--- name: juicebox-sdk-patterns-55af56b1b1bf description: 'Apply production Juicebox SDK patterns. Trigger: "juicebox patterns", "juicebox best practices". ' allowed-tools: Read, Write, Edit version: 1.16.0 license: MIT author: Jeremy Longshore tags: - saas - recruiting - juicebox compatibility: Designed for Claude Code --- # Juicebox SDK Patterns ## Overview Production-ready patterns for the Juicebox AI-powered people search API. Juicebox provides REST endpoints for searching professional profiles and enriching candidate data. The API authenticates via `JUICEBOX_API_KEY` and returns structured profile objects with LinkedIn URLs as natural dedup keys. A singleton client centralizes rate-limit handling across search and enrich endpoints. ## Singleton Client ```typescript const JUICEBOX_BASE = 'https://api.juicebox.work/v1'; let _client: JuiceboxClient | null = null; export function getClient(): JuiceboxClient { if (!_client) { const apiKey = process.env.JUICEBOX_API_KEY; if (!apiKey) throw new Error('JUICEBOX_API_KEY must be set — get it from juicebox.work/settings'); _client = new JuiceboxClient(apiKey); } return _client; } class JuiceboxClient { private headers: Record; constructor(apiKey: string) { this.headers = { 'Authorization': `Bearer ${apiKey}`, 'Content-Type': 'application/json' }; } async search(query: string, limit = 20): Promise { const res = await fetch(`${JUICEBOX_BASE}/search`, { method: 'POST', headers: this.headers, body: JSON.stringify({ query, limit }) }); if (!res.ok) throw new JuiceboxError(res.status, await res.text()); return res.json(); } async enrich(linkedinUrl: string): Promise { const res = await fetch(`${JUICEBOX_BASE}/enrich`, { method: 'POST', headers: this.headers, body: JSON.stringify({ linkedin_url: linkedinUrl }) }); if (!res.ok) throw new JuiceboxError(res.status, await res.text()); return res.json(); } } ``` ## Error Wrapper ```typescript export class JuiceboxError extends Error { constructor(public status: number, message: string) { super(message); this.name = 'JuiceboxError'; } } export async function safeCall(operation: string, fn: () => Promise): Promise { try { return await fn(); } catch (err: any) { if (err instanceof JuiceboxError && err.status === 429) { await new Promise(r => setTimeout(r, 5000)); return fn(); } if (err instanceof JuiceboxError && err.status === 401) throw new JuiceboxError(401, 'Invalid JUICEBOX_API_KEY'); throw new JuiceboxError(err.status ?? 0, `${operation} failed: ${err.message}`); } } ``` ## Request Builder ```typescript class JuiceboxSearchBuilder { private body: Record = {}; query(q: string) { this.body.query = q; return this; } limit(n: number) { this.body.limit = Math.min(n, 100); return this; } location(loc: string) { this.body.location = loc; return this; } title(t: string) { this.body.title_filter = t; return this; } company(c: string) { this.body.company_filter = c; return this; } yearsExp(min: number, max: number) { this.body.years_experience = { min, max }; return this; } build() { return this.body; } } // Usage: new JuiceboxSearchBuilder().query('ML engineer').location('San Francisco').yearsExp(3, 8).build(); ``` ## Response Types ```typescript interface Profile { id: string; name: string; title: string; company: string; linkedin_url: string; location: string; skills: string[]; experience_years: number; } interface SearchResponse { profiles: Profile[]; total: number; has_more: boolean; cursor?: string; } interface EnrichResult { profile: Profile; education: Array<{ school: string; degree: string; year: number }>; experience: Array<{ company: string; title: string; start: string; end: string | null }>; } ``` ## Testing Utilities ```typescript export function mockProfile(overrides: Partial = {}): Profile { return { id: 'prof-001', name: 'Jane Smith', title: 'Senior ML Engineer', company: 'Acme Corp', linkedin_url: 'https://linkedin.com/in/janesmith', location: 'San Francisco, CA', skills: ['Python', 'PyTorch', 'MLOps'], experience_years: 6, ...overrides }; } export function mockSearchResponse(count = 3): SearchResponse { return { profiles: Array.from({ length: count }, (_, i) => mockProfile({ id: `prof-${i}` })), total: count, has_more: false }; } ``` ## Error Handling | Pattern | When to Use | Example | |---------|-------------|---------| | `safeCall` wrapper | All Juicebox API calls | Structured error with operation context | | Retry on 429 | Batch search pipelines | 5s backoff before retry | | LinkedIn dedup | Multi-query search | `Set` on `linkedin_url` prevents duplicates | | Cursor pagination | Search results > 100 | Pass `cursor` from previous response | ## Prerequisites - A typed client boundary, secret-manager reference, sandbox workspace, approved source/destination allowlist, and synthetic prospect fixture. ## Instructions 1. Construct clients from scoped environment configuration and reject unknown workspace, source, or destination before a request is sent. 2. Centralize source-authority, suppression, schema, redaction, and idempotency checks in the client boundary. 3. Model partial/denied responses explicitly; never turn a denied or unverified enrichment into a success or export fallback. 4. Test malformed data, rate limits, source/suppression rejection, and rollback using synthetic fixtures only. ## Output Produce a client-contract receipt with SDK revision, environment, source/destination policy revisions, fixture result, error classification, aggregate counts, export-count assertion, and rollback behavior. Exclude contacts, enrichment data, and credentials. ## Examples `sdk=v3; env=sandbox; source=synthetic; destination=approved; suppression=pass; malformed=blocked; contacts_exported=0; telemetry=aggregate-only` is a safe SDK result. ## Resources - Juicebox API Docs ## Next Steps Apply patterns in `juicebox-core-workflow-a`. --- Source attribution: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/juicebox-pack/skills/juicebox-sdk-patterns/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Jeremy Longshore Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/juicebox-pack/skills/juicebox-sdk-patterns/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: Backend Developer, ML / AI Engineer, Product Manager Specialties: API, Backend, Design, Frontend, Testing Tools: Juicebox Sdk Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 921247bd0ea75bddcc38149e5af5e1e30af9ff286ed6f2625968043058a3cf5d