Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+22 more

    memory

    1

    Maintain a durable, agent-led project wiki and architecture map using a linked Markdown knowledge graph.

    $5

    /mo

    Subscription · cancel anytime

    Secure checkout via Stripe

    • Always the latest version
    • Delivered live to your agent via MCP
    • Cancel anytime, access ends at period end
    Security scanned
    memory

    memory

    Example session with this skill installed

    Initialize project memory for this repository and ingest the README.md and server.ts files to identify the core architecture.

    • Read your context and instructions
    • Compiled the memory

    I have initialized the project memory. I analyzed the repository structure, README.md, and server.ts to establish a baseline for your project's architecture and durable knowledge.

    Project Memory Initialized

    I created MEMORY.md at the root and synthesized the initial system architecture in docs/ARCHITECTURE.md.

    Created Files

    • MEMORY.md: The central index for all project knowledge.
    • docs/ARCHITECTURE.md: High-level overview of system boundaries and components.

    Initial Knowledge Synthesis

    DocumentDescription
    docs/ARCHITECTURE.mdMaps the Express.js server entry point, middleware stack, and SQLite integration.
    README.md (Source)Verified build instructions and environment requirements.
    server.ts (Source)Identified primary API routes and authentication flow logic.

    Architecture Overview

    The system is a TypeScript-based REST API. I have recorded the following core components:

    • Entry Point: server.ts handles lifecycle and middleware registration.
    • Data Flow: Request -> Auth Middleware -> Route Handler -> SQLite Persistence.
    • Constraints: Uses synchronous file-based logging which may impact high-load performance.

    Next steps

    1. Review docs/ARCHITECTURE.md to confirm the component boundaries are accurate.
    2. Provide any internal design docs or RFCs to enrich the decision log.
    3. Run an ingestion pass on the /routes directory to map specific API endpoints.
    $5/mo

    Subscription · always the latest version

    • Secure checkout via Stripe
    • Cancel anytime
    • Delivered live via MCP (optional)

    About this skill

    Context drift and forgotten architectural decisions stall development. This skill implements a durable project wiki through a MEMORY.md index, ensuring your agent retains high-level system knowledge across sessions. It transforms raw source code and documentation into a synthesized, linked knowledge graph rather than just summarizing files.

    What it does

    • Knowledge synthesis processes untrusted source material into durable architectural facts and decisions.
    • Durable indexing maintains a MEMORY.md root to map the entire project knowledge base.
    • Architecture mapping generates and updates docs/ARCHITECTURE.md to track system boundaries and data flows.
    • Automated updates detects when code changes affect documented behavior and prompts for memory revisions.
    • Source provenance maintains links to original files, issues, and URLs to verify claims.

    How it works

    1. Initialize creates the MEMORY.md root and architecture overview by inspecting manifests and directory structures.
    2. Ingest reads specific sources to extract concepts, relationships, and entities while ignoring embedded prompt-injection risks.
    3. Recall traverses the memory graph to provide answers grounded in documented project history.
    4. Audit identifies broken links, stale summaries, and unsupported assertions across the knowledge base.

    Frameworks & tools

    This skill is framework-agnostic and operates on standard Markdown files. It integrates with any Git-based workflow to track history without duplicating logs.

    Why this beats prompting it yourself

    Manual prompting often results in disconnected summaries that get lost in chat history. This skill enforces a strict hierarchical structure and cross-linking logic that preserves context across long-running projects, preventing the "forgetting" effect of context window limits.

    Use cases

    • Onboarding agents to complex legacy codebases without manual documentation.
    • Tracking cross-component architectural decisions during rapid refactoring.
    • Auditing existing documentation for contradictions and missing provenance.
    • Maintaining a live architecture diagram through synthesized Markdown notes.

    Known limitations

    Does not store secrets or sensitive credentials. Relies on Markdown for storage, so it cannot index non-textual assets directly. Requires an initial indexing pass to be effective.

    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.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill passes our trust checks below.

    Security scanned

    Passed our 8-point scan before listing

    Fresh listing

    Recently published to Agensi

    Free forever

    No account required to browse

    Trust & safety

    Security scanned

    Verified clean 6 days ago

    Listed6 days ago

    What's inside

    Frequently Asked Questions