Write production-quality GenLayer intelligent contracts. Always pins concrete GenVM runner version hashes and never uses local-only test/latest runner aliases. Covers equivalence principles, storage r
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add internet-court/internet-court-skill --skill write-contract -a cursor -y
Write production-quality GenLayer intelligent contracts. Always pins concrete GenVM runner version hashes and never uses local-only test/latest runner aliases. Covers equivalence principles, storage rules, LLM resilience, and cross-contract interaction.
Write production-quality GenLayer intelligent contracts. Always pins concrete GenVM runner version hashes and never uses local-only test/latest runner aliases. Covers equivalence principles, storage rules, LLM resilience, and cross-contract interaction.
name: write-contract description: Write production-quality GenLayer intelligent contracts. Always pins concrete GenVM runner version hashes and never uses local-only test/latest runner aliases. Covers equivalence principles, storage rules, LLM resilience, and cross-contract interaction. allowed-tools:
Guidance for writing GenLayer intelligent contracts that pass consensus, handle errors correctly, and survive production.
All GenLayer networks reject py-genlayer:test, py-genlayer:latest, and
unversioned runner aliases. Every generated contract MUST start with a pinned
runner dependency header.
# { "Depends": "py-genlayer:1jb45aa8ynh2a9c9xn3b7qqh8sm5q93hwfp7jqmwsfhh8jpz09h6" }
test and latest are local-development aliases for GenLayer runtime
developers. They may work only in a specially configured local Studio
environment with a GenLayer developer environment variable, but they do not work
on GenLayer networks and must not appear in generated user contracts.
Before returning any contract code, verify:
Depends runner version hash.py-genlayer:test.py-genlayer:latest.py-genlayer.Always lint with genvm-lint check after writing or modifying a contract.
Before writing code, decide whether the feature actually needs GenLayer consensus. Recent builder feedback shows many projects start by treating GenLayer as a generic AI backend; push them toward a clear on-chain consensus role.
Use GenLayer when the contract must coordinate or settle around a subjective, external, or AI-mediated judgment that multiple validators should verify independently:
Prefer a normal backend, frontend, or off-chain LLM workflow when:
For every contract, write down the boundary before implementation:
If the boundary is unclear, create a one-page architecture note before coding: user action -> evidence source -> nondeterministic call -> equivalence principle -> state update -> user-visible settlement.
# { "Depends": "py-genlayer:1jb45aa8ynh2a9c9xn3b7qqh8sm5q93hwfp7jqmwsfhh8jpz09h6" }
from genlayer import *
class MyContract(gl.Contract):
# Storage fields — typed, persisted on-chain
owner: Address
items: TreeMap[str, Item]
item_order: DynArray[str]
def __init__(self, param: str):
self.owner = gl.message.sender_account
@gl.public.view
def get_item(self, item_id: str) -> dict:
return {"id": item_id, "value": self.items[item_id].value}
@gl.public.write
def set_item(self, item_id: str, value: str) -> None:
if gl.message.sender_account != self.owner:
raise gl.UserError("Only owner")
self.items[item_id] = Item(value=value)
self.item_order.append(item_id)
The first line of a contract declares the GenVM Python runner. Always pin a
specific runner version hash. All GenLayer networks reject test, latest, and
unversioned runner aliases in generated contracts.
# { "Depends": "py-genlayer:1jb45aa8ynh2a9c9xn3b7qqh8sm5q93hwfp7jqmwsfhh8jpz09h6" }
Use py-genlayer-multi when the contract is packaged across multiple files.
# { "Depends": "py-genlayer-multi:06zyvrlivjga0d5jlpdbprksc0pa6jmllxvp8s20hq1l512vh5yk" }
Add py-lib-genlayer-embeddings before the main Python runner with a Seq
block.
# {
# "Seq": [
# { "Depends": "py-lib-genlayer-embeddings:0bmbm3cyfwxsyh454z53vxqjf47wz2q7smcqp1q4g4a6k2kidnyk" },
# { "Depends": "py-genlayer:1jb45aa8ynh2a9c9xn3b7qqh8sm5q93hwfp7jqmwsfhh8jpz09h6" }
# ]
# }
This is the most critical decision. Pick wrong and consensus will fail or be trivially exploitable.
Can validators reproduce the exact same normalized output?
├── YES → strict_eq
│ Exact match. Use when outputs are deterministic or can be
│ canonicalized (e.g., JSON with sort_keys=True).
│ Examples: blockchain RPC, stable REST APIs.
│
└── NO → Write a custom validator function (run_nondet_unsafe)
Default: produce independent evidence. Usually rerun the same task
and compare decision fields, derived status, scores, or other stable
outputs with explicit tolerances. Only skip the second answer when the
validator can judge the leader output against source data and criteria.
GenLayer also provides prompt_comparative and prompt_non_comparative as convenience wrappers, but most contracts outgrow them quickly. Start with a custom validator function for full flexibility.
For LLM and web operations, never trust the leader. The validator must verify the substance of the leader's answer using evidence other than the leader's answer alone. In practice that means one of:
Do not write validators that only check leader_result.calldata for a valid JSON shape, allowed enum value, non-empty summary, or confidence in range. That is leader-output-only validation, not consensus. It trusts the leader's substantive answer 100% and only proves that the leader formatted the answer correctly.
Non-comparative validation does not mean "trust the leader." It means the validator does not produce a second candidate answer. It still must read the same input/source data and ask whether the leader output is valid under clear criteria. A summary validator, for example, should check whether the proposed summary is faithful to the article, covers the material points, avoids hallucinated facts, and satisfies length/style constraints.
Classification, scoring, extraction, authenticity decisions, safety decisions, ranking, and settlement logic almost always need comparative validation: rerun or independently derive the answer, then compare the decision field, extracted fields, score bucket, or derived status. If the validator only checks that the leader chose an allowed label such as authentic, suspicious, or inconclusive, the leader is deciding alone.
def fetch_balance(self) -> int:
def call_rpc():
res = gl.nondet.web.post(rpc_url, body=payload, headers=headers)
return json.loads(res.body.decode("utf-8"))["result"]
return gl.eq_principle.strict_eq(call_rpc)
Never use for LLM calls or web pages that change between requests.
The default choice for non-deterministic operations. You write the leader function and a validator function with your own comparison logic. The validator should independently perform or verify the same substantive task, then compare the result fields that matter.
def score_content(self, content: str) -> dict:
def leader_fn():
analysis = gl.nondet.exec_prompt(prompt, response_format="json")
score = _parse_llm_score(analysis)
return {"score": score, "analysis": str(analysis.get("analysis", ""))}
def validator_fn(leaders_res: gl.vm.Result) -> bool:
if not isinstance(leaders_res, gl.vm.Return):
return _handle_leader_error(leaders_res, leader_fn)
validator_result = leader_fn()
leader_score = leaders_res.calldata["score"]
validator_score = validator_result["score"]
# Gate check: if either is zero (reject), both must agree
if (leader_score == 0) != (validator_score == 0):
return False
# Tolerance: within 5x/0.5x bounds
if leader_score > 0 and validator_score > 0:
ratio = leader_score / validator_score
if ratio > 5.0 or ratio < 0.2:
return False
return True
return gl.vm.run_nondet_unsafe(leader_fn, validator_fn)
prompt_comparative reruns the task and sends both outputs to an LLM with your principle string. prompt_non_comparative does not rerun the task; it asks validators to judge the leader output against input data and criteria. Both are convenient for prototyping but limited - for most production contracts, prefer a custom validator function with explicit comparison logic.
Prefer prompt_comparative unless you can explain why independently doing the task again would be meaningless and how the validator will still verify the leader output against source data. If the only reason is "outputs may differ," compare the decision fields, normalize the output, derive a status, or use tolerance instead of dropping comparison entirely.
def resolve(self) -> str:
def analyze():
page = gl.get_webpage(url, mode="text")
return gl.exec_prompt(f"Analyze: {page}\nReturn JSON with outcome and reasoning.")
return gl.eq_principle.prompt_comparative(
analyze,
principle="`outcome` field must be exactly the same. All other fields must be similar.",
)
Classify errors so validators know how to compare them. This is critical for consensus on failure paths.
ERROR_EXPECTED = "[EXPECTED]" # Business logic (deterministic) — exact match required
ERROR_EXTERNAL = "[EXTERNAL]" # External API 4xx (deterministic) — exact match required
ERROR_TRANSIENT = "[TRANSIENT]" # Network/5xx (non-deterministic) — agree if both transient
ERROR_LLM = "[LLM_ERROR]" # LLM misbehavior — always disagree, force rotation
def _handle_leader_error(leaders_res, leader_fn) -> bool:
leader_msg = leaders_res.message if hasattr(leaders_res, 'message') else ''
try:
leader_fn()
return False # Leader errored, validator succeeded — disagree
except gl.vm.UserError as e:
validator_msg = e.message if hasattr(e, 'message') else str(e)
# Deterministic errors: must match exactly
if validator_msg.startswith(ERROR_EXPECTED) or validator_msg.startswith(ERROR_EXTERNAL):
return validator_msg == leader_msg
# Transient: agree if both hit transient failure
if validator_msg.startswith(ERROR_TRANSIENT) and leader_msg.startswith(ERROR_TRANSIENT):
return True
# LLM or unknown: disagree — forces consensus retry
return False
except Exception:
return False
# Web requests
if response.status >= 400 and response.status < 500:
raise gl.vm.UserError(f"{ERROR_EXTERNAL} API returned {response.status}")
elif response.status >= 500:
raise gl.vm.UserError(f"{ERROR_TRANSIENT} API temporarily unavailable")
# LLM responses
if not isinstance(analysis, dict):
raise gl.vm.UserError(f"{ERROR_LLM} LLM returned non-dict: {type(analysis)}")
# Business logic
if user_balance < amount:
raise gl.vm.UserError(f"{ERROR_EXPECTED} Insufficient balance")
| Python | GenLayer | Notes |
|---|---|---|
dict | TreeMap[K, V] | O(log n) lookup, persisted |
list | DynArray[T] | Dynamic array, persisted |
int | u256 / i256 | Sized integers for on-chain math |
float | use with care | See float guidance below |
enum | str | Store .value, not the enum itself |
u256 with atto-scale (value × 10^18) — this is standard across all blockchains.@allow_storage
@dataclass
class Item:
name: str
status: str # Use str, not Enum
atto_amount: u256 # Atto-scale (value * 10^18) for money
created_at: str # ISO format string
tags: DynArray[str]
__init__. The type annotation declares the storage slot; __init__ only sets initial values.class MyContract(gl.Contract):
owner: Address # ← storage field (class-level annotation)
items: DynArray[str] # ← storage field
count: u256 # ← storage field
def __init__(self):
self.owner = gl.message.sender_address # ← initial value only
# DynArray/TreeMap don't need initialization — they start empty
Wrong:
def __init__(self):
self.owner: Address = gl.message.sender_address # ← NOT a storage field!
self.items = [] # ← list is not a storage type
__init__, not by assignment. self.items = [x] does not work.TreeMap[str, u256] counter alongside collections for fast counts.DynArray[str] with json.dumps()/json.loads().LLMs return unpredictable formats. Always defensively parse.
def _parse_llm_score(analysis: dict) -> int:
"""Extract numeric score from LLM response, handling common variations."""
if not isinstance(analysis, dict):
raise gl.vm.UserError(f"{ERROR_LLM} Non-dict response: {type(analysis)}")
# Key aliasing — LLMs use alternate names
raw = analysis.get("score")
if raw is None:
for alt in ("rating", "points", "value", "result"):
if alt in analysis:
raw = analysis[alt]
break
if raw is None:
raise gl.vm.UserError(f"{ERROR_LLM} Missing 'score'. Keys: {list(analysis.keys())}")
# Coerce aggressively — handles int, float, "3", "3.5", whitespace
try:
return max(0, int(round(float(str(raw).strip()))))
except (ValueError, TypeError):
raise gl.vm.UserError(f"{ERROR_LLM} Non-numeric score: {raw}")
def _parse_json(text: str) -> dict:
"""Clean LLM JSON: strip wrapping text, fix trailing commas."""
import re
first = text.find("{")
last = text.rfind("}")
text = text[first:last + 1]
text = re.sub(r",(?!\s*?[\{\[\"\'\w])", "", text) # Remove trailing commas
return json.loads(text)
result = gl.nondet.exec_prompt(task, response_format="json")
This tells the LLM to return JSON. Still validate and clean — LLMs don't always comply.
LLMs can't reliably inspect characters in their input (they hallucinate em dashes, miscount characters, etc.). But they CAN generate correct Python code for these checks. Use eval() inside spawn_sandbox() to run LLM-generated code deterministically, then feed results back as ground truth.
def check_rules(self, text: str, rules: str) -> dict:
def run():
# Step 1: LLM generates Python checks from natural language rules
checks = gl.nondet.exec_prompt(
f"""Generate Python expressions to verify these rules.
Variable `text` contains the post. Skip subjective rules.
Rules: {rules}
Output JSON: {{"checks": [{{"rule": "...", "expression": "..."}}]}}""",
response_format="json",
).get("checks", [])
# Step 2: eval() all checks in one sandbox — deterministic, no hallucination
def eval_checks():
results = []
for c in checks:
try:
ok = eval(c["expression"], {
"__builtins__": {"len": len, "any": any, "all": all, "str": str},
"text": text,
})
results.append({"rule": c["rule"], "result": "SATISFIED" if ok else "VIOLATED"})
except Exception:
pass # skip broken expressions, let LLM handle the rule
return results
check_results = gl.vm.unpack_result(gl.vm.spawn_sandbox(eval_checks))
# Step 3: LLM scores with ground truth — can't hallucinate what code already verified
ground_truth = "\n".join(f"- {r['rule']}: {r['result']}" for r in check_results)
score = gl.nondet.exec_prompt(
f"""GROUND TRUTH (from code — do NOT override): {ground_truth}
For rules not listed, use your judgment.
Post: {text} Rules: {rules}
Output: {{"analysis": "...", "passed": true/false}}""",
response_format="json",
)
return {"passed": score.get("passed", False), "analysis": score.get("analysis", ""), "checks": check_results}
return gl.eq_principle.prompt_comparative(run, "Must agree on passed/failed and same rule violations")
When to use: any contract where rules are specified in natural language and include character-level or format checks that LLMs are unreliable at (specific punctuation, character counts, URL presence, hashtag limits, etc.).
other = gl.get_contract_at(Address(other_address))
value = other.view().get_data()
other = gl.get_contract_at(Address(other_address))
other.emit(on="accepted").process_data(payload) # Non-blocking
emit() queues the call — it executes after current transaction. Use on="accepted" (fast) or on="finalized" (safe).
Warning: If the current transaction is appealed after emit(), the emitted call still happens but the balance may already be decremented.
def __init__(self, num_workers: int):
with open("/contract/Worker.py", "rt") as f:
worker_code = f.read()
for i in range(num_workers):
addr = gl.deploy_contract(
code=worker_code.encode("utf-8"),
args=[i, gl.message.contract_address],
salt_nonce=i + 1,
on="accepted",
)
self.worker_addresses.append(addr)
Workers are immutable after deployment. Code changes require redeploying the factory.
def verify_deposit(self, rpc_url: str, contract_addr: str, call_data: bytes) -> bytes:
"""Verify state on another chain via eth_call."""
payload = {
"jsonrpc": "2.0", "id": 1,
"method": "eth_call",
"params": [{"to": contract_addr, "data": "0x" + call_data.hex()}, "latest"],
}
def fetch():
res = gl.nondet.web.post(rpc_url, body=json.dumps(payload).encode(),
headers={"Content-Type": "application/json"})
if res.status != 200:
raise gl.vm.UserError(f"{ERROR_EXTERNAL} RPC failed: {res.status}")
data = json.loads(res.body.decode("utf-8"))
if "error" in data:
raise gl.vm.UserError(f"{ERROR_EXTERNAL} RPC error: {data['error']}")
hex_result = data.get("result", "0x")[2:]
return bytes.fromhex(hex_result) if hex_result else b""
return gl.eq_principle.strict_eq(fetch)
External APIs return variable data (timestamps, counts). Extract only stable fields:
def leader_fn():
res = gl.nondet.web.get(api_url)
data = json.loads(res.body.decode("utf-8"))
# Only return fields that won't change between leader and validator calls
return {"id": data["id"], "login": data["login"], "status": data["status"]}
# NOT: follower_count, updated_at, online_status
When raw data may differ (e.g., CI check counts change), compare derived summaries:
def validator_fn(leaders_res: gl.vm.Result) -> bool:
validator_checks = leader_fn()
def derive(checks):
if not checks: return "pending"
for c in checks:
if c.get("conclusion") != "success": return "failing"
return "success"
return derive(leaders_res.calldata) == derive(validator_checks)
| Don't | Do Instead | Why |
|---|---|---|
py-genlayer:test, py-genlayer:latest, or unversioned py-genlayer | Pin the documented runner version hash | All GenLayer networks reject runner aliases and unpinned dependencies |
strict_eq() for LLM calls | Custom validator function | LLM outputs are non-deterministic — strict_eq always fails consensus |
Store list or dict | DynArray[T] or TreeMap[K, V] | Python builtins aren't persistable |
Use native float for money | Atto-scale u256 (value * 10^18) | Standard across blockchains for cross-chain interop |
| Insert fields in the middle of a dataclass | Append at END only (for upgradable contracts) | Storage layout is positional — insertion shifts all subsequent fields |
Store Enum directly | Store enum.value as str | Enum type not supported in storage |
| Ignore LLM response format | Validate type, sanitize JSON, alias keys | LLMs return unpredictable formats |
| Schema-only or leader-output-only validator for LLM/web output | Rerun the task, independently derive the result, or verify against source data | Format checks prove only that JSON is well-formed; they do not verify the leader's answer |
prompt_non_comparative for classification/scoring/extraction decisions | Comparative validator with field-level comparison or tolerance | Decisions need agreement on the substantive result; allowed-label checks let one leader decide alone |
| Let validator agree on LLM errors | Return False (disagree) to force rotation | Agreeing on broken LLM output locks bad state |
Use bare Exception in contracts | Use gl.vm.UserError with error prefix | Bare exceptions become unrecoverable VMError |
| Compare variable API fields in validators | Extract stable fields or derive status | Timestamps, counts change between calls |
| O(n) scans over large collections | Maintain TreeMap indexes for O(1) lookups | Transactions have compute limits |
genvm-lint check contracts/my_contract.py