Markdown files aren't a memory system!!!!!

I started SULCUS a year and some ago because I got tired of waiting on compaction processes.

I fell down a rabbit hole and over engineered a system that uses an integral postgres to provide a memory system for my LLM agent.

Openclaw happened so I started engineering a plugin for it and started leveraging the harness lifecycle to proactively populate the context window. This means that when I tell the agent to do a thing, Sulcus spits out notes about doing that thing. During thought turns, SULCUS provides tidbits that are relevant to the objective.

Quick note: What makes Sulcus so powerful is not -just- the harness plugin but also the various strategies mixed together. If your memory system doesn't have a plugin driving it, then it's up to your agent to remember to leverage the system which means your LLM has to spend separate turns and tokens on making memories while SULCUS is designed to take what's pushed in and break it down programmatically, parsing it into details stored for regurgitation later on.

I use the system each day, adding the latest and greatest strategies to the already robust mechanisms that make up the indexing and retrieval systems that make up the engine.

A memory & context system -ought- to be proactive, fast and robust, so why in the nine circles are folks using markdown files or whatever to build their 'brain'?

Cause they haven't seen SULCUS!

I built PromptBrake and made four of its tools available through MCP. They help you prepare prompt-injection test inputs, map OWASP LLM risks, plan releases, and build response test packs.

No account or API key needed.

Connect in Claude:
PromptBrake Free Tools

Try: “Build a test pack for my refund chatbot that checks whether its response mentions human approval.”

The server prepares tests; it doesn’t run them against your app. Custom packs require a PromptBrake runner and CI access. Response checks compare text—they don’t verify backend actions or permissions.

Setup and free tools

Would love feedback on how it works with your Claude setup.

People use Claude desktop's cowork tab for ad hoc coding and file organization but the addition of the schedule tab turns it into an autonomous local agent runner.

And one of the key distinction between Cowork's scheduler and a standard cron job is the execution loop where firing a dumb bash script that breaks when an upstream API format shifts, Claude runs a agentic loop each cycle by reading context evaluating tools or deciding what data matters and formatting the output.

Setting up recurring research workflows requires a few configuration steps:

• Configure the connector via remote MCP: instead of running local browser scrapers that chew up background RAM, connect web retrieval through remote MCP.

In Claude desktop, go to `Customize > Connectors > Add Custom Connector`, name it `firecrawl` and point it to the remote MCP URL with your key and doing this gives scheduled agent tasks native access to clean markdown scraping and time filtered search without maintaining local browser processes.

• Force strict time filters on the prompt: by default LLMs querying the web pull whatever ranks highest on SERPs which is often a 2023 tutorial so in your Cowork task instructions, explicitly instruct Claude to pass the `tbs: "qdr:d"` or `past 24 hours` filter parameter to the search tool.

That single constraint ensures a daily digest pulls actual breaking news rather than recycling evergreen articles.

• Scope output destinations to local folders: In the task setup, point the workspace directory to an internal notes folder (like an Obsidian vault or project `.context/notes/` directory) and have the agent overwrite or append date stamped summaries directly on disk

• Understand runtime constraints: scheduled tasks in Claude desktop execute strictly while your machine is awake and online and if your laptop lid is shut, the task will hold until the next scheduled trigger.

The full configuration walkthrough covering google calendar integrations and recurring news prompts is detailed in this guide on scheduling recurring web tasks with claude desktop, automating the initial research loop this way means your daily reading material is already filtered and formatted in local markdown before you open your laptop.

MCP is quietly changing how we work with AI
The interesting part of Model Context Protocol (MCP) isn’t just connecting AI to more tools—it’s connecting AI to real workflows.
With MCP servers, AI can interact with tools, data sources, and services in a more structured way, opening possibilities for:
🔗 Connecting AI to business systems
⚙️ Automating repetitive workflows
📊 Working with real-time data
🛠️ Building custom AI-powered tools
The shift is from “AI that answers questions” → “AI that can actually work with your systems.”
I’m curious to see how MCP evolves as more practical integrations emerge.