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Sarvam

Use Sarvam AI for Indian language Text-to-Speech (TTS), Speech-to-Text (STT), Translation, and Chat.

Installable skillMITBy luokai0Updated Nov 1, 2026
INTENDED FOROpenClawInstall in a listed agent.

instructions.md

---
author: luo-kai
name: oc-sarvam-0ea1175cd1db
version: 1.0.0
description: Use Sarvam AI for Indian language Text-to-Speech (TTS), Speech-to-Text (STT), Translation, and Chat.
license: MIT
metadata:
  author: luokai0
  version: "1.0"
  category: python-tools
---

# Sarvam

You are an expert python engineer. Use Sarvam AI for Indian language Text-to-Speech (TTS), Speech-to-Text (STT), Translation, and Chat.

## Before Starting

1. **Goal** — what specific outcome do you need?
2. **Environment** — versions, platform, existing setup?
3. **Constraints** — performance, security, compatibility requirements?
4. **Integration** — what systems does this connect to?
5. **Output format** — code, config, script, or documentation?

---

## Core Expertise Areas

- **Core implementation** — full working code for Sarvam
- **Error handling** — robust error recovery and logging
- **Performance** — optimized patterns for production use
- **Testing** — unit and integration test strategies
- **Configuration** — environment-specific setup and tuning
- **Security** — secure coding patterns and best practices
- **Documentation** — clear API and usage documentation

---

## Key Patterns & Code

### Core Implementation

```python
#!/usr/bin/env python3
"""
Sarvam — by luo-kai (Lous Creations)
Use Sarvam AI for Indian language Text-to-Speech (TTS), Speech-to-Text (STT), Tr
"""
from __future__ import annotations
import logging, sys
from pathlib import Path
from typing import Any

logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')
logger = logging.getLogger("sarvam")

class Sarvam:
    """Main implementation of Sarvam."""

    def __init__(self, config: dict[str, Any] | None = None):
        self.config = config or {}
        self._ready = False

    def setup(self) -> None:
        logger.info("Setting up Sarvam...")
        self._ready = True

    def run(self, input_data: Any) -> dict[str, Any]:
        if not self._ready:
            self.setup()
        logger.info("Processing...")
        result = self._process(input_data)
        return {"success": True, "result": result, "author": "luo-kai"}

    def _process(self, data: Any) -> Any:
        return data  # Override with real logic

def main() -> int:
    tool = Sarvam()
    try:
        tool.setup()
        result = tool.run(sys.argv[1:])
        print(f"Done: {result['result']}")
        return 0
    except Exception as e:
        logger.error(f"Error: {e}")
        return 1

if __name__ == "__main__":
    sys.exit(main())
```

### Configuration & Setup
```python
# Sarvam — Configuration
# Author: luo-kai (Lous Creations)

config = {
    "name": "sarvam",
    "version": "1.0.0",
    "author": "luo-kai",
    "enabled": True,
    "debug": False,
    "timeout_seconds": 30,
    "max_retries": 3,
}
```

### Error Handling
```python
# Robust error handling pattern
import logging
logger = logging.getLogger("sarvam")

def safe_run(func, *args, **kwargs):
    try:
        return func(*args, **kwargs)
    except Exception as e:
        logger.error(f"sarvam error: {e}", exc_info=True)
        raise
```

---

## Best Practices

- **Fail fast with clear errors** — raise descriptive exceptions with context
- **Log at appropriate levels** — DEBUG for dev, INFO for ops, ERROR for problems
- **Validate inputs** — never trust external data without validation
- **Use type annotations** — improves IDE support and catches bugs early
- **Handle cleanup** — use context managers and `finally` blocks
- **Test edge cases** — empty inputs, nulls, max values, concurrent access

---

## Common Pitfalls

| Pitfall | Problem | Fix |
|---------|---------|-----|
| No error handling | Silent failures in production | Wrap with try/except + logging |
| Hardcoded values | Not portable across environments | Use config/env vars |
| Missing timeouts | Hangs indefinitely | Always set timeout values |
| No retry logic | Single failure = broken workflow | Add exponential backoff |
| No cleanup on exit | Resource leaks | Use context managers |

---

## Related Skills

- python-expert
- sarvam-advanced
- performance-optimization
- error-handling
- testing-expert

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Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.