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    Agent Checkpoint (Time Travel for AI)

    1

    Save and restore AI agent state using a local SQLite database. Resume failed tasks without losing context or wasting tokens.

    $19

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    Agent Checkpoint (Time Travel for AI)

    Agent Checkpoint (Time Travel for AI)

    Example session with this skill installed

    python scripts/checkpoint.py save "refactor-auth" '{"step": 2, "files_modified": ["auth.py", "session.py"], "notes": "Completed JWT validation logic, moving to OAuth next"}'

    • Read your context and instructions
    • Compiled the agent checkpoint time

    I have successfully saved the current task state to the local database. This checkpoint is now indexed under the task name "refactor-auth" and can be retrieved in future sessions to resume work.

    Checkpoint Summary

    FieldValue
    Task Namerefactor-auth
    Checkpoint ID8
    Timestamp2023-10-27 14:22:10
    Current Step2
    Modified Filesauth.py, session.py
    Context NotesCompleted JWT validation logic, moving to OAuth next
    Storage Path./.agent_checkpoints.db

    Next steps

    1. Proceed with the OAuth implementation as planned in the task notes.
    2. Run python scripts/checkpoint.py save "refactor-auth" ... once the OAuth flow is verified.
    3. If the session expires, use the load command to recover this specific metadata.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Stop starting from scratch when your AI agent fails halfway through a 10-step task.

    The Agent Checkpoint skill gives your agent local memory. It saves the current state (variables, file progress, notes) to a fast SQLite database. If the session crashes, hits a token limit, or the user needs to pause, the agent can simply run load to pick up exactly where it left off.

    Why use it?

    Save Money: Don't pay for the same 5,000 tokens twice.

    Save Time: Resume complex multi-file refactors instantly.

    Zero Setup: Uses standard Python sqlite3. No databases to install.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for LLM Ops.

    Requires Python and local file write permissions. Does not perform automatic interval-based saving.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Software
    Python

    The checkpoint file is plain SQLite — you can inspect it, back it up, or move it between machines. Restoring a checkpoint overwrites the current agent state, so save one before you restore a previous one.

    Permissions

    Terminal / Shell
    Read Files
    Listed2 days ago
    Updatedtoday

    What's inside

    Frequently Asked Questions

    This skill allows your AI agents to store their current progress and metadata in a local SQLite database, preventing lost work due to context window resets, process crashes, or session timeouts.

    The skill is framework-agnostic and works with any AI agent capable of executing Python scripts, including custom-built LLM agents and popular open-source frameworks.

    You will receive the core Python script and the implementation logic required to integrate the SQLite state manager into your agent's workflow.

    The skill creates a local `.agent_checkpoints.db` file in your project directory; simply ensure your environment has Python installed and your agent has standard file write permissions.

    While it doesn't save every keystroke automatically, it is designed for you to trigger 'save' commands at critical milestones, ensuring you have a clean recovery point if a crash occurs.

    Unlike basic prompting, which relies on the LLM's volatile context window, this skill offloads specific data like file pointers and step counts to a persistent database that survives even if the agent is restarted.

    New in LLM Ops