audience-research-agent

audience-research-agent is a skill for Claude Code, Codex from lckx777/copy-chief-black. It costs 63 tokens per session (4,808 once invoked), scanned A, original, MIT.

A research skill that studies a target audience for digital offers and turns the findings into organized customer-language and insight files.

In plain words
What is it for?
Use it to research an audience, extract voice-of-customer material, analyse its psychology, and prepare a library for briefings or creative work.
Why use it?
It helps replace guesses about an audience's problems, desires, objections, and wording with structured research for later copywriting.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: reads .claude/ paths; mentions subagents.

Good fit Use it to research an audience, extract voice-of-customer material, analyse its psychology, and prepare a library for briefings or creative work.

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Install with agentmods
npx agentmods add skills/lckx777/copy-chief-black/audience-research-agent
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 lckx777/copy-chief-black --skill audience-research-agent
Clone the repo
git clone --depth 1 https://github.com/lckx777/copy-chief-black

Made for: Claude Code, Codex.

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 audience-research-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/lckx777/copy-chief-black/audience-research-agent/github.svg)](https://agentmods.dev/skills/lckx777/copy-chief-black/audience-research-agent)
Your own site
<a href="https://agentmods.dev/skills/lckx777/copy-chief-black/audience-research-agent"><img src="https://agentmods.dev/badge/skills/lckx777/copy-chief-black/audience-research-agent/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 audience-research-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/lckx777/copy-chief-black/audience-research-agent"><img src="https://agentmods.dev/badge/skills/lckx777/copy-chief-black/audience-research-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,808 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 $0.00063 $0.04808
Opus 5 $0.00032 $0.02404
Sonnet 5 $0.00013 $0.00962
Haiku 4.5 $0.00006 $0.00481

Measured 9d ago against content hash 3ecc5a25cb86, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-13, from the pricing page.

Security

Grade A, and why

audience-research-agent 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 9d 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.

framework/skills/audience-research-agent/SKILL.md · 487 lines

How it starts

The opening of the file, as written. The whole thing — 487 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Audience Research Agent

Copy Chief estratégico responsável por coordenar pesquisa de público em 4 fases, delegar extração técnica ao voc-research-agent, aplicar frameworks de análise psicográfica na Fase 3, e gerar VOC Library RAG-otimizada para outros agentes.

Quick Start

  1. Receber materiais da oferta (produto, avatar inicial, concorrentes)
  2. Executar 4 fases: Análise → Extração VOC → Síntese Psicográfica → Output
  3. Gerar VOC Library YAML com Score de Prontidão ≥70/100 → Output: voc_library_[nicho]_[data].yaml pronto para HELIX/criativos

Output Location

Write all outputs to:

  • Raw VOC: research/{offer-name}/voc/raw/ — Extração bruta por plataforma
  • Processed: research/{offer-name}/voc/processed/
    • pain-points.md — Dores classificadas por intensidade
    • desires.md — Desejos declarados/implícitos/secretos
    • objections.md — Objeções mapeadas com counters
    • language-patterns.md — Expressões e hooks verbatim
  • Summary: research/{offer-name}/voc/summary.md — MAX 500 tokens
  • VOC Library: research/{offer-name}/voc_library.yaml — Arquivo completo

CRITICAL: Return only summary.md path to orchestrator. Never return raw content.

Quando Usar

Ativar para pesquisa profunda de público-alvo além de personas superficiais, extração de dores viscerais e linguagem natural, briefing psicográfico para VSL/landing page/campanha, ou preparação de inputs para helix-system-agent ou criativos-agent.

Workflow de 4 Fases

Fase 1 - Análise de Inputs

Objetivo: Coletar e processar materiais existentes ANTES de iniciar pesquisa. Estabelecer baseline que guia a Fase 2.

Prompt de Intake (usar ao iniciar):

## FASE 1: COLETA DE INPUTS

Antes de iniciar a pesquisa VOC, preciso de contexto sobre a oferta.

### Materiais Existentes (se houver)
- VSL/TSL atual? (link ou arquivo)
- Landing page? (link)
- Criativos rodando? (prints, links, ou arquivo consolidado)
- Materiais de referência/mineração?

### Informações do Produto
- Nome da oferta:
- Tipo (VSL/TSL/SaaS/Curso/Ebook):
- Faixa de preço:
- Expert/fundador (se houver):
- Promessa central (1 frase):

### Contexto de Mercado
- Concorrentes conhecidos:
- O que já funciona/não funciona (se souber):
- Nicho/sub-nicho específico:

[Após receber inputs, gerar research/fase-01-inputs.md]

Read the full file on GitHub · 487 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. 9d ago First seen · 487 lines · 63 tokens per session scan A 3ecc5a25cb86

Subscribe to this mod's changes

audience-research-agent is a skill published in the GitHub repository lckx777/copy-chief-black (5 stars, last pushed 6mo ago), licensed MIT. It adds 63 tokens to every session and 4,808 once invoked, about $0.0003 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 2026-09-03.

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