ChristopherKahler/paul

Health v2 · maintenance only

Adoption not part of the score

1205 stars · 128 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PAUL (Plan-Apply-Unify Loop) is a structured development framework for Claude Code that enforces a PLAN → APPLY → UNIFY cycle with acceptance criteria, state persistence, and decision logging. It combats context rot by keeping implementation in-session and reserving subagents for research, distributed as an npm package runnable via npx.

Use cases

• structure ai-assisted development with claude code
• prevent context rot in long ai coding sessions
• enforce acceptance criteria in ai-generated code
• manage spec-driven development workflows
• track planned vs completed work across ai sessions
• run structured non-code projects like marketing campaigns with ai

When to choose

• you use Claude Code daily and suffer from drifting plans and degraded session quality
• you want enforced loop discipline with reconciliation (UNIFY) and audit trails
• you prefer in-session execution over subagent sprawl
• you want acceptance-criteria-first, BDD-style task qualification

When to avoid

• you don't use Claude Code or another compatible AI coding agent
• you want lightweight ad-hoc prompting without structured state files
• your workflow depends on heavy subagent parallelization for implementation
• you need a battle-tested enterprise framework rather than a young community tool

Facets

framework · maturity active

agent-framework prompt-engineering workflow-automation developer-tools developer-tools large-language-models windows cli claude-code spec-driven-development vibe-coding context-management acceptance-criteria plan-execute-loop ai-assisted-development automation ai-agents nodejs macos linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ChristopherKahler/paul")