market-research-agent

market-research-agent is a skill for Claude Code from oyi77/1ai-skills. It costs 58 tokens per session (736 once invoked), scanned A, original, MIT.

An agent for researching markets, competitors, customer groups, trends, pricing, and business opportunities using available evidence.

In plain words
What is it for?
Use it before launching or changing a product, studying competitors or customer segments, researching prices and trends, planning market entry, or performing investment due diligence.
Why use it?
It organizes broad business questions into research tasks so decisions are based on gathered information rather than assumptions.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the 1ai-skills plugin — 209 skills, 4 commands shipped together

Good fit Use it before launching or changing a product, studying competitors or customer segments, researching prices and trends, planning market entry, or performing investment due diligence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/oyi77/1ai-skills/market-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 oyi77/1ai-skills --skill market-research-agent
Clone the repo
git clone --depth 1 https://github.com/oyi77/1ai-skills

Made for: Claude Code.

Or install 1ai-skills, the plugin that ships this one along with the rest of its 209 skills, 4 commands.

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 market-research-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/oyi77/1ai-skills/market-research-agent/github.svg)](https://agentmods.dev/skills/oyi77/1ai-skills/market-research-agent)
Your own site
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/market-research-agent"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/market-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 market-research-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/market-research-agent"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/market-research-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 736 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00058 $0.00736
Opus 5 $0.00029 $0.00368
Sonnet 5 $0.00012 $0.00147
Haiku 4.5 $0.00006 $0.00074

Measured 12d ago against content hash a89c96566f98, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

market-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 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.

agents/research/market-research-agent/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Market Research Agent

When to Use

Trigger phrases:

  • "market research agent"

  • "Analyze markets, competitors, user segments, and trends to produce evidence-base"

  • Evaluating market opportunity before building a product

  • Analyzing competitors before launching or pivoting

  • Pricing strategy research for new or existing products

  • Understanding user segments and their needs

  • Tracking market trends and technology shifts

  • Due diligence for investment or acquisition decisions

  • Go-to-market planning for new features or products

When NOT to Use

  • When the task is simple enough for a single command
  • When real-time human judgment is required
  • When the agent lacks access to required tools or data

Overview

Market Research Agent is an AI agent skill for agent orchestration. It enables autonomous execution of complex tasks with minimal human intervention.

Capabilities

  • Autonomous operation — Execute multi-step market research agent workflows independently
  • Context awareness — Adapt behavior based on current state and history
  • Error recovery — Handle failures gracefully with retry and fallback logic
  • Integration — Connect with external tools and services as needed

Workflow

# Example: Agent orchestration
from dataclasses import dataclass

@dataclass
class Task:
    name: str
    priority: int
    assigned_agent: str

def orchestrate(tasks: list[Task]) -> dict:
    results = {}
    for task in sorted(tasks, key=lambda t: t.priority):
        results[task.name] = execute(task)
    return results
  1. Initialize — Set up the agent context and load required resources
  2. Plan — Break down the task into executable steps
  3. Execute — Run each step, monitoring for errors and adapting as needed
  4. Verify — Validate results against acceptance criteria
  5. Report — Summarize outcomes and suggest next steps

Configuration

  • Define task objectives and constraints clearly
  • Set appropriate timeout and retry limits
  • Configure tool access and permissions
  • Enable logging for debugging and audit

Read the full file on GitHub · 109 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 · 109 lines · 58 tokens per session scan A a89c96566f98

Subscribe to this mod's changes

market-research-agent is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 736 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 2026-08-30.

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