exa-multi-env-setup
Configure Exa across development, staging, and production environments. Use when setting up multi-environment search pipelines, managing API key isolation, or configuring per-environment search limits and caching. Trigger with phrases like "exa environments", "exa staging", "exa dev prod", "exa environment setup", "exa multi-env".
What this skill does
# Exa Multi-Environment Setup
## Overview
Exa charges per search request at `api.exa.ai`. Multi-environment setup focuses on API key isolation per environment, request limits and caching to control costs in staging, and appropriate `numResults`/content settings per tier.
## Prerequisites
- Exa API key(s) from dashboard.exa.ai
- `exa-js` installed (`npm install exa-js`)
- Optional: Redis for search result caching in staging/production
## Environment Strategy
| Environment | Key Isolation | numResults | Content | Cache TTL |
|-------------|---------------|------------|---------|-----------|
| Development | Shared dev key | 3 | highlights only | None |
| Staging | Staging key | 5 | text (1000 chars) | 5 min |
| Production | Prod key | 10 | text (2000 chars) | 1 hour |
## Instructions
### Step 1: Environment-Aware Configuration
```typescript
// config/exa.ts
import Exa from "exa-js";
type Env = "development" | "staging" | "production";
interface ExaEnvConfig {
apiKey: string;
defaultNumResults: number;
maxCharacters: number;
searchType: "auto" | "neural" | "keyword";
cacheEnabled: boolean;
cacheTtlSeconds: number;
}
const configs: Record<Env, Omit<ExaEnvConfig, "apiKey"> & { keyVar: string }> = {
development: {
keyVar: "EXA_API_KEY",
defaultNumResults: 3,
maxCharacters: 500,
searchType: "auto",
cacheEnabled: false,
cacheTtlSeconds: 0,
},
staging: {
keyVar: "EXA_API_KEY_STAGING",
defaultNumResults: 5,
maxCharacters: 1000,
searchType: "auto",
cacheEnabled: true,
cacheTtlSeconds: 300, // 5 minutes
},
production: {
keyVar: "EXA_API_KEY_PROD",
defaultNumResults: 10,
maxCharacters: 2000,
searchType: "neural",
cacheEnabled: true,
cacheTtlSeconds: 3600, // 1 hour
},
};
export function getExaConfig(): ExaEnvConfig {
const env = (process.env.NODE_ENV || "development") as Env;
const config = configs[env] || configs.development;
const apiKey = process.env[config.keyVar];
if (!apiKey) {
throw new Error(`${config.keyVar} not set for ${env} environment`);
}
return { ...config, apiKey };
}
export function getExaClient(): Exa {
return new Exa(getExaConfig().apiKey);
}
```
### Step 2: Search Service with Config-Driven Defaults
```typescript
// lib/exa-search.ts
import { getExaClient, getExaConfig } from "../config/exa";
export async function search(query: string, numResults?: number) {
const exa = getExaClient();
const cfg = getExaConfig();
const n = numResults ?? cfg.defaultNumResults;
return exa.searchAndContents(query, {
type: cfg.searchType,
numResults: n,
text: { maxCharacters: cfg.maxCharacters },
});
}
```
### Step 3: Redis Cache Layer (Staging/Production)
```typescript
// lib/exa-cache.ts
import { Redis } from "ioredis";
import { getExaClient, getExaConfig } from "../config/exa";
const redis = process.env.REDIS_URL ? new Redis(process.env.REDIS_URL) : null;
export async function cachedSearch(query: string, numResults?: number) {
const exa = getExaClient();
const cfg = getExaConfig();
const n = numResults ?? cfg.defaultNumResults;
if (cfg.cacheEnabled && redis) {
const cacheKey = `exa:${Buffer.from(`${query}:${n}:${cfg.searchType}`).toString("base64")}`;
const cached = await redis.get(cacheKey);
if (cached) return JSON.parse(cached);
const results = await exa.searchAndContents(query, {
type: cfg.searchType,
numResults: n,
text: { maxCharacters: cfg.maxCharacters },
});
await redis.set(cacheKey, JSON.stringify(results), "EX", cfg.cacheTtlSeconds);
return results;
}
return exa.searchAndContents(query, {
type: cfg.searchType,
numResults: n,
text: { maxCharacters: cfg.maxCharacters },
});
}
```
### Step 4: Environment Variables
```bash
# .env.local (development)
EXA_API_KEY=exa-dev-key-here
# .env.staging
EXA_API_KEY_STAGING=exa-staging-key-here
REDIS_URL=redis://staging-redis:6379
# .env.production
EXA_API_KEY_PROD=exa-prod-key-here
REDIS_URL=redis://prod-redis:6379
```
### Step 5: CI/CD Secret Configuration
```yaml
# .github/workflows/deploy.yml
jobs:
deploy-staging:
environment: staging
env:
EXA_API_KEY_STAGING: ${{ secrets.EXA_API_KEY_STAGING }}
NODE_ENV: staging
steps:
- run: npm ci && npm run build && npm run deploy:staging
deploy-production:
environment: production
env:
EXA_API_KEY_PROD: ${{ secrets.EXA_API_KEY_PROD }}
NODE_ENV: production
steps:
- run: npm ci && npm run build && npm run deploy:prod
```
### Step 6: Health Check Per Environment
```typescript
export async function checkExaHealth(): Promise<{
status: string;
env: string;
latencyMs: number;
}> {
const start = performance.now();
try {
const exa = getExaClient();
await exa.search("health check", { numResults: 1 });
return {
status: "healthy",
env: process.env.NODE_ENV || "development",
latencyMs: Math.round(performance.now() - start),
};
} catch {
return {
status: "unhealthy",
env: process.env.NODE_ENV || "development",
latencyMs: Math.round(performance.now() - start),
};
}
}
```
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| `401 Unauthorized` | Wrong API key for environment | Verify correct env var name |
| `429 rate_limit_exceeded` | Too many requests | Enable caching and request queuing |
| High API costs in staging | No caching enabled | Enable Redis cache with 5-min TTL |
| Empty results in dev | numResults too low | Increase from 3 to 5 |
## Resources
- [Exa API Documentation](https://docs.exa.ai)
- [Exa Pricing](https://exa.ai/pricing)
- [exa-js SDK](https://github.com/exa-labs/exa-js)
## Next Steps
For deployment configuration, see `exa-deploy-integration`.
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.