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find-and-enrich-by-role

find-and-enrich-by-role is a skill for Claude Code from fiber-ai/fiber-ai-plugin. It costs 97 tokens per session (1,966 once invoked), scanned A, original, MIT.

A prospecting tool for finding people by job role and company criteria, then adding contact details to a selected shortlist. Prospecting means identifying potential customers or business contacts.

In plain words
What is it for?
It helps find roles such as engineering vice presidents or chief technology officers at matching companies, then add emails, phone numbers, or profiles for fewer than 50 prospects.
Why use it?
It combines search and contact lookup in one workflow, so users do not need to find suitable people and reveal their details separately.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Part of the fiber plugin — 16 skills, 4 commands, 7 agents, 3 MCP servers shipped together

not rated 2repo 2mo ago A scan 97 tokens original MIT

Good fit It helps find roles such as engineering vice presidents or chief technology officers at matching companies, then add emails, phone numbers, or profiles for fewer than 50 prospects.

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Install with agentmods
npx agentmods add skills/fiber-ai/fiber-ai-plugin/find-and-enrich-by-role
View source ↗ fiber-ai/fiber-ai-plugin
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add fiber-ai/fiber-ai-plugin --skill find-and-enrich-by-role
Clone the repo
git clone --depth 1 https://github.com/fiber-ai/fiber-ai-plugin

Made for: Claude Code.

Or install fiber, the plugin that ships this one along with the rest of its 16 skills, 4 commands, 7 agents, 3 MCP servers.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for find-and-enrich-by-role

README.md
[![agentmods](https://agentmods.dev/badge/skills/fiber-ai/fiber-ai-plugin/find-and-enrich-by-role/github.svg)](https://agentmods.dev/skills/fiber-ai/fiber-ai-plugin/find-and-enrich-by-role)
Your own site
<a href="https://agentmods.dev/skills/fiber-ai/fiber-ai-plugin/find-and-enrich-by-role"><img src="https://agentmods.dev/badge/skills/fiber-ai/fiber-ai-plugin/find-and-enrich-by-role/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for find-and-enrich-by-role

Your own site · 80×15
<a href="https://agentmods.dev/skills/fiber-ai/fiber-ai-plugin/find-and-enrich-by-role"><img src="https://agentmods.dev/badge/skills/fiber-ai/fiber-ai-plugin/find-and-enrich-by-role.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,966 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00097 $0.01966
Opus 5 $0.00048 $0.00983
Sonnet 5 $0.00019 $0.00393
Haiku 4.5 $0.00010 $0.00197

Measured 12d ago against content hash 726fb4d0ee2a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

find-and-enrich-by-role scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/find-and-enrich-by-role/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Fiber AI: Find and Enrich By Role

Discover prospects by role and company, then reveal contact details for the shortlist the user actually wants to reach. The default sales-prospecting combo skill.

When to use

  • User asks for people by role + company criteria ("VPs of Eng at fintech startups")
  • User wants a short, chat-time list (under 50 rows) with emails/phones attached
  • User says "prospect by role", "sales target research", "find me N s in "
  • User wants a quick pass before deciding whether to build a full audience

Do not use when

  • User wants a saved, exportable list of 100+ prospects - use /fiber:audience or /fiber:build-recruiting-audience
  • User already has LinkedIn URLs - use /fiber:enrich-linkedin-csv
  • User only wants to search, not reveal contacts - use /fiber:search
  • User wants code - use /fiber:sdk-ts or /fiber:sdk-py

Happy path

One search entry, one shared enrichment tail.

  1. Map the user's brief into peopleSearch's searchParams. Fiber's search uses typed seniority levels (vp, director, c-level) and canonicalized company slugs — no regex or case-normalization needed. If the user only gave freeform prose (e.g. "VPs of Engineering at Series B fintechs in NYC") and you cannot extract clean filters, use textToProfileSearch to translate intent into structured filters automatically, or hand the whole prompt to the Core MCP search_endpoints meta-tool so it picks the correct route; do not invent fields. Run get_endpoint_details_full("peopleSearch") on the Core MCP for the current searchParams schema.
  2. Call peopleSearch with the mapped filters. Always echo the filters you used back to the user in one line so they can correct before paginating ("using: title=VP Engineering, industry=Fintech, funding=Series B, location=NYC - edit or proceed?").
  3. Before paginating, call peopleSearchCount with the same filters and surface the total ("4,120 people match - show page 1 of 25, or narrow first?"). Never silently page past page 1. Keep pageSize <= 50.
  4. User picks a shortlist (typically <= 25). For each row, call syncQuickContactReveal. For shortlists of 26-500, use startBatchContactDetails + pollBatchContactDetails instead - cheaper per row and async.
  5. If the shortlist grows past ~50 or the user asks to export, stop and hand off to /fiber:build-recruiting-audience or /fiber:audience. Those flows are cheaper and more robust at that scale.

Read the full file on GitHub · 107 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 107 lines · 97 tokens per session scan A 726fb4d0ee2a

Subscribe to this mod's changes

find-and-enrich-by-role is a skill published in the GitHub repository fiber-ai/fiber-ai-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session and 1,966 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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