optim-plans
Reduces execution risk by enforcing human review checkpoints and documented decision records before agent actions proceed, critical for teams deploying AI ag…
Human-in-the-loop planning plugin for Claude and Codex: turn ideas into reviewed Markdown plans, record decisions, enforce explicit execution gates, and provide tested controller primitives for safer agent workflows.
- Generate detailed project plans from rough ideas and get human approval before execution.
- Record technical decisions and their rationale in structured Markdown for team reference.
- Require explicit checkpoint reviews before agents proceed to high-risk deployment steps.
Reduces execution risk by enforcing human review checkpoints and documented decision records before agent actions proceed, critical for teams deploying AI agents in production environments.
Engineering teams implementing agentic workflows who need audit trails, approval gates, and safer autonomous decision-making in development or operational contexts.
https://github.com/Optim-Agent/optim-plans
By Optim-Agent
How to Get It
claude plugins install Optim-Agent/optim-plans
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me generate detailed project plans from rough ideas and get human approval before execution
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 94 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 94 GitHub stars; contributors unknown; last commit 0d ago; license MIT.