Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    long task manager

    1

    Persistent filesystem-based state management and background process tracking for multi-session agent tasks.

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    long task manager

    Example session with this skill installed

    I need to index 5000 markdown files for a search engine. This will take about 4 hours. Start the task and keep me updated.

    • Read your context and instructions
    • Compiled the long task manager

    I have initialized a long-term task to manage this indexing process. Since this will exceed the current session duration, I've created a persistent state file and started the indexing script in the background.

    Task Initialization: search-index-20231027

    FieldValue
    Task IDsearch-index-20231027
    Statusactive
    Background SIDsession_8f2k9l
    Log File/home/user/logs/indexing.log
    Next ActionPoll background process and verify index.json generation

    Task Steps

    1. Inventory Files: done (5000 files found)
    2. Generate Index: in_progress (started background process)
    3. Validate Metadata: pending
    4. Finalize Artifacts: pending

    Next steps

    • I will monitor the background process session_8f2k9l every 15 minutes.
    • If the session resets, tell me "resume search-index-20231027" to continue.
    • I will notify you once the state transitions to done or if a blocker is detected.
    Poll background process and verify index.json

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

    About this skill

    Stop losing progress when your agent's session resets or hits context limits. This skill provides a durable filesystem-based state machine for tasks that span hours or days. It moves task tracking out of volatile session memory and into structured JSON records, ensuring your agent can pick up exactly where it left off after a crash, timeout, or manual reset.

    What it does

    • State persistence manages JSON records in a dedicated directory to track goals, steps, and progress across session resets.
    • Process lifecycle management handles background command execution, monitoring, and recovery for long-running scripts.
    • Resume protocol provides a standardized handshake for agents to re-acquire context and verify environmental state after an interruption.
    • Checkpointing system enforces atomic updates to task files, including artifact logging and next-action instructions.

    How it works

    1. Initialize the task using the bundled task_state.py helper to define the goal and discrete steps.
    2. Execute long-running work via background terminal sessions, immediately attaching the process ID to the task state.
    3. Checkpoint progress by updating step statuses and logging artifacts at natural intervals.
    4. Resume interrupted work by listing active tasks, verifying existing artifacts, and executing the stored next_action.

    Frameworks & tools

    This skill requires python3 for the state management script. It is designed to work with Unix-like and Windows environments where agents have access to terminal and process tool calls.

    Why this beats prompting it yourself

    Standard prompts rely on conversation history which is wiped during session resets or compressed during long tasks. This skill replaces "hopeful memory" with a concrete filesystem-based source of truth that survives process death and context clearing.

    Use cases

    • Large-scale data processing like rebuilding search indexes or crawling deep directories.
    • Multi-step research where information gathering spans multiple days.
    • Codebase migrations requiring long-running builds and validation suites.
    • Background monitoring of processes that outlive the active chat window.

    Known limitations

    Background processes do not survive host or gateway restarts. State files must use absolute paths to remain valid across changing session working directories.

    Background processes die on host restart. Requires absolute paths for artifacts. State files are per-profile.

    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 any payment.

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    Trust & safety

    Security scan

    Security scan passed 21 days ago

    Permissions

    Terminal / Shell
    Listed21 days ago

    What's inside

    Frequently Asked Questions

    This skill moves task tracking out of volatile conversation history and into durable JSON records on the filesystem, preventing 'amnesia' when an agent hits context limits or experiences a session timeout.

    The skill is compatible with any AI agent that has filesystem access and terminal execution capabilities, such as those running in VS Code, local development environments, or custom Python-based frameworks.

    You receive a set of optimized tool definitions for your agent, the `task_state.py` core management script, and comprehensive documentation on implementing the resume protocol.

    The skill includes a 'Process Lifecycle Management' feature that tracks PIDs (Process IDs) and logs background command output, allowing the agent to re-attach to and monitor a script that started in a previous session.

    Yes, the checkpointing system uses atomic JSON updates and absolute pathing to ensure that state files remain valid even if the agent's working directory or environment context changes between sessions.

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