autoresearch

name

autoresearch

description

Automatically research and reverse-engineer a technique, visual effect, algorithm, or codebase. Given a reference (image, video, URL, library name, description, or existing code), Claude researches how it works, reverse-engineers the approach, and produces a structured findings document. Use when you want to understand "how is this made", "reverse engineer this effect", "research this technique", or "figure out how X works so I can implement it".

Autoresearch

Given a reference target, automatically research it and produce a reverse-engineering report with implementation guidance.

Input (via $ARGUMENTS or conversation context)

The target can be any of:

• A visual reference — image path, screenshot, video frame
• A URL — article, repo, shader toy link, paper
• A library or tool name — "how does pixelmatch work"
• A description — "that liquid metal refraction effect"
• An existing codebase or file — reverse-engineer undocumented code
• A combination of the above

Research phases

1. Identify the target

Clarify what's being researched: a visual technique, an algorithm, an architecture, a library internals. If ambiguous, ask one focused question before proceeding.

2. Collect evidence

• Visual refs: analyze images/screenshots with vision tools — describe what's happening geometrically, mathematically, temporally
• Code refs: read source files — identify core algorithms, data structures, key functions
• Web research: search for papers, shader implementations, blog posts, prior art
• Existing project context: scan refs/, docs/, reference/, CLAUDE.md, README.md in the working directory for related material already gathered

3. Reverse engineer

Break the technique into layers:

• What is the high-level effect or behavior?
• What mathematical/algorithmic primitives drive it?
• What are the key parameters and how do they interact?
• What are the non-obvious parts (the "tricks")?
• What constraints apply (platform, performance, compatibility)?

4. Implementation map

Translate findings into actionable implementation notes for the current project:

• Language/platform fit (GLSL ES 1.00, Metal MSL, TypeScript, etc.)
• Suggested implementation order (scaffold → core → details)
• Known pitfalls from the research

Output

Save a Markdown report to refs/research-<topic>-<YYYY-MM-DD>.md if a refs/ folder exists, otherwise to the current directory as research-<topic>-<YYYY-MM-DD>.md.

# [Topic] — Autoresearch
_Date: YYYY-MM-DD | Source: [reference used]_

## What it is
One paragraph: the effect/technique/system in plain terms.

## How it works
### Core mechanism
### Key mathematical primitives
### Parameters & controls
### The non-obvious parts

## Prior art & references
- [link or file] — what it contributes

## Implementation map
### Platform notes
### Suggested approach
### Pitfalls

## Open questions
- [ ] ...

Behavior

1. Identify the target from $ARGUMENTS or conversation context.
2. Run all relevant research phases in parallel where possible.
3. Synthesize into the report structure above — omit empty sections.
4. Save the file and confirm path to user.
5. Optionally: if the project has a todo.md or tasks.md, offer to append a follow-up implementation task.

$ARGUMENTS