How do I save my current work state?

You can manually trigger a checkpoint at any time using the /cc:save command, which will detect your current stage and save relevant metadata.

Where are the workflow checkpoints stored?

State is saved as standardized Markdown files within Serena MCP memory, following a specific naming convention based on the date and feature name.

When does automatic saving occur?

The skill automatically triggers a save after the research-orchestration phase completes and once a workflow is fully implemented before PR creation.

How do I resume a previous session?

You can resume any saved state by using the /cc:resume command followed by the specific memory filename.

Does this skill work with Git?

Yes, it automatically collects metadata including your current branch, commit hash, and git status to ensure context is preserved accurately.

State Persistence Workflow

Name: State Persistence Workflow
Author: seanGSISG

byseanGSISG

Productividad y Flujo de Trabajo

Saves and manages AI workflow states to enable seamless task resumption using Serena MCP memory.

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The State Persistence skill allows Claude to track and save the current state of complex development workflows across research, planning, execution, and completion phases. By utilizing Serena MCP memory, it captures essential metadata like Git commits, branches, and task progress, ensuring that work can be paused and resumed exactly where it left off using the /cc:resume command without losing context or research findings.

Características Principales

Automatic state saving after research and completion phases
Standardized memory file format for easy workflow auditing
0 GitHub stars
Manual checkpointing during planning and execution via /cc:save
Comprehensive metadata capture including Git status and timestamps
Context-aware stage detection algorithm for intelligent categorization

Casos de Uso

Documenting research and planning decisions for team handoffs
Resuming complex feature implementations after context switching
Maintaining a detailed progress log for long-running coding tasks

Acerca de

Características Principales

Automatic state saving after research and completion phases
Standardized memory file format for easy workflow auditing
0 GitHub stars
Manual checkpointing during planning and execution via /cc:save
Comprehensive metadata capture including Git status and timestamps
Context-aware stage detection algorithm for intelligent categorization

Casos de Uso

Documenting research and planning decisions for team handoffs
Resuming complex feature implementations after context switching
Maintaining a detailed progress log for long-running coding tasks