Borrowing it
Nothing to install: this file belongs to tan-yong-sheng/ai-vision-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tan-yong-sheng/ai-vision-mcp/main/.claude/skills/market-research/SKILL.mdgit clone --depth 1 https://github.com/tan-yong-sheng/ai-vision-mcpWrote 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/tan-yong-sheng/ai-vision-mcp/market-research)<a href="https://agentmods.dev/skills/tan-yong-sheng/ai-vision-mcp/market-research"><img src="https://agentmods.dev/badge/skills/tan-yong-sheng/ai-vision-mcp/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/tan-yong-sheng/ai-vision-mcp/market-research"><img src="https://agentmods.dev/badge/skills/tan-yong-sheng/ai-vision-mcp/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 | $0.00050 | $0.00449 |
| Opus 5 | $0.00025 | $0.00225 |
| Sonnet 5 | $0.00010 | $0.00090 |
| Haiku 4.5 | $0.00005 | $0.00045 |
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.
This is a copy
92% identical to market-research — 149 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Market Research
Produce research that supports decisions, not research theater.
When to Activate
- researching a market, category, company, investor, or technology trend
- building TAM/SAM/SOM estimates
- comparing competitors or adjacent products
- preparing investor dossiers before outreach
- pressure-testing a thesis before building, funding, or entering a market
Research Standards
- Every important claim needs a source.
- Prefer recent data and call out stale data.
- Include contrarian evidence and downside cases.
- Translate findings into a decision, not just a summary.
- Separate fact, inference, and recommendation clearly.
Common Research Modes
Investor / Fund Diligence
Collect:
- fund size, stage, and typical check size
- relevant portfolio companies
- public thesis and recent activity
- reasons the fund is or is not a fit
- any obvious red flags or mismatches
Competitive Analysis
Collect:
- product reality, not marketing copy
- funding and investor history if public
- traction metrics if public
- distribution and pricing clues
- strengths, weaknesses, and positioning gaps
Market Sizing
Use:
- top-down estimates from reports or public datasets
- bottom-up sanity checks from realistic customer acquisition assumptions
- explicit assumptions for every leap in logic
Technology / Vendor Research
Collect:
- how it works
- trade-offs and adoption signals
- integration complexity
- lock-in, security, compliance, and operational risk
Output Format
Default structure:
- executive summary
- key findings
- implications
- risks and caveats
- recommendation
- sources
Quality Gate
Before delivering:
- all numbers are sourced or labeled as estimates
- old data is flagged
- the recommendation follows from the evidence
- risks and counterarguments are included
- the output makes a decision easier
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 76 lines · 50 tokens per session scan A 7d5ba9b09206
market-research is a skill published in the GitHub repository tan-yong-sheng/ai-vision-mcp (78 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 449 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to market-research, differing in 149 lines, and is treated as a copy.
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