anth-reliability-patterns
Implement reliability patterns for Claude API: circuit breakers, graceful degradation, idempotency, and fallback strategies. Trigger with phrases like "anthropic reliability", "claude circuit breaker", "claude fallback", "anthropic fault tolerance".
What this skill does
# Anthropic Reliability Patterns
## Overview
Production reliability patterns for Claude API: circuit breaker (prevent cascading failures), graceful degradation (serve fallbacks), idempotency (safe retries), and timeout management.
## Circuit Breaker
```python
import time
from enum import Enum
class CircuitState(Enum):
CLOSED = "closed" # Normal operation
OPEN = "open" # Failing, reject requests
HALF_OPEN = "half_open" # Testing recovery
class ClaudeCircuitBreaker:
def __init__(self, failure_threshold: int = 5, recovery_timeout: int = 60):
self.state = CircuitState.CLOSED
self.failures = 0
self.threshold = failure_threshold
self.recovery_timeout = recovery_timeout
self.last_failure_time = 0.0
def call(self, func, *args, **kwargs):
if self.state == CircuitState.OPEN:
if time.time() - self.last_failure_time > self.recovery_timeout:
self.state = CircuitState.HALF_OPEN
else:
raise Exception("Circuit breaker OPEN — Claude API unavailable")
try:
result = func(*args, **kwargs)
if self.state == CircuitState.HALF_OPEN:
self.state = CircuitState.CLOSED
self.failures = 0
return result
except Exception as e:
self.failures += 1
self.last_failure_time = time.time()
if self.failures >= self.threshold:
self.state = CircuitState.OPEN
raise
# Usage
breaker = ClaudeCircuitBreaker(failure_threshold=5, recovery_timeout=60)
def safe_claude_call(prompt: str) -> str:
try:
return breaker.call(
client.messages.create,
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}]
).content[0].text
except Exception:
return "AI assistant is temporarily unavailable."
```
## Graceful Degradation
```python
import anthropic
def complete_with_fallback(prompt: str) -> str:
"""Try Sonnet → Haiku → cached response → static fallback."""
models = ["claude-sonnet-4-20250514", "claude-haiku-4-20250514"]
for model in models:
try:
msg = client.messages.create(
model=model,
max_tokens=1024,
messages=[{"role": "user", "content": prompt}]
)
return msg.content[0].text
except anthropic.RateLimitError:
continue # Try cheaper model
except anthropic.APIStatusError:
continue # Try next model
# All models failed — return cached or static response
cached = cache.get(f"claude:{hash(prompt)}")
if cached:
return f"[Cached response] {cached}"
return "Our AI assistant is temporarily unavailable. Please try again in a few minutes."
```
## Idempotent Requests
```python
import hashlib
import json
class IdempotentClaude:
def __init__(self):
self.client = anthropic.Anthropic()
self.cache = {} # Use Redis in production
def create_message(self, idempotency_key: str | None = None, **kwargs) -> str:
# Generate deterministic key from request params if not provided
if not idempotency_key:
idempotency_key = hashlib.sha256(
json.dumps(kwargs, sort_keys=True, default=str).encode()
).hexdigest()
# Return cached result for duplicate requests
if idempotency_key in self.cache:
return self.cache[idempotency_key]
msg = self.client.messages.create(**kwargs)
result = msg.content[0].text
self.cache[idempotency_key] = result
return result
```
## Timeout Configuration
```python
# Layer timeouts for defense-in-depth
client = anthropic.Anthropic(
timeout=60.0, # SDK-level timeout (covers connect + read)
max_retries=3, # Auto-retry on 429/5xx
)
# Per-request timeout override
msg = client.messages.create(
model="claude-haiku-4-20250514",
max_tokens=64,
messages=[{"role": "user", "content": "Quick question"}],
timeout=10.0 # Override for fast operations
)
```
## Reliability Checklist
- [ ] Circuit breaker prevents cascading failures
- [ ] Graceful degradation serves fallback responses
- [ ] Idempotency keys prevent duplicate processing
- [ ] Timeouts configured at SDK and application level
- [ ] Health check probes API connectivity
- [ ] Retry logic uses exponential backoff (SDK default)
- [ ] Rate limit headers monitored for pre-emptive throttling
## Resources
- [API Error Types](https://docs.anthropic.com/en/api/errors)
- [Rate Limits](https://docs.anthropic.com/en/api/rate-limits)
## Next Steps
For policy guardrails, see `anth-policy-guardrails`.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.