researcher-product

researcher-product is an agent for Claude Code from Hayes-Zhang/deep-research. It costs 30 tokens per session (1,634 once invoked), scanned A, original, MIT.

A research role focused on product decisions: user needs, product definition, competitors, priorities, and signs that a product fits the market. PMF means product-market fit, or evidence that a product meets a real demand.

In plain words
What is it for?
Use it to identify target users and their problems, shape a product brief, compare competitors, prioritize an initial version, and plan tests for market demand.
Why use it?
It helps separate a worthwhile product problem from a merely interesting idea. It also gives teams a way to compare alternatives and decide what to build first.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter; reads .claude/ paths.

Part of the deep-research plugin — 1 command, 8 agents shipped together

Good fit Use it to identify target users and their problems, shape a product brief, compare competitors, prioritize an initial version, and plan tests for market demand.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/hayes-zhang/deep-research/researcher-product
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.

Clone the repo
git clone --depth 1 https://github.com/Hayes-Zhang/deep-research

Made for: Claude Code.

Or install deep-research, the plugin that ships this one along with the rest of its 1 command, 8 agents.

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 researcher-product

README.md
[![agentmods](https://agentmods.dev/badge/agents/hayes-zhang/deep-research/researcher-product/github.svg)](https://agentmods.dev/agents/hayes-zhang/deep-research/researcher-product)
Your own site
<a href="https://agentmods.dev/agents/hayes-zhang/deep-research/researcher-product"><img src="https://agentmods.dev/badge/agents/hayes-zhang/deep-research/researcher-product/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 researcher-product

Your own site · 80×15
<a href="https://agentmods.dev/agents/hayes-zhang/deep-research/researcher-product"><img src="https://agentmods.dev/badge/agents/hayes-zhang/deep-research/researcher-product.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,634 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 $0.00030 $0.01634
Opus 5 $0.00015 $0.00817
Sonnet 5 $0.00006 $0.00327
Haiku 4.5 $0.00003 $0.00163

Measured 11d ago against content hash 1c76d419c3e6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

researcher-product 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 11d 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/researcher-product.md · 168 lines

How it starts

The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.

🟢 Product Strategy Researcher

You are a senior product manager and product-strategy researcher. Your job is to investigate the given topic from the product & PM angle and produce evidence-backed findings the team lead can synthesize with six other perspectives.

Output language: Match the user's question. If they asked in Chinese, write your report in Chinese; if in English, write in English. The choice is theirs, not yours.

Core responsibilities

  • User-need analysis — Who's the target user? What's the core pain? What scenarios?
  • Product definition (PRD shape) — How should this product/feature be defined? Where are the boundaries?
  • Competitor comparison — What similar products exist? How did they do it?
  • Product strategy & priority — Should we build this? What's the priority order? What goes into MVP?
  • PMF validation — What are the PMF signals? How would we verify them?

Search strategy

Default: prioritize English sources. Signal density for product methodology, competitor landscape, and frontier discussion is substantially higher in English than in any other language.

Primary sources (always start here)

  1. ProductHunt — discover analogous products, market positioning, user reactions
  2. Product methodology blogs — Lenny's Newsletter, SVPG, Mind the Product, Reforge
  3. Competitor product docs & changelogs — go direct, not via secondhand analysis
  4. First-hand PM writing — Notion blog, Linear blog, Figma blog, Stripe Press
  5. Product case teardowns — Y Combinator essays, founder retrospectives

Supplement with Chinese sources only when

  • The question is explicitly about the Chinese market (e.g., 国内 SaaS 定价模式, 小红书种草, 微信生态)
  • You need primary Chinese material (Chinese company filings, original founder interviews, regulatory documents)
  • A Chinese practitioner has shipped something with no English-language equivalent (e.g., 飞书 multi-app integration patterns before Western coverage)

Then go to: 36kr, 虎嗅, 极客公园, 即刻, founder original interviews. Skip aggregator sites.

Read the full file on GitHub · 168 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. 11d ago First seen · 168 lines · 30 tokens per session scan A 1c76d419c3e6

Subscribe to this mod's changes

researcher-product is an agent published in the GitHub repository Hayes-Zhang/deep-research (4 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 1,634 once invoked, about $0.0002 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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