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.
npx skills add kelegele/oh-my-pm --skill market-researchgit clone --depth 1 https://github.com/kelegele/oh-my-pmWrote 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.
[](https://agentmods.dev/skills/kelegele/oh-my-pm/market-research)<a href="https://agentmods.dev/skills/kelegele/oh-my-pm/market-research"><img src="https://agentmods.dev/badge/skills/kelegele/oh-my-pm/market-research/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.
<a href="https://agentmods.dev/skills/kelegele/oh-my-pm/market-research"><img src="https://agentmods.dev/badge/skills/kelegele/oh-my-pm/market-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00169 | $0.04817 |
| Opus 5 | $0.00084 | $0.02409 |
| Sonnet 5 | $0.00034 | $0.00963 |
| Haiku 4.5 | $0.00017 | $0.00482 |
Grade A, and why
market-research 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.
How it starts
The opening of the file, as written. The whole thing — 447 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Research
统一研究入口：市场面看清赛道，用户面看懂用户。
What This Skill Does
按研究交付物分为两种模式：
- 市场面 (Market Intelligence)：搜索驱动的市场趋势、市场规模（TAM-SAM-SOM）、关键玩家、机会与威胁分析，交付
market-analysis.json+ Markdown 报告 - 用户面 (User Research)：访谈提纲、调研设计、用户画像与洞察，交付
user-research.json+ Markdown 报告
两种模式可独立运行，也可组合执行（先市场面定赛道，再用户面定人群）。
When to Use
市场面 — activate when:
- User asks about market trends or industry developments
- Phrases like "market research", "industry analysis", "market size", "X 市场发生了什么"
- Exploring new markets or product categories
- Need to understand market dynamics before product decisions
用户面 — activate when:
- User asks about understanding user needs or behaviors
- Phrases like "user research", "talk to users", "用户想要什么", "create personas"
- Creating or updating user personas
- Analyzing user feedback or interview data
- Need to validate product ideas with real users
How It Works
- Determine mode — 根据用户意图选择市场面、用户面或两者组合；意图不明时询问用户
- 市场面: Define market scope → Search industry sources → Analyze trends → Assess market size (TAM/SAM/SOM) → Identify opportunities → Generate insights
- 用户面: Define research goals → Select methods → Create instruments (interview guides, surveys) → Gather data → Analyze findings → Create artifacts (personas, journey maps, insights)
Research Modes
市场面 (Market Intelligence)
搜索驱动：所有市场数据必须来自实际搜索，禁止仅凭训练记忆输出（见 Anti-Hallucination Rules）。
分析深度：
| Depth | When to Use | What You Get |
|---|---|---|
overview |
Quick market check | Market size, 3 trends, 5 key players |
standard |
Regular analysis (default) | Full sizing, trends, opportunities, threats, recommendations |
deep |
Strategic decisions | Segmentation analysis, buying patterns, vendor landscape, 10+ recommendations |
用户面 (User Research)
研究方法：
| Method | When to Use | Output |
|---|---|---|
interviews |
Deep understanding of needs | Interview transcripts, key quotes |
surveys |
Broad pattern validation | Survey results, statistical insights |
personas |
User archetype creation | Persona profiles |
journey-mapping |
Experience optimization | User journey maps |
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 · 447 lines · 169 tokens per session scan A 2dcb20e1af60
market-research is a skill published in the GitHub repository kelegele/oh-my-pm (4 stars, last pushed 1mo ago), licensed MIT. It adds 169 tokens to every session and 4,817 once invoked, about $0.0008 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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