anth-multi-env-setup
Configure Claude API across dev, staging, and production environments with isolated keys, model routing, and spend controls per environment. Trigger with phrases like "anthropic environments", "claude multi-env", "anthropic staging setup", "claude dev vs prod config".
What this skill does
# Anthropic Multi-Environment Setup
## Overview
Configure isolated Claude API environments with per-env API keys, model selection, and spend controls using Anthropic Workspaces.
## Environment Configuration
```python
# config.py
import os
from dataclasses import dataclass
@dataclass
class ClaudeConfig:
api_key: str
model: str
max_tokens: int
max_retries: int
timeout: float
monthly_budget_usd: float
CONFIGS = {
"development": ClaudeConfig(
api_key=os.environ["ANTHROPIC_API_KEY_DEV"],
model="claude-haiku-4-20250514", # Cheap for dev
max_tokens=256,
max_retries=1,
timeout=15.0,
monthly_budget_usd=10.0,
),
"staging": ClaudeConfig(
api_key=os.environ["ANTHROPIC_API_KEY_STAGING"],
model="claude-sonnet-4-20250514",
max_tokens=1024,
max_retries=2,
timeout=30.0,
monthly_budget_usd=50.0,
),
"production": ClaudeConfig(
api_key=os.environ["ANTHROPIC_API_KEY_PROD"],
model="claude-sonnet-4-20250514",
max_tokens=4096,
max_retries=5,
timeout=120.0,
monthly_budget_usd=5000.0,
),
}
def get_config() -> ClaudeConfig:
env = os.getenv("APP_ENV", "development")
return CONFIGS[env]
```
## Anthropic Workspaces (Key Isolation)
Create separate Workspaces in [console.anthropic.com](https://console.anthropic.com/settings/workspaces):
| Workspace | Purpose | Rate Limit Tier |
|-----------|---------|-----------------|
| `dev` | Development & testing | Tier 1 |
| `staging` | Pre-production validation | Tier 2 |
| `production` | Live traffic | Tier 3+ |
Each workspace has independent API keys, usage tracking, and rate limits.
## Environment Files
```bash
# .env.development
ANTHROPIC_API_KEY_DEV=sk-ant-api03-dev-...
APP_ENV=development
# .env.staging
ANTHROPIC_API_KEY_STAGING=sk-ant-api03-stg-...
APP_ENV=staging
# .env.production (stored in secret manager, not files)
ANTHROPIC_API_KEY_PROD=sk-ant-api03-prd-...
APP_ENV=production
```
## Client Factory
```python
import anthropic
def create_client() -> anthropic.Anthropic:
config = get_config()
return anthropic.Anthropic(
api_key=config.api_key,
max_retries=config.max_retries,
timeout=config.timeout,
)
```
## Per-Environment Model Override
```python
# Development: always use Haiku (cheapest)
# Staging: use production model for accuracy testing
# Production: use configured model
def get_model(override: str | None = None) -> str:
if override:
return override
return get_config().model
```
## Error Handling
| Issue | Cause | Fix |
|-------|-------|-----|
| Dev key used in prod | Wrong env loaded | Validate key prefix matches environment |
| Staging rate limited | Low tier workspace | Upgrade staging workspace tier |
| Cost overrun in dev | No budget guard | Add per-env spend limits |
## Resources
- [Workspaces](https://docs.anthropic.com/en/docs/administration/workspaces)
- [Console](https://console.anthropic.com)
## Next Steps
For monitoring, see `anth-observability`.
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.