genlayer-dev-claw-skill
Build GenLayer Intelligent Contracts - Python smart contracts with LLM calls and web access. Use for writing/deploying contracts, SDK reference, CLI commands, equivalence principles, storage types. Triggers: write intelligent contract, genlayer contract, genvm, gl.Contract, deploy genlayer, genlayer CLI, genlayer SDK, DynArray, TreeMap, gl.nondet, gl.eq_principle, prompt_comparative, strict_eq, genlayer deploy, genlayer up. (For explaining GenLayer concepts, use genlayer-claw-skill instead.)
Best use case
genlayer-dev-claw-skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Build GenLayer Intelligent Contracts - Python smart contracts with LLM calls and web access. Use for writing/deploying contracts, SDK reference, CLI commands, equivalence principles, storage types. Triggers: write intelligent contract, genlayer contract, genvm, gl.Contract, deploy genlayer, genlayer CLI, genlayer SDK, DynArray, TreeMap, gl.nondet, gl.eq_principle, prompt_comparative, strict_eq, genlayer deploy, genlayer up. (For explaining GenLayer concepts, use genlayer-claw-skill instead.)
Teams using genlayer-dev-claw-skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/genlayer-dev/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How genlayer-dev-claw-skill Compares
| Feature / Agent | genlayer-dev-claw-skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Build GenLayer Intelligent Contracts - Python smart contracts with LLM calls and web access. Use for writing/deploying contracts, SDK reference, CLI commands, equivalence principles, storage types. Triggers: write intelligent contract, genlayer contract, genvm, gl.Contract, deploy genlayer, genlayer CLI, genlayer SDK, DynArray, TreeMap, gl.nondet, gl.eq_principle, prompt_comparative, strict_eq, genlayer deploy, genlayer up. (For explaining GenLayer concepts, use genlayer-claw-skill instead.)
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# GenLayer Intelligent Contracts
GenLayer enables **Intelligent Contracts** - Python smart contracts that can call LLMs, fetch web data, and handle non-deterministic operations while maintaining blockchain consensus.
## Quick Start
### Minimal Contract
```python
# v0.1.0
# { "Depends": "py-genlayer:latest" }
from genlayer import *
class MyContract(gl.Contract):
value: str
def __init__(self, initial: str):
self.value = initial
@gl.public.view
def get_value(self) -> str:
return self.value
@gl.public.write
def set_value(self, new_value: str) -> None:
self.value = new_value
```
### Contract with LLM
```python
# v0.1.0
# { "Depends": "py-genlayer:latest" }
from genlayer import *
import json
class AIContract(gl.Contract):
result: str
def __init__(self):
self.result = ""
@gl.public.write
def analyze(self, text: str) -> None:
prompt = f"Analyze this text and respond with JSON: {text}"
def get_analysis():
return gl.nondet.exec_prompt(prompt)
# All validators must get the same result
self.result = gl.eq_principle.strict_eq(get_analysis)
@gl.public.view
def get_result(self) -> str:
return self.result
```
### Contract with Web Access
```python
# v0.1.0
# { "Depends": "py-genlayer:latest" }
from genlayer import *
class WebContract(gl.Contract):
content: str
def __init__(self):
self.content = ""
@gl.public.write
def fetch(self, url: str) -> None:
url_copy = url # Capture for closure
def get_page():
return gl.nondet.web.render(url_copy, mode="text")
self.content = gl.eq_principle.strict_eq(get_page)
@gl.public.view
def get_content(self) -> str:
return self.content
```
## Core Concepts
### Contract Structure
1. **Version header**: `# v0.1.0` (required)
2. **Dependencies**: `# { "Depends": "py-genlayer:latest" }`
3. **Import**: `from genlayer import *`
4. **Class**: Extend `gl.Contract` (only ONE per file)
5. **State**: Class-level typed attributes
6. **Constructor**: `__init__` (not public)
7. **Methods**: Decorated with `@gl.public.view` or `@gl.public.write`
### Method Decorators
| Decorator | Purpose | Can Modify State |
|-----------|---------|------------------|
| `@gl.public.view` | Read-only queries | No |
| `@gl.public.write` | State mutations | Yes |
| `@gl.public.write.payable` | Receive value + mutate | Yes |
### Storage Types
Replace standard Python types with GenVM storage-compatible types:
| Python Type | GenVM Type | Usage |
|-------------|------------|-------|
| `int` | `u32`, `u64`, `u256`, `i32`, `i64`, etc. | Sized integers |
| `int` (unbounded) | `bigint` | Arbitrary precision (avoid) |
| `list[T]` | `DynArray[T]` | Dynamic arrays |
| `dict[K,V]` | `TreeMap[K,V]` | Ordered maps |
| `str` | `str` | Strings (unchanged) |
| `bool` | `bool` | Booleans (unchanged) |
**⚠️ `int` is NOT supported!** Always use sized integers.
### Address Type
```python
# Creating addresses
addr = Address("0x03FB09251eC05ee9Ca36c98644070B89111D4b3F")
# Get sender
sender = gl.message.sender_address
# Conversions
hex_str = addr.as_hex # "0x03FB..."
bytes_val = addr.as_bytes # bytes
```
### Custom Data Types
```python
from dataclasses import dataclass
@allow_storage
@dataclass
class UserData:
name: str
balance: u256
active: bool
class MyContract(gl.Contract):
users: TreeMap[Address, UserData]
```
## Non-Deterministic Operations
### The Problem
LLMs and web fetches produce different results across validators. GenLayer solves this with the **Equivalence Principle**.
### Equivalence Principles
#### 1. Strict Equality (`strict_eq`)
All validators must produce **identical** results.
```python
def get_data():
return gl.nondet.web.render(url, mode="text")
result = gl.eq_principle.strict_eq(get_data)
```
Best for: Factual data, boolean results, exact matches.
#### 2. Prompt Comparative (`prompt_comparative`)
LLM compares leader's result against validators' results using criteria.
```python
def get_analysis():
return gl.nondet.exec_prompt(prompt)
result = gl.eq_principle.prompt_comparative(
get_analysis,
"The sentiment classification must match"
)
```
Best for: LLM tasks where semantic equivalence matters.
#### 3. Prompt Non-Comparative (`prompt_non_comparative`)
Validators verify the leader's result meets criteria (don't re-execute).
```python
result = gl.eq_principle.prompt_non_comparative(
lambda: input_data, # What to process
task="Summarize the key points",
criteria="Summary must be under 100 words and factually accurate"
)
```
Best for: Expensive operations, subjective tasks.
#### 4. Custom Leader/Validator Pattern
```python
result = gl.vm.run_nondet(
leader=lambda: expensive_computation(),
validator=lambda leader_result: verify(leader_result)
)
```
### Non-Deterministic Functions
| Function | Purpose |
|----------|---------|
| `gl.nondet.exec_prompt(prompt)` | Execute LLM prompt |
| `gl.nondet.web.render(url, mode)` | Fetch web page (`mode="text"` or `"html"`) |
**⚠️ Rules:**
- Must be called inside equivalence principle functions
- Cannot access storage directly
- Copy storage data to memory first with `gl.storage.copy_to_memory()`
## Contract Interactions
### Call Other Contracts
```python
# Dynamic typing
other = gl.get_contract_at(Address("0x..."))
result = other.view().some_method()
# Static typing (better IDE support)
@gl.contract_interface
class TokenInterface:
class View:
def balance_of(self, owner: Address) -> u256: ...
class Write:
def transfer(self, to: Address, amount: u256) -> bool: ...
token = TokenInterface(Address("0x..."))
balance = token.view().balance_of(my_address)
```
### Emit Messages (Async Calls)
```python
other = gl.get_contract_at(addr)
other.emit(on='accepted').update_status("active")
other.emit(on='finalized').confirm_transaction()
```
### Deploy Contracts
```python
child_addr = gl.deploy_contract(code=contract_code, salt=u256(1))
```
### EVM Interop
```python
@gl.evm.contract_interface
class ERC20:
class View:
def balance_of(self, owner: Address) -> u256: ...
class Write:
def transfer(self, to: Address, amount: u256) -> bool: ...
token = ERC20(evm_address)
balance = token.view().balance_of(addr)
token.emit().transfer(recipient, u256(100)) # Messages only on finality
```
## CLI Commands
### Setup
```bash
npm install -g genlayer
genlayer init # Download components
genlayer up # Start local network
```
### Deployment
```bash
# Direct deploy
genlayer deploy --contract my_contract.py
# With constructor args
genlayer deploy --contract my_contract.py --args "Hello" 42
# To testnet
genlayer network set testnet-asimov
genlayer deploy --contract my_contract.py
```
### Interaction
```bash
# Read (view methods)
genlayer call --address 0x... --function get_value
# Write
genlayer write --address 0x... --function set_value --args "new_value"
# Get schema
genlayer schema --address 0x...
# Check transaction
genlayer receipt --tx-hash 0x...
```
### Networks
```bash
genlayer network # Show current
genlayer network list # Available networks
genlayer network set localnet # Local dev
genlayer network set studionet # Hosted dev
genlayer network set testnet-asimov # Testnet
```
## Best Practices
### Prompt Engineering
```python
prompt = f"""
Analyze this text and classify the sentiment.
Text: {text}
Respond using ONLY this JSON format:
{{"sentiment": "positive" | "negative" | "neutral", "confidence": float}}
Output ONLY valid JSON, no other text.
"""
```
### Security: Prompt Injection
- **Restrict inputs**: Minimize user-controlled text in prompts
- **Restrict outputs**: Define exact output formats
- **Validate**: Check parsed results match expected schema
- **Simplify logic**: Clear contract flow reduces attack surface
### Error Handling
```python
from genlayer import UserError
@gl.public.write
def safe_operation(self, value: int) -> None:
if value <= 0:
raise UserError("Value must be positive")
# ... proceed
```
### Memory Management
```python
# Copy storage to memory for non-det blocks
data_copy = gl.storage.copy_to_memory(self.some_data)
def process():
return gl.nondet.exec_prompt(f"Process: {data_copy}")
result = gl.eq_principle.strict_eq(process)
```
## Common Patterns
### Token with AI Transfer Validation
See `references/examples.md` → LLM ERC20
### Prediction Market
See `references/examples.md` → Football Prediction Market
### Vector Search / Embeddings
See `references/examples.md` → Log Indexer
## Debugging
1. **GenLayer Studio**: Use `genlayer up` for local testing
2. **Logs**: Filter by transaction hash, debug level
3. **Print statements**: `print()` works in contracts (debug only)
## Reference Files
- `references/sdk-api.md` - Complete SDK API reference
- `references/equivalence-principles.md` - Consensus patterns in depth
- `references/examples.md` - Full annotated contract examples (incl. production oracle)
- `references/deployment.md` - CLI, networks, deployment workflow
- `references/genvm-internals.md` - VM architecture, storage, ABI details
## Links
- Docs: https://docs.genlayer.com
- SDK: https://sdk.genlayer.com
- Studio: https://studio.genlayer.com
- GitHub: https://github.com/genlayerlabsRelated Skills
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