EX

exa-reference-architecture

Provides a production-ready reference architecture for integrating Exa's neural search, RAG pipelines, and caching.

Install

mkdir -p .claude/skills/exa-reference-architecture && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/9375" && unzip -o skill.zip -d .claude/skills/exa-reference-architecture && rm skill.zip

Installs to .claude/skills/exa-reference-architecture

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Implement Exa reference architecture for search pipelines, RAG, and
67 charsno explicit “when” trigger
Intermediate

Key capabilities

  • →Design search service layers for Exa integrations
  • →Integrate RAG pipelines with Exa
  • →Implement content extraction strategies
  • →Configure domain-scoped search profiles
  • →Cache search results using LRU and Redis
  • →Discover competitors based on a company URL

How it works

The skill defines a standard project layout for Exa-based applications, covering search service design, RAG pipeline integration, content extraction, and caching strategies.

Inputs & outputs

You give it
Search queries, URLs, or topics
You get back
Structured search results, related content, AI summaries, or competitor lists

When to use exa-reference-architecture

  • →Designing new Exa search integrations
  • →Structuring RAG data pipelines
  • →Reviewing project architecture standards
  • →Implementing neural search caching

About this skill

Exa Retrieval Architecture Boundary

Overview

Design an Exa architecture that separates query policy, retrieval, content handling, asynchronous state, citations, and evidence. Treat credentials, queries, retrieved content, generated output, spend, and destructive state as separately governed boundaries.

Prerequisites

  • The target repository, environment, Exa team, product surface, and accountable owner.
  • The workload's data classification, latency and freshness promise, cost ceiling, and retention policy.
  • Current first-party documentation plus credentials only for a narrowly approved live check.

Current Contract

Use Search for ranked discovery, Contents for known URLs, Answer for a direct cited response, Agent for multi-step research, Monitors for recurring discovery, Websets for verified and enriched sets, and Batch for enabled enterprise offline volume. Each product has distinct lifecycle and trust boundaries.

Authentication

For normal REST work, inject EXA_API_KEY from an approved server-side secret manager and send it only as Authorization: Bearer to the configured first-party Exa API host. Team Management service keys, hosted MCP OAuth or enterprise managed authorization, and payment-protocol calls are separate trust models. Never print, commit, place in a URL, or expose a credential to an untrusted client.

Instructions

  1. Classify the user outcome, latency class, data sensitivity, freshness, and volume.
  2. Select the narrowest Exa product that satisfies the outcome.
  3. Place policy and credential enforcement before the vendor adapter.
  4. Separate retrieved content from content-free operational metadata and citations.
  5. Persist only necessary asynchronous IDs and define terminal-state reconciliation.
  6. Diagram failure, retention, deletion, webhook, cost, and rollback boundaries.

Tool Discipline

Use Read, Glob, and Grep to inspect repository code, configuration, fixtures, and evidence. Use Write and Edit only for approved implementation or documentation changes. Do not call Exa, run paid research, create or alter a Monitor, Webset, Agent run, Batch, team, member, API key, budget, webhook, or deployment merely because this skill was invoked.

Approval Boundaries

Require an accountable owner before live queries involving sensitive intent, production credentials, spend or rate-limit changes, forced live crawling, generated summaries, external delivery, deployment, member or key changes, schedule creation, or destructive cancellation, stopping, deletion, or revocation. Read-only repository inspection and synthetic offline validation do not authorize live vendor actions.

Failure Modes

  • Do not use Agent when a bounded Search or Contents call is sufficient.
  • Do not feed untrusted retrieved text directly into privileged tool execution.
  • Do not share one queue and retry policy across interactive and offline products.

Output

Return the operation scope, environment, team and product surface, authorization class, contract and policy decisions, deterministic validation results, content-free identifiers, status and cost counts, risks, cleanup or rollback state, and a concise pass or fail receipt. Exclude credentials, raw queries, prompts, presigned URLs, retrieved content, generated output, and customer-derived data unless separately approved.

Example

  • A RAG service uses Search highlights first, Contents only for selected URLs, a citation-preserving model boundary, and a separate audit stream.
  • Finish with request or resource IDs, assertion counts, cost and terminal state, rollback or deletion status, and the decision owner; never reproduce secrets or retrieved content.

Validation

Rerun the smallest relevant deterministic test, compare actual behavior with the requested outcome and current first-party contract, verify sensitive fields are absent from evidence, and confirm deadlines, terminal state, downstream retention, and rollback before reporting success.

References

Review the dated first-party evidence map before relying on any endpoint, parameter, search type, price, limit, beta, compliance, identity, retry, or lifecycle claim.

When not to use it

  • →When the query is too specific and returns no results
  • →When search results have low relevance due to incorrect search type
  • →When site scraping is blocked, resulting in empty text or highlights

Limitations

  • →Default rate limit of 10 QPS for api.exa.ai
  • →Some sites may block scraping, leading to empty content
  • →Search relevance can be low if the wrong search type is used

How it compares

This skill provides a structured, best-practice approach to Exa integration, unlike ad-hoc implementations that may lack standardized architecture for search, RAG, and content discovery.

Compared to similar skills

exa-reference-architecture side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
exa-reference-architecture (this skill)02moReviewIntermediate
llm-app-patterns38moNo flagsIntermediate
engineering-advanced-skills33moReviewAdvanced
ai-product18moNo flagsAdvanced

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