches the description.

Is Reverse Engineer Anything Self Learning Loop popular?

It lives in Speedrunlab/reverse-engineer-anything-self-learning-loop, which has 120 GitHub stars and 9 forks.

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