OpenClaw Skill Server for Indian Market Analysis and Trading

OpenClaw Integration for Indian Market Analysis
OpenClawRadar has released an open-source trading terminal for Indian markets wired up as an OpenClaw skill server. Any OpenClaw agent can now pull Indian stock market data and run full analysis over HTTP without local installation.
How It Works
Users can type /analyze RELIANCE in Telegram and receive a full structured analysis with a trade plan in 3-4 minutes. The system uses seven specialist agents working in parallel:
- Technical (RSI, MACD, EMAs, Bollinger, ATR, pivot levels)
- Fundamental (PE, ROE, ROCE pulled from Screener.in)
- Options (Greeks, OI buildup, IV skew)
- News and Macro (reads current headlines and connects them to the stock)
- Sentiment (FII/DII flows, market breadth)
- Sector Rotation
- Risk Manager
Each agent returns a verdict and confidence score. Scores go into a weighted composite that flags disagreements explicitly rather than averaging into vague calls.
The system then runs a five-round debate: Bull argues, Bear argues, Bull rebuts, Bear rebuts, and a Facilitator summarizes. A Fund Manager agent reads the transcript and writes a final verdict with a trade plan including entry price, stop-loss, targets, and position sizing across three risk profiles (aggressive, neutral, conservative) calibrated to your capital.
The pipeline uses 8 LLM calls in standard mode and 11 in deep mode.
API Access
The same pipeline is available as an OpenClaw skill:
curl -X POST http://localhost:8765/skills/analyze -H "Content-Type: application/json" -d '{"symbol": "RELIANCE"}'This takes 30 to 90 seconds and returns the scorecard, debate summary, verdict, and all three trade plans.
OpenClaw Integration Benefits
The skill server publishes a discovery manifest at /.well-known/openclaw.json. Any OpenClaw agent fetches this once, reads the input schemas, and knows what it can call without hardcoding.
This enables agent chaining: one agent can monitor a watchlist and call quote every few minutes, then call analyze when something moves, check macro conditions with another agent, and push a Telegram message with the full picture.
Currently 17 skills are live: quotes, options chain, FII/DII flows, earnings calendar, macro snapshot, bulk and block deals, morning brief, backtesting, pairs analysis, session-aware chat, and price and technical alerts with webhook callbacks.
Current Limitations and Future Plans
Broker support is currently limited to Fyers only, which has a free developer API with real-time WebSocket data. Zerodha, Angel One, Upstox, and Groww are in progress. The broker interface uses a clean abstract class where adding a new broker mostly involves mapping their SDK to the system's data models.
Upcoming features include trading directly from Telegram and OpenClaw agents via a /trade RELIANCE command that shows the trade plan with Confirm/Cancel buttons. OpenClaw agents will be able to call analyze, read the plan, and call execute without buttons.
Future development includes custom strategy creation in plain English: users describe what they want, the system interviews about parameters, writes Python code, backtests on NSE history, and saves the strategy. A wealth management layer will watch entire portfolios rather than individual stocks.
📖 Read the full source: r/openclaw
👀 See Also

SpecLock: MCP Server for Enforcing AI Coding Constraints
SpecLock is an open-source MCP server that remembers project constraints across sessions and blocks AI coding agents from violating them. Claude independently tested it with 100 adversarial tests, scoring 100/100 with zero false positives and 15.7ms per check.

Interactive Website Simulates Claude Code Project Structure
A developer built exploreclaudecode.com, a browser-based simulation of a Claude Code project with a functional file tree, configurable files, and terminal panel. The site explains how .claude/ directories, settings files, skills, agents, hooks, and MCP configs work together.
PinchBench Ranks Qwen and Nemotron on Mac Studio M3 Ultra: Nemotron Super Hits 99.2% Coding
A developer ran six local models through PinchBench on a Mac Studio M3 Ultra with 256GB RAM. Nemotron-3-super topped coding at 99.2% but scored 78.2% overall; Qwen3.6 35B A3B led total score at 87.9%.

MoltPoker.xyz: Play-money Texas Hold'em for AI Agents
MoltPoker.xyz is a platform where AI agents can play No-Limit Texas Hold'em against each other using WebSocket connections, with replayable hands and visible agent reasoning during live games.