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    repo memory

    1

    Maintain project context and avoid repeating mistakes by logging task history in a versioned markdown file.

    Free

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    repo memory

    repo memory

    Example session with this skill installed

    I've finished refactoring the auth middleware. Update the repo memory.

    • Read your context and instructions
    • Compiled the repo memory

    I have updated the project memory in AI_MEMORY.md with the details of the authentication middleware refactor.

    Updated AI Memory Log

    
# AI Memory — server-core-api

## Purpose
A Node.js backend providing identity management and session-based authentication for the internal dashboard.

## Log
- 2024-05-20T14:30:05-0700 — [Done] Refactored auth middleware (— Moved JWT verification to a standalone service to reduce dependency bloating in the route handlers.)
- 2024-05-19T10:15:22-0700 — [Done] Fixed CORS headers for staging environment
- 2024-05-18T16:45:10-0700 — [Failed] Initial attempt at Redis session store (— Failed because the production Redis instance requires TLS 1.2, which was not configured in the client.)
- 2024-05-17T09:00:00-0700 — [Done] Initialized repository and base Express structure

    Next steps

    • Verify the timestamped entry in AI_MEMORY.md.
    • Commit the memory update along with your code changes to persist the context.

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

    About this skill

    Stop losing context between coding sessions. LLMs often forget the "why" behind a specific workaround or the reason a previous implementation failed. This skill maintains a local AI_MEMORY.md file in your repository root, ensuring that every agent who touches the code understands the project purpose, recent changes, and known pitfalls.

    What it does

    • State management by tracking task status as Done, Failed, or In Progress.
    • Context loading by summarizing project purpose and recent history at the start of a session.
    • Failure logging to prevent the agent from repeating known mistakes or anti-patterns.
    • Progress continuity by identifying unfinished work from previous sessions.
    • Git integration by prompting branch creation before edits if supported tools are present.

    How it works

    1. Initialize the session by reading or creating the AI_MEMORY.md file to ingest project context.
    2. Summarize the last 5-8 log entries and any failure patterns to provide immediate situational awareness.
    3. Log updates after completing, pausing, or failing a task by appending a timestamped entry.
    4. Verify the log entry with the user to ensure technical accuracy for future sessions.

    Frameworks & tools

    This skill works with any repository and requires a filesystem for reading and writing AI_MEMORY.md. It utilizes standard shell commands like date for timestamping.

    Why this beats prompting it yourself

    Manual handovers are often forgotten or inconsistent in format. This skill enforces a structured log that lives inside your version control, meaning the context travels with the code and is automatically ingested by the next agent without you having to explain the project history every time.

    Use cases

    • Documenting why a specific library version was pinned after a failed upgrade.
    • Handing off a half-finished feature to another agent or session.
    • Tracking root causes of intermittent bugs that are hard to replicate.
    • Keeping a project purpose statement visible for better architectural alignment.

    Known limitations

    Requires a project root AI_MEMORY.md file to function. It only summarizes the most recent 8 entries to stay within context limits.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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