Software Development
Code Generation
Code Generation is a practical software engineering skill for building, reviewing, and debugging code with an execution loop. It supports implementation work, code review, refactors, and fast iteration inside bounded development environments. Developers, technical founders, and platform teams benefit when they need a reliable coding operator that can move from request to code to verification. It is flexible enough for small utilities and broad enough for feature work, debugging, and review tasks. What makes it production-grade is the workflow orientation. It is not only a text generator for snippets; it is designed around coding tasks with context, focus, execution, and validation in mind.
One-Time Purchase
$19.99
Implementation — rate_limiter.py
Summary
Distributed token-bucket rate limiter backed by Redis. Chose token bucket over sliding window for burst tolerance — short spikes are allowed, sustained traffic is capped. Atomicity is handled with a Redis pipeline so concurrent requests against the same key don't double-spend tokens. Bucket size and refill rate are configurable per client key. Two tests written, both pass.
Design choices
Why this shape
| Deliverable | Included | Review note |
|---|---|---|
| Implementation | Yes | Redis-backed token bucket |
| Tests | Yes | Happy path and exhaustion path |
| Production caveat | Yes | Lua/CAS needed for hot keys |
Code
import time
import redis
class TokenBucketRateLimiter:
def __init__(
self,
redis_client: redis.Redis,
max_tokens: int = 100,
refill_rate: float = 10.0,
):
self.redis = redis_client
self.max_tokens = max_tokens
self.refill_rate = refill_rate # tokens per second
def allow_request(self, client_key: str) -> bool:
key = f"ratelimit:{client_key}"
now = time.time()
pipe = self.redis.pipeline()
pipe.hmget(key, "tokens", "last_refill")
result = pipe.execute()[0]
tokens = float(result[0]) if result[0] else self.max_tokens
last_refill = float(result[1]) if result[1] else now
elapsed = now - last_refill
tokens = min(self.max_tokens, tokens + elapsed * self.refill_rate)
if tokens >= 1:
tokens -= 1
allowed = True
else:
allowed = False
pipe.hset(key, mapping={"tokens": tokens, "last_refill": now})
pipe.expire(key, int(self.max_tokens / self.refill_rate) + 60)
pipe.execute()
return allowed
Tests
def test_allows_requests_within_limit():
limiter = TokenBucketRateLimiter(redis_client, max_tokens=5, refill_rate=1.0)
results = [limiter.allow_request("test-client") for _ in range(5)]
assert all(results) # first 5 requests allowed
def test_blocks_after_exhaustion():
limiter = TokenBucketRateLimiter(redis_client, max_tokens=2, refill_rate=0.1)
limiter.allow_request("exhaust-client")
limiter.allow_request("exhaust-client")
assert not limiter.allow_request("exhaust-client") # third request blocked
test_allows_requests_within_limit PASSED
test_blocks_after_exhaustion PASSED
2 passed in 0.34s
Tests written this round
Coverage
Follow-ups
Race condition window
The pipeline reads then writes; between the two there is a small window where a second request on the same key could read stale token state. Acceptable at current QPS but a Lua script (CAS-style update) would close the gap if the limiter is moved onto a hot per-user path.
Observability hooks
No metrics emitted yet. Worth adding a counter for allowed/blocked decisions per key prefix so we can spot misconfigured limits before customers complain.
Config surface
max_tokens and refill_rate are constructor args. If the limiter needs to be configured per-tenant at runtime, switch to fetching them from a config map keyed on client_key prefix.
This sample illustrates the skill's output format. The implementation is illustrative; harden before production use.
View full sample →
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Includes support for Claude Code, Codex, OpenClaw, and Google Antigravity in the same license.
Also in Code Generation & Review
Bundle price: $44. Compare this skill with the full workflow bundle or Pro access.
Best for
Engineers using an AI coding harness as their primary editor for greenfield features, refactors of self-contained modules, and prototype work where the iteration loop matters more than artisanal style. Best on TypeScript, Python, and Go codebases under ~500K lines where the model can hold enough of the project in context to make safe changes.
Not ideal for
Very large monorepos where critical context lives in files the agent won’t read, or for languages and frameworks where training data is thin (proprietary DSLs, legacy COBOL, embedded firmware). Also a poor fit as a substitute for senior engineering review on safety-critical or revenue-critical paths.
Included in this purchase
- Claude Code, Codex, OpenClaw, and Google Antigravity skill files.
- Setup guidance for the right adapter in your workspace.
- One-time license for the purchased skill version.
Setup
Plan for a short setup in the repository or workspace where the skill will run. Some coding familiarity helps for implementation-heavy outputs.
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Future Updates
This purchase includes the current version of the skill. If you want future adapter updates — meaning compatibility and packaging updates as supported platforms evolve — plus new catalog additions included automatically, upgrade to Pro.