Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Genlayer Dev Claw Skill
Write, deploy, and interact with GenLayer Python smart contracts featuring LLM calls, web access, and blockchain-consensus-safe non-determinism.
skill openclawclawhub Free
Write, deploy, and interact with GenLayer Python smart contracts featuring LLM calls, web access, and blockchain-consensus-safe non-determinism.
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0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- CHANGELOG.md, README.md, SKILL.md, references/deployment.md, references/equivalence-principles.md, references/examples.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
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- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- acastellana
- Source page
- View original listing
- Canonical URL
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Documentation
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.
Minimal Contract
# 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
# 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
# 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
Contract Structure
Version header: # v0.1.0 (required) Dependencies: # { "Depends": "py-genlayer:latest" } Import: from genlayer import * Class: Extend gl.Contract (only ONE per file) State: Class-level typed attributes Constructor: __init__ (not public) Methods: Decorated with @gl.public.view or @gl.public.write
Method Decorators
DecoratorPurposeCan Modify State@gl.public.viewRead-only queriesNo@gl.public.writeState mutationsYes@gl.public.write.payableReceive value + mutateYes
Storage Types
Replace standard Python types with GenVM storage-compatible types: Python TypeGenVM TypeUsageintu32, u64, u256, i32, i64, etc.Sized integersint (unbounded)bigintArbitrary precision (avoid)list[T]DynArray[T]Dynamic arraysdict[K,V]TreeMap[K,V]Ordered mapsstrstrStrings (unchanged)boolboolBooleans (unchanged) β οΈ int is NOT supported! Always use sized integers.
Address Type
# 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
from dataclasses import dataclass @allow_storage @dataclass class UserData: name: str balance: u256 active: bool class MyContract(gl.Contract): users: TreeMap[Address, UserData]
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. 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. 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). 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 result = gl.vm.run_nondet( leader=lambda: expensive_computation(), validator=lambda leader_result: verify(leader_result) )
Non-Deterministic Functions
FunctionPurposegl.nondet.exec_prompt(prompt)Execute LLM promptgl.nondet.web.render(url, mode)Fetch web page (mode="text" or "html") β οΈ Rules: Must be called inside equivalence principle functions Cannot access storage directly Copy storage data to memory first with gl.storage.copy_to_memory()
Call Other Contracts
# 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)
other = gl.get_contract_at(addr) other.emit(on='accepted').update_status("active") other.emit(on='finalized').confirm_transaction()
Deploy Contracts
child_addr = gl.deploy_contract(code=contract_code, salt=u256(1))
EVM Interop
@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
Setup
npm install -g genlayer genlayer init # Download components genlayer up # Start local network
Deployment
# 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
# 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
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
Prompt Engineering
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
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
# 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)
Token with AI Transfer Validation
See references/examples.md β LLM ERC20
Prediction Market
See references/examples.md β Football Prediction Market
Vector Search / Embeddings
See references/examples.md β Log Indexer
Debugging
GenLayer Studio: Use genlayer up for local testing Logs: Filter by transaction hash, debug level 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/genlayerlabs
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- CHANGELOG.md
- README.md
- references/deployment.md
- references/equivalence-principles.md
- references/examples.md