Personal Productivity Agent

Created By
mwill20a year ago
Personal Productivity Agent (Python) for Windows. Automates tasks, provides system info, and uses Google Gemini for intelligent Windows Event Log analysis. Supports interactive CLI and MCP server mode.
Overview

What is Personal Productivity Agent?

The Personal Productivity Agent is a Python-based tool designed to assist with various tasks on a Windows system, leveraging Large Language Models (LLMs) for intelligent analysis of Windows Event Logs.

How to use Personal Productivity Agent?

To use the agent, you can run it in two modes:

  1. Interactive CLI Mode: Execute python main_agent.py in your command prompt for a menu-driven interface.
  2. MCP Server Mode: Execute python main_agent.py --mcp-mode for programmatic integration, allowing other applications to send JSON requests.

Key features of Personal Productivity Agent?

  • Interactive file system operations (find, move, rename files)
  • System information retrieval (disk usage, memory, CPU info)
  • Productivity tools (document summarization, calendar events, reminders)
  • Windows Event Log analysis using Google Gemini LLM for insights and troubleshooting.

Use cases of Personal Productivity Agent?

  1. Automating file management tasks on Windows.
  2. Analyzing Windows Event Logs for system diagnostics.
  3. Setting reminders and notifications for important tasks.

FAQ from Personal Productivity Agent?

  • What programming language is used?

The agent is developed in Python 3.x.

  • Is there an API key required?

Yes, for LLM features, you need to set a GEMINI_API_KEY environment variable.

  • Can it run in the background?

Yes, it can operate in MCP server mode for background processing.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
mwill20
Star
0
Language
Python
License
-

Recommend Servers

View All
AI Work Market — USDC settlement rails for AI labor on Base Mainnet)
@Dario (DME)

AI Work Market is a USDC escrow protocol on Base Mainnet, designed for autonomous AI agents to find work, post jobs, and settle payments without humans in the loop. This MCP server exposes 10 tools: **Escrow lifecycle** - `create_intent_quote` — get calldata + gas estimate for funding a new escrow intent - `submit_proof_quote` — get calldata for the seller to submit a proof URI - `release_funds_quote` — get calldata for the buyer to release payment (or claim/refund) **x402 single-call binding** - `x402_consume` — replaces the 5-step x402 flow with one HMAC-signed POST that returns a delivery URL **Onboarding & discovery** - `agent_onboard` — generate a signed agent card with marketplace attestation - `agent_search` — tf-idf search over the live agent catalog - `agent_reputation` — server-side reputation from on-chain Released/Refunded/Disputed events **Live state** - `system_status` — live on-chain state (nextIntentId, accumulatedFees, contract balance, owner) - `escrow_rules` — contract semantics, lifecycle, call guides, failure modes - `events_subscribe` — SSE stream of new on-chain intent events All endpoints are serverless (Vercel) and return their schema on GET. No browser, no wallet UI required for an agent to integrate. The protocol takes a 1% commission on every settlement; the rest goes to the seller. The full AgentCard is at `/.well-known/agent-card.json` (A2A-compatible). The OpenAPI 3.0.3 spec is at `/.well-known/openapi.json` with `components.securitySchemes` (none, hmacX402). `robots.txt` allows GPTBot, ClaudeBot, anthropic-ai, PerplexityBot, Google-Extended, Applebot-Extended, CCBot, Amazonbot.

7 hours ago
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago