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Enrich companies or contacts with emails, phone numbers, and detailed profiles using Fiber AI. Use when the user wants to reveal contact information or get more details about a company or person.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Run a reproducible, pre-registered benchmark comparing Fiber AI to a competing data provider (People Data Labs, Apollo, Clearbit, ZoomInfo, Coresignal, or any other API the user has credentials for) on the user's own sample. Reports honest side-by-side numbers including where Fiber underperformed. Use this when the user says "compare Fiber to <vendor>", "bake-off", "benchmark data quality", "which provider is better for my segment", "test data providers", "evaluate Fiber AI", or "should I switch from <vendor> to Fiber".
Enrich a list of LinkedIn URLs with work emails, personal emails, and phone numbers using Fiber AI. Use this when the user says "enrich these LinkedIn URLs", "I have a CSV of people", "bulk reveal emails for these profiles", "get contact info for this list", "decorate these contacts with phone/email", or otherwise hands over a set of LinkedIn identifiers and wants contact data back.
Find prospects by role + company criteria and enrich a shortlist with contact details using Fiber AI. Use this when the user says "find VPs of Engineering at fintech startups", "CMOs at Series B SaaS", "heads of sales at X companies", "find me 20 CTOs in healthcare", "prospect by role + company", "sales target research", or otherwise wants to discover people by role and immediately get emails / phones back.
Search for companies or people using Fiber AI. Use when the user wants to find companies by criteria (location, industry, size, tech stack) or search for individual prospects.
Find lookalike companies from one or more seed accounts using Fiber AI. Use this when the user says "find companies like <seed>", "competitors of <seed>", "similar to <seed>", "lookalike accounts", "companies that look like our best customers", "ABM lookalikes", "seed-based company search", or otherwise asks to expand a known account into a larger candidate list.
Show what Fiber AI can do and how to use it. Use when the user asks about Fiber AI capabilities, available commands, or needs general guidance.
| name | enrich |
| description | Enrich companies or contacts with emails, phone numbers, and detailed profiles using Fiber AI. Use when the user wants to reveal contact information or get more details about a company or person. |
| user-invocable | true |
| argument-hint | <what to enrich — a person, company, LinkedIn URL, or domain> |
Reveal work emails, phone numbers, and detailed profiles for companies and individuals.
/fiber:search instead/fiber:audience instead/fiber:sdk-ts or /fiber:sdk-py insteadFiber exposes three tiers for single-profile contact reveal. Pick by latency vs cost. All tiers work on standard self-serve API keys — no enterprise gate or demo call required. Every response includes chargeInfo with the exact credits charged so agents can track cost programmatically.
syncQuickContactReveal — POST /v1/contact-details/single. Balanced speed and cost. Start here.syncTurboContactEnrichment — POST /v1/contact-details/turbo/sync. Use when absolute latency matters.triggerExhaustiveContactEnrichment — POST /v1/contact-details/exhaustive/start. Returns a taskId; poll with pollExhaustiveContactEnrichmentResult (GET /v1/contact-details/exhaustive/poll). Use as a fallback when both sync tiers return nothing.Via Core MCP:
get_endpoint_details_full("syncQuickContactReveal") to get the exact parameter schema.call_operation("syncQuickContactReveal", {"body": {"apiKey": "...", "linkedinUrl": "...", "enrichmentType": {...}}}).Via V2 MCP (if available as a generated tool):
syncQuickContactReveal_tool — it may be available depending on the server's tool generation priority.The enrichmentType object controls what data to fetch (e.g., work emails, personal emails, phone numbers). Fiber's schemas are large and evolve across versions — never hardcode field names. Discover the current schema via:
@fiberai/sdk (TypeScript) or fiberai (Python) export typed models with all fields. Install: npm install @fiberai/sdk / pip install fiberai.get_endpoint_details_full("syncQuickContactReveal") on the Core MCP for the exact current schema.https://api.fiber.ai/ai-docs/syncQuickContactReveal.md, https://api.fiber.ai/llms.txt, or https://api.fiber.ai/docs/.https://github.com/fiber-ai/open-fiber.KitchenSinkProfile returns 44+ top-level fields by default (experiences, education, skills, tenures, tags, inferred location with coordinates/timezone, career-began date, follower/connection counts, is-hiring/open-to-work/premium/influencer flags) with no field-group parameter needed. For live-fresh data, use profileLiveEnrich — works on any standard API key, returns in 2-4 seconds.
For company data, the relevant operations include:
companyLiveEnrich — live LinkedIn company data (works on standard keys, no enterprise gate)kitchenSinkCompany — comprehensive company profile lookupVia Core MCP:
get_endpoint_details_full("companyLiveEnrich") or get_endpoint_details_full("kitchenSinkCompany") to see the schemacall_operation("companyLiveEnrich", {"body": {...}}) with the correct parametersFor multiple contacts (but not a full audience), use the batch endpoints:
startBatchContactDetails — POST /v1/contact-details/batch/start. Starts async batch processing.pollBatchContactDetails — polls for results.For larger-scale enrichment, recommend /fiber:audience instead.
chargeInfo object, on https://api.fiber.ai/ai-docs/<operationId>.md, or via the llms://fiber.ai/llms.txt MCP resource — costs vary by enrichment type and change over time./fiber:audience workflow instead — it's more efficient.apiKey is required in the request body for POST endpoints. Check https://api.fiber.ai/docs/ for the exact authentication format for each endpoint.
Accept: text/markdown to https://api.fiber.ai/openapi.json to get the agent-friendly index markdown at the same URL (Stripe pattern) instead of the full JSON./fiber:setup.