What is build-recruiting-audience?

A workflow for building a saved list of job candidates with Fiber AI. It can search for people, add work and personal email addresses and phone numbers, and export the results as a CSV file.

Use it to find candidates for a role, add contact details to many candidates, maintain a recruiting audience, and export a candidate pipeline. It is not intended for a few immediate lookups or sales and marketing lists.

It turns a job description or recruiting brief into a reusable candidate list instead of leaving search results only in the current conversation. It is intended for larger sourcing tasks.

build-recruiting-audience — Skill for Claude Code by fiber-ai

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 build-recruiting-audience

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/fiber-ai/fiber-ai-plugin/build-recruiting-audience/github.svg)](https://agentmods.dev/skills/fiber-ai/fiber-ai-plugin/build-recruiting-audience)

Your own site

<a href="https://agentmods.dev/skills/fiber-ai/fiber-ai-plugin/build-recruiting-audience"><img src="https://agentmods.dev/badge/skills/fiber-ai/fiber-ai-plugin/build-recruiting-audience/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/fiber-ai/fiber-ai-plugin/build-recruiting-audience"><img src="https://agentmods.dev/badge/skills/fiber-ai/fiber-ai-plugin/build-recruiting-audience.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,010 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.

Model	Per session	Once invoked
Fable 5.1	$0.00102	$0.02010
Opus 5	$0.00051	$0.01005
Sonnet 5	$0.00020	$0.00402
Haiku 4.5	$0.00010	$0.00201

Measured 12d ago against content hash 23442e481db9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

build-recruiting-audience 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/build-recruiting-audience/SKILL.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Fiber AI: Build a Recruiting Audience

Stand up a persistent, exportable audience optimized for recruiting - prospects first, company metadata as context. This is the right flow when the user wants a saved candidate list, not just a chat-time search.

When to use

User asks to "build a recruiting list", "talent audience", "sourcing list", "candidate pipeline"
User wants to enrich hundreds or thousands of candidates with work + personal email + phone
User wants a CSV export at the end, not just chat results
User has a JD or a role spec and wants to find people who fit, at scale

Do not use when

User only needs 1-5 candidates revealed right now - use /fiber:enrich
User just wants to see who exists without saving a list - use /fiber:search or /fiber:find-and-enrich-by-role
User is enriching a seller / marketing list, not recruiting - use /fiber:audience (more generic)
User wants to generate this in code - use /fiber:sdk-ts or /fiber:sdk-py

Happy path

If the user pasted a JD instead of a structured brief, first sanity-check candidates with jdToProfileSearch and show the top 10. Then move into the audience lifecycle:

Create the audience via createAudience in DRAFT. For people-first recruiting flows, set creationMethod: START_FROM_PROSPECTS so the audience prioritizes prospect results over companies. Save the audienceId.
Attach targeting via updateAudienceSearchParams on the same audienceId. Recruiting-relevant prospectSearchParams fields typically include seniority, departments, jobTitleV2, yearsOfExperience, and location. Call get_endpoint_details_full("updateAudienceSearchParams") on the Core MCP for the exact current schema - do not guess field names. Echo the filters back to the user and confirm.
Get explicit user consent, then call buildAudience. Poll getAudienceStatus every 30 seconds until status === "NORMAL". Report the prospect count when done.
Call estimateEnrichmentCost selecting recruiter-relevant enrichment types (work emails, personal emails, phone numbers, and optionally live profile). This step is free.
Surface the estimate verbatim - total credits, breakdown, estimated duration - and get explicit user consent. Only then call triggerEnrichment. Poll getEnrichmentStatus every 30 seconds until progressPercent === 100.
Export via exportProspects. Recruiting cares about prospects, not companies - skip exportCompanies unless the user asks.

Read the full file on GitHub · 91 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.

12d ago First seen · 91 lines · 102 tokens per session scan A 23442e481db9

Subscribe to this mod's changes

build-recruiting-audience is a skill published in the GitHub repository fiber-ai/fiber-ai-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 102 tokens to every session and 2,010 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.

Marketing and SEO

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens