Ross ROSS = Recommend OSS · open-source software intelligence for agents

ChristopherKahler/paul

Plan-Apply-Unify Loop — Structured AI-assisted development for Claude Code. Quality over speed-for-speed's-sake. observed · 2026-10-10

github.com/ChristopherKahler/paul · homepage · JavaScript · MIT (permissive) observed · 2026-10-10

Health v2 · maintenance only

57/100

  • Activity 90
  • Release rhythm 37
  • Longevity 18
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 265
  • days_rel: 210
  • days_push: 60
  • n_releases_24m: 1

Full methodology

Adoption not part of the score

1251 stars · 140 forks observed · 2026-10-10

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

2 sources

Member repositories

RepositoryRoleHealth v2
ChristopherKahler/paulmain57

For agents

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

Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem