
Formula Derivation
Takes messy research notes, scattered equations, or half-formed theory sketches and turns them into a structured derivation document with explicit assumptions, notation, and step-by-step logic. It won't fake a proof if your notes don't support one. Instead it freezes your target, identifies the invariant object organizing the derivation, labels each step as identity, proposition, approximation, or interpretation, and writes to DERIVATION_PACKAGE.md by default. The workflow is deliberate about catching hidden assumptions, notation conflicts, and cases where you're silently switching between different conceptual objects. Use it when you need honest theory scaffolding before writing a paper section, not when you already have a polished proof.
- skills.sh installs
- 563
- GitHub stars
- 18.9K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add wanshuiyin/auto-claude-code-research-in-sleep --skill formula-derivation --agent claude-codeInstalls into .claude/skills of the current project.
Files
Formula Derivation: Research Theory Line Construction
Build an honest derivation package, not a fake polished theorem story.
Constants
- DEFAULT_DERIVATION_DOC =
DERIVATION_PACKAGE.mdin project root - STATUS =
COHERENT AS STATED | COHERENT AFTER REFRAMING / EXTRA ASSUMPTION | NOT YET COHERENT
Context: $ARGUMENTS
Goal
Produce exactly one of:
- a coherent derivation package for the original target
- a reframed derivation package with corrected object / assumptions / scope
- a blocker report explaining why the current notes cannot yet support a coherent derivation
Inputs
Extract and normalize:
- the target phenomenon, formula, relation, or theory line
- the intended role of the derivation:
- exact identity / algebra
- proposition / local theorem
- approximation
- mechanism interpretation
- explicit assumptions
- notation and definitions
- any user-provided formula chain, sketch, messy notes, or current draft
- nearby local theory files if the request points to them
- desired output style if specified:
- internal alignment note
- paper-style theory draft
- blocker report
Sponsors
Details
- Repository
- github.com/wanshuiyin/auto-claude-code-research-in-sleep
- Categories
- DevOps & CI/CD, Data Science & ML
- First seen
- Jun 3, 2026
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