By gyy0592
Humanize - An iterative development plugin that uses Codex to review Claude's work. Creates a feedback loop where Claude implements plans and Codex independently reviews progress, ensuring quality through continuous refinement.
Cancel active PR loop
Cancel active RLCR loop
Generate implementation plan from draft document
Refine an annotated implementation plan and generate a QA ledger
Start PR review loop with bot monitoring
Selects required BitLesson entries for a specific sub-task. Use before execution for every task or sub-task.
Checks if a draft document is relevant to the current repository. Use when validating draft content for gen-plan command.
Checks plan relevance and compliance before RLCR loop. Use when validating plan files for start-rlcr-loop command.
Analyzes a plan and generates multiple-choice technical comprehension questions to verify user understanding before RLCR loop. Use when validating user readiness for start-rlcr-loop command.
Generate a structured implementation plan from a draft document. Validates input, checks relevance, analyzes for issues, and generates a complete plan.md with acceptance criteria.
Refine an annotated implementation plan into a comment-free plan and a QA ledger while preserving the gen-plan schema.
Start RLCR (Ralph-Loop with Codex Review) with hook-equivalent enforcement from skill mode by reusing the existing stop-hook logic.
Iterative development with AI review. Provides RLCR (Ralph-Loop with Codex Review) for implementation planning and code review loops, plus PR review automation with bot monitoring.
Consult Codex as an independent expert. Sends a question or task to codex exec and returns the response.
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
No model invocation
Executes directly as bash, bypassing the AI model
No model invocation
Executes directly as bash, bypassing the AI model
Current Version: 1.15.1
Derived from the GAAC (GitHub-as-a-Context) project.
A Claude Code plugin that provides iterative development with independent AI review. Build with confidence through continuous feedback loops.
RLCR stands for Ralph-Loop with Codex Review, inspired by the official ralph-loop plugin and enhanced with independent Codex review. The name also reads as Reinforcement Learning with Code Review -- reflecting the iterative cycle where AI-generated code is continuously refined through external review feedback.
The loop has two phases: Implementation (Claude works, Codex reviews summaries) and Code Review (Codex checks code quality with severity markers). Issues feed back into implementation until resolved.
# Add humania marketplace
/plugin marketplace add humania-org/humanize
# If you want to use development branch for experimental features
/plugin marketplace add humania-org/humanize#dev
# Then install humanize plugin
/plugin install humanize@humania
Requires codex CLI for review. See the full Installation Guide for prerequisites and alternative setup options.
Generate a plan from your draft:
/humanize:gen-plan --input draft.md --output docs/plan.md
Refine an annotated plan before implementation when reviewers add CMT: ... ENDCMT comments:
/humanize:refine-plan --input docs/plan.md
Run the loop:
/humanize:start-rlcr-loop docs/plan.md
Monitor progress:
source <path/to/humanize>/scripts/humanize.sh
humanize monitor rlcr
MIT
npx claudepluginhub gyy0592/humanize --plugin humanizeComprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Intelligent draw.io diagramming plugin with AI-powered diagram generation, multi-platform embedding (GitHub, Confluence, Azure DevOps, Notion, Teams, Harness), conditional formatting, live data binding, and MCP server integration for programmatic diagram creation and management.
Evidence-gated AI coding workflow: scan → analyze → plan → TDD → execute → fix → verify → review, powered by Codebase Memory MCP >= 0.9.0 with optional Serena LSP intelligence. Includes blast-radius planning, test/cycle gates, independent review, and Windows Git Bash hook auto-resolution.
Consult multiple AI coding agents (Gemini, OpenAI, Grok, Perplexity, plus codex, antigravity, and grok CLIs when installed) to get diverse perspectives on coding problems
Production-grade engineering skills for AI coding agents — covering the full software development lifecycle from spec to ship.
Lazy senior dev mode. Forces the simplest, shortest solution that actually works: YAGNI, stdlib first, no unrequested abstractions.
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