Checkpointed Execution

Avoid expensive rollback costs and configuration drift in complex deployments. This skill enforces a structured, step-by-step execution framework designed for stateful operations like migrations, cluster builds, and API integrations. It replaces the "black box" execution style with a transparent series of reviewed checkpoints where every action is verified by evidence rather than status codes.

What it does

• Multi-step planning creates a detailed execution map with owner labels, verification steps, and risk assessments.
• Mandatory checkpointing pauses execution after every discrete step to allow for user review and redirection.
• Evidence-based verification queries the actual state of the system to prove success instead of relying on return codes.
• Stateful runbooks maintain a persistent record of every command run, surprise encountered, and result observed.
• Variable modes adjust the stopping frequency from lite (phase boundaries) to strict (before every write).

How it works

1. Initialize: Start by invoking /checkpointed-execution with a chosen mode or by describing a complex multi-step task.
2. Plan: Review the generated plan which identifies assumptions, scope, and specific verification methods for each step.
3. Execute & Verify: The agent performs one step, verifies the outcome through read-only checks, and presents a step report.
4. Confirm: Provide a "go" or "next" command to proceed, or provide corrections that are immediately applied to the plan and future steps.

Frameworks & tools

This skill is language-agnostic and works across CLI tools, cloud providers (AWS, GCP, Azure), database migrations, and CI/CD pipelines. It is particularly effective for Terraform, Kubernetes, and complex Python or Node.js integration scripts.

Why this beats prompting it yourself

Standard agents often "run ahead," completing five steps before realizing a typo in step one broke the entire state. This skill forces rigorous stopping points and formal evidence reporting that generic prompts ignore when they prioritize autonomous completion.

Use cases

• Infrastructure builds: Setting up VPCs, clusters, or databases where mistakes are costly to undo.
• Data migrations: Transferring records between systems while verifying integrity at each batch.
• Production changes: Implementing updates on live systems where every write requires a second pair of eyes.
• Onboarding/Learning: Guiding a developer through a new environment where they perform the manual steps.

Known limitations

Not intended for single-file edits, trivial bug fixes, or read-only investigations. Requires a user to be present for confirmation unless running in headless mode for report generation only.