My AI Agent Project: What Im Learning Now

Hey everyone, Jake here from clawgo.net. It’s March 23rd, 2026, and if you’re anything like me, your inbox is probably overflowing with “AI this” and “agent that” headlines. It’s easy to feel like you’re drowning in a sea of hype, especially when you’re just trying to figure out how to get some actual work done. Forget the pie-in-the-sky promises for a minute. Today, I want to talk about something tangible, something I’ve been kicking the tires on for the last few weeks that’s actually making a difference in my day-to-day: using AI agents to wrangle my overflowing digital asset collection. Specifically, I’m talking about OpenClaw, and how I’ve set up a system to actually manage my ridiculous number of screenshots, code snippets, and half-baked design ideas.

My digital life is a mess. There, I said it. As a tech blogger, I’m constantly grabbing screenshots, downloading code examples, saving articles, and drafting notes. It all ends up in a handful of folders, usually named something like “Stuff,” “New Folder (2),” or “Desktop (final) (DO NOT DELETE).” When I actually need to find that perfect screenshot of an OpenClaw console output from three weeks ago, or that Python script I cobbled together to parse some JSON, it’s a scavenger hunt. I’ve tried all the usual suspects: cloud storage with built-in search, desktop search tools, even just religiously tagging everything. Nothing ever sticks. Until now.

The Mess That Needed Taming: My Digital Hoard

Let’s be real. My “system” involved:

• A “Downloads” folder that was essentially a black hole.
• A “Screenshots” folder with hundreds of PNGs named like “Screenshot 2026-03-01 at 10.34.12 AM.png.”
• Various project folders, each with their own mini-downloads and screenshots folders.
• An Evernote account full of notes I never reread.
• A Google Drive with files organized… by Google.

The problem isn’t just finding things; it’s the cognitive load of knowing where to even start looking. It’s the mental friction that stops me from reusing good content, or even remembering I had it in the first place.

Why OpenClaw? My Agent-Based Approach

I’ve played with a few different agent frameworks, but OpenClaw really clicked for me because of its emphasis on modularity and local control. I didn’t want my entire digital life getting uploaded to some third-party service. I wanted something that ran on my machine, could access my local files, and was flexible enough to adapt to my weird, specific needs. OpenClaw’s ability to define custom “skills” and orchestrate them with simple YAML configurations felt like exactly what I needed.

My core idea was simple: an agent that could watch specific folders, identify new files, understand what they were (image, code, document), and then move them to an organized structure while also extracting metadata and making them searchable. Think of it as a super-powered digital butler for my files.

Building the “Digital Archivist” Agent

Here’s how I broke it down:

1. The Watcher Skill: This skill’s job is just to notice when new files appear in my “incoming” folders (Downloads, Desktop, Screenshots).
2. The Classifier Skill: Once a new file is detected, this skill uses a local LLM (I’m running a quantized version of Llama 3 on my desktop) to determine the file type and content. For images, it tries to describe what’s in the image. For code, it identifies the language and tries its purpose. For documents, it extracts keywords.
3. The Mover Skill: Based on the classification, this skill moves the file to an appropriate, structured folder (e.g., ~/Archive/Screenshots/2026/March/ or ~/Archive/Code/Python/).
4. The Indexer Skill: This is the crucial part. It takes all the extracted metadata (description, keywords, file path, date) and shoves it into a local SQLite database, making it searchable.

It sounds complex, but with OpenClaw, defining these as separate, reusable skills and then orchestrating them was surprisingly straightforward. Here’s a simplified version of my agent_config.yaml:

agent_name: DigitalArchivist
description: An agent to automatically organize and index digital assets.

skills:
 - name: FileWatcher
 module: local_skills.file_management
 function: watch_directory
 config:
 directories: ["/Users/jake/Downloads", "/Users/jake/Desktop/temp_incoming"]
 interval_seconds: 60

 - name: FileClassifier
 module: local_skills.classification
 function: classify_and_extract
 dependencies: [FileWatcher]
 config:
 llm_model_path: "/Users/jake/llm_models/llama3-8b-quant.gguf"
 image_processor_endpoint: "http://localhost:8000/image_describe" # A local API for image captioning

 - name: FileMover
 module: local_skills.file_management
 function: move_file_to_archive
 dependencies: [FileClassifier]
 config:
 archive_root: "/Users/jake/Archive"

 - name: MetadataIndexer
 module: local_skills.indexing
 function: index_metadata
 dependencies: [FileMover]
 config:
 db_path: "/Users/jake/Archive/metadata.db"

tasks:
 - name: ProcessNewFiles
 steps:
 - skill: FileWatcher
 output_key: new_files
 - skill: FileClassifier
 input_key: new_files
 output_key: classified_data
 - skill: FileMover
 input_key: classified_data
 output_key: moved_files
 - skill: MetadataIndexer
 input_key: classified_data

And here’s a peek at a simplified version of the classify_and_extract function from local_skills/classification.py. This is where the local LLM comes in for text-based files:

import os
from llama_cpp import Llama
import mimetypes

def classify_and_extract(file_path, llm_model_path, image_processor_endpoint=None):
 llm = Llama(model_path=llm_model_path, n_ctx=2048, n_batch=512)
 mime_type, _ = mimetypes.guess_type(file_path)
 file_extension = os.path.splitext(file_path)[1].lower()

 if mime_type and mime_type.startswith('image'):
 # In a real scenario, this would call the image_processor_endpoint
 # For simplicity, we'll just mock a description here
 description = f"Image file: {os.path.basename(file_path)}. Likely a screenshot."
 keywords = ["image", "screenshot", "visual"]
 file_type = "image"
 elif file_extension in ['.py', '.js', '.html', '.css', '.md', '.txt']:
 with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
 content = f.read(2000) # Read first 2KB for classification
 
 prompt = f"Analyze the following text content and determine its type (e.g., Python code, JavaScript, Markdown, plain text). Then, extract 3-5 keywords and provide a brief summary of its likely purpose. Format as JSON.\n\nContent:\n{content}\n\nJSON:"
 
 output = llm(prompt, max_tokens=200, stop=["\n\n"], echo=False)
 try:
 parsed_output = json.loads(output['choices'][0]['text'])
 description = parsed_output.get('summary', 'No summary provided.')
 keywords = parsed_output.get('keywords', [])
 file_type = parsed_output.get('type', 'document')
 except json.JSONDecodeError:
 description = "Could not parse LLM output. Generic document."
 keywords = ["document", "unclassified"]
 file_type = "document"
 else:
 description = f"Generic file: {os.path.basename(file_path)}"
 keywords = ["generic", "unclassified"]
 file_type = "other"

 return {
 "file_path": file_path,
 "description": description,
 "keywords": keywords,
 "file_type": file_type,
 "timestamp": os.path.getmtime(file_path)
 }

The image_processor_endpoint is a separate, small FastAPI service I’m running locally that uses a fine-tuned CLIP model for image captioning. It’s a bit beyond the scope of this article, but it’s a great example of how you can integrate specialized local models with OpenClaw agents.

The Results: Less Clutter, More Focus

After letting this agent run for a couple of weeks, the difference is night and day. My Downloads folder isn’t a graveyard anymore. Every new file I drop onto my desktop or save from a browser gets processed within minutes. I now have a beautifully organized ~/Archive directory, with subfolders for year, month, and content type. It’s not just about the organization, though.

The real magic is the searchable database. I built a tiny web UI (another small FastAPI app) that queries the SQLite database. Now, if I need that “OpenClaw console output” screenshot, I just type “OpenClaw console output” into my search bar, and boom, there it is, along with the file path and a link to open it. If I’m looking for “Python script to parse JSON,” I get a list of all relevant scripts, complete with their LLM-generated summaries. It’s like having a personal librarian for my digital mess.

A Small Setback, A Quick Fix

One challenge I ran into early on was with very large files. The LLM would sometimes choke trying to process massive text files. My solution was to add a file size check in the FileClassifier and, for anything over a certain threshold (say, 5MB), only extract basic metadata like filename, type, and creation date, rather than attempting a full content analysis. It’s a pragmatic trade-off: better to have some metadata than no metadata at all because the agent crashed.

Actionable Takeaways

If you’re drowning in digital clutter like I was, here’s how you can start building your own digital archivist:

1. Identify Your Pain Points: What specific types of files do you struggle to organize? Where do they usually end up? For me, it was screenshots and code snippets.
2. Start Small with OpenClaw: Don’t try to build the ultimate agent all at once. Begin with a single skill, like just watching a folder. Get that working.
3. use Local LLMs (or APIs): For classification and summarization, a local LLM can be incredibly powerful and offers privacy. If local isn’t an option, consider a private API endpoint for specific tasks.
4. Define Clear Skills: Break down your desired workflow into discrete, manageable skills. This makes your agent easier to debug and extend.
5. Build a Search Layer: The organization is great, but true utility comes from being able to find things again. A simple SQLite database and a basic search interface can make all the difference.
6. Iterate and Refine: My agent isn’t perfect, and I’m constantly tweaking the classification prompts and adding new skills (like auto-deleting temporary files after processing). AI agents are living systems; they get better with use and refinement.

This isn’t about magical “AI takes over my life” scenarios. This is about using smart tools to solve real, everyday problems. My OpenClaw agent isn’t just organizing files; it’s freeing up mental energy, reducing frustration, and ultimately, making me more efficient. And that, in my book, is a huge win.

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🕒 Last updated: March 26, 2026  ·  Originally published: March 22, 2026