humanize

Humanize - Iterative Development with AI Review

Humanize creates a feedback loop where AI implements your plan while another AI independently reviews the work, ensuring quality through continuous refinement.

Runtime Command

All command examples below use the humanize CLI available on PATH:

humanize

Core Philosophy

Iteration over Perfection: Instead of expecting perfect output in one shot, Humanize leverages an iterative feedback loop where:

• AI implements your plan
• Another AI independently reviews progress
• Issues are caught and addressed early
• Work continues until all acceptance criteria are met

Available Workflows

1. RLCR Loop - Iterative Development with Review

The RLCR (Ralph-Loop with Codex Review) loop has two phases:

Phase 1: Implementation

• AI works on the implementation plan
• AI writes a summary of work completed
• Codex reviews the summary for completeness and correctness
• If issues found -> feedback loop continues
• If Codex outputs "COMPLETE" -> enters Review Phase

Phase 2: Code Review

• codex review --base <branch> checks code quality
• Issues marked with [P0-9] severity markers
• If issues found -> AI fixes them and continues
• If no issues -> loop completes with Finalize Phase
• In skill mode, always run humanize gate rlcr to enforce hook-equivalent transitions and blocking

2. PR Loop - Automated PR Review Handling

Automates handling of GitHub PR reviews from remote bots:

1. Detects the PR associated with the current branch
2. Fetches review comments from specified bot(s) (--claude and/or --codex)
3. AI analyzes and fixes issues identified by the bot(s)
4. Pushes changes and triggers re-review by commenting @bot
5. Stop Hook polls for new bot reviews (every 30s, 15min timeout per bot)
6. Local Codex validates if remote concerns are resolved
7. Loop continues until all bots approve or max iterations reached

3. Generate Plan - Structured Plan from Draft

Transforms a rough draft document into a structured implementation plan with:

• clear goal description
• acceptance criteria in AC-X format with TDD-style positive/negative tests
• path boundaries (upper/lower bounds, allowed choices)
• feasibility hints and conceptual approach
• dependencies and milestone sequencing

When running inside Claude Code, prefer the humanize-gen-plan flow/skill behavior:

• use humanize config merged --json --with-meta to read merged plan-generation defaults
• use humanize gen-plan --prepare-only for deterministic validation and scaffold preparation
• use humanize ask-codex for first-pass and convergence reviews
• use host reasoning plus AskUserQuestion for clarification, convergence, and final authoring

The full humanize gen-plan command remains available for standalone terminal workflows.

Commands Reference

Start RLCR Loop

# With a plan file
humanize setup rlcr path/to/plan.md

# Or without plan (review-only mode)
humanize setup rlcr --skip-impl

# For each round, run the RLCR gate (required)
humanize gate rlcr

Common Options:

• --max or --max-iterations N - Maximum iterations before auto-stop (default: 42)
• --codex-model MODEL:EFFORT - Codex model and reasoning effort for codex exec (default: gpt-5.4:xhigh)
• Review phase codex review uses gpt-5.4:high
• --codex-timeout SECONDS - Timeout for each Codex review (default: 5400)
• --base-branch BRANCH - Base branch for code review (auto-detects if not specified)
• --full-review-round N - Interval for full alignment checks (default: 5)
• --skip-impl - Skip implementation phase, go directly to code review
• --track-plan-file - Enforce plan-file immutability when tracked in git
• --push-every-round - Require git push after each round
• --claude-answer-codex - Let Claude answer Codex Open Questions directly (default is AskUserQuestion behavior)
• --agent-teams - Enable Agent Teams mode

Cancel RLCR Loop

humanize cancel rlcr
# or force cancel during finalize phase
humanize cancel rlcr --force

Start PR Loop

# Monitor claude[bot] reviews
humanize setup pr --claude

# Monitor chatgpt-codex-connector[bot] reviews
humanize setup pr --codex

# Monitor both
humanize setup pr --claude --codex

Common Options:

• --max or --max-iterations N - Maximum iterations (default: 42)
• --codex-model MODEL:EFFORT - Codex model for validation (default: gpt-5.4:medium)
• --codex-timeout SECONDS - Timeout for Codex validation (default: 900)

Cancel PR Loop

humanize cancel pr

Generate Plan from Draft

humanize gen-plan --prepare-only --input path/to/draft.md --output path/to/plan.md

Then continue with host-driven Codex consultation, convergence, clarification, and plan authoring.

Ask Codex (One-shot Consultation)

humanize ask-codex [--model MODEL] [--effort EFFORT] [--timeout SECONDS] "your question"

Goal Tracker System

The RLCR loop uses a Goal Tracker to prevent goal drift:

• IMMUTABLE SECTION: Ultimate Goal and Acceptance Criteria (set in Round 0, never changed)
• MUTABLE SECTION: Active Tasks, Completed Items, Deferred Items, Plan Evolution Log

Key Principles

1. Acceptance Criteria: Each task maps to a specific AC
2. Plan Evolution Log: Document any plan changes with justification
3. Explicit Deferrals: Deferred tasks require strong justification
4. Full Alignment Checks: Every N rounds (default: 5), comprehensive goal alignment audit

Important Rules

1. Write summaries: Always write work summary to the specified file before exiting
2. Maintain Goal Tracker: Keep goal-tracker.md up-to-date with progress
3. Be thorough: Include details about implementation, files changed, tests added
4. No cheating: Don't try to exit by editing state files or running cancel commands
5. Run stop gate each round: Use humanize gate rlcr instead of manual phase control
6. Trust the process: External review helps improve implementation quality

Prerequisites

• humanize - Humanize CLI
• codex - OpenAI Codex CLI (for review)
• gh - GitHub CLI (for PR loop)