India Financial Freedom Calculator using OpenClaw

This is a submission for the OpenClaw Challenge.

What I Built

I built the India Financial Freedom (FIRE) Skill for OpenClaw. This tool allows AI agents to provide hyper-localized retirement planning for the Indian market. Unlike standard global calculators, this skill accounts for:

• Inflation-Adjusted Planning: Factoring in India's ~6% average inflation rate.
• Localized Safe Withdrawal Rate (SWR): Using a conservative 3% rule (33x multiplier) which is safer for the Indian economic context than the traditional US-based 4% rule.
• Equity Growth: Calculating SIP (Systematic Investment Plan) projections based on a 12% Indian equity market average.

Tech Stack: Python 3.10+, OpenClaw SDK, YAML, and python-dotenv for secure environment management.

How I Used OpenClaw

OpenClaw acts as the "intelligent bridge" between a user's natural language and my financial logic. Here is how the integration works:

1. Skill Definition: I created a custom class IndiaFireSkill that inherits from the OpenClaw base. This defines the metadata (parameters like monthly_expenses, current_savings) that the AI needs to look for.
2. The Gateway: I utilized the OpenClaw Gateway to host the skill locally. This allowed the AI agent to "see" my Python function as a callable tool.
3. Intent Extraction: When a user asks, "I'm 28 and spend 60k a month, can I retire by 50?", OpenClaw automatically extracts the numbers, handles the units, and passes them to my logic.py engine.
4. Actionable Output: Instead of a simple number, OpenClaw wraps my skill's output into a conversational response, telling the user exactly what their "shortfall" is and how much more they need to invest via SIPs to reach their goal.

Demo

GitHub Repo: https://github.com/NatarajanVijaikumar/openclaw-financial-freedom-india

What I Learned

Building this skill taught me the importance of Type Validation in AI-agent workflows. Initially, the LLM was passing numbers as strings, which crashed the math logic. I learned to use explicit float/int conversion within the execute method of the OpenClaw skill.

I also faced a challenge with the launchd background service on macOS, which taught me how to manage persistent gateway processes and debug port conflicts using launchctl.

ClawCon Michigan

participated in this challenge remotely! While I couldn't make it to Michigan in person, following the updates and building on the OpenClaw framework has been an incredible way to engage with the AI developer community.