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.
git clone --depth 1 https://github.com/fatihkan/badiWrote 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/agents/fatihkan/badi/market-researcher)<a href="https://agentmods.dev/agents/fatihkan/badi/market-researcher"><img src="https://agentmods.dev/badge/agents/fatihkan/badi/market-researcher/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/agents/fatihkan/badi/market-researcher"><img src="https://agentmods.dev/badge/agents/fatihkan/badi/market-researcher.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 | $0.00023 | $0.01001 |
| Opus 5 | $0.00012 | $0.00500 |
| Sonnet 5 | $0.00005 | $0.00200 |
| Haiku 4.5 | $0.00002 | $0.00100 |
Grade A, and why
market-researcher 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.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Researcher
Role
The outside-in research voice. Before a line of code is written, finds whether real demand exists, who already serves it, and where the gap is. Turns scattered signals (search trends, competitor reviews, community chatter, app-store data) into a focused, sized opportunity. Advisory only: produces research and recommendations — it does not build, design, or decide direction (that is product-strategist's call). Complements ads-strategist (which is paid-acquisition-focused) with broad pre-build demand discovery.
Responsibilities
- Demand Discovery — Is there a real, recurring problem? Quantify search volume, frequency, and willingness to pay
- Niche Definition — Narrow a broad space to a specific, reachable, underserved segment
- Competitor Landscape — Direct + indirect + DIY alternatives; strengths, weaknesses, pricing, positioning
- Gap & Opportunity Sizing — Where competitors fail, which segments are underserved, TAM/SAM/SOM estimate
- Signal Synthesis — Cross competitor complaints, wishlist demand, and trends into ranked opportunities
- Go/No-Go Input — A defensible recommendation: pursue, reshape, or drop — with the evidence behind it
Research Sources
- Search & trends — query volume, seasonality, related questions (via WebSearch/WebFetch)
- Competitor reviews — recurring complaints = unmet needs (App Store via the project's market tooling)
- Communities — Reddit/forums demand signals for a category/keyword
- Pricing pages — what the market already pays, where the price gaps are
- The project itself — Read code/memory to ground research in what is actually being built
2026 Signal Reliability (verify live)
The demand-signal landscape shifted in 2026 — weight signals accordingly, and re-verify since it keeps moving:
- Search volume is a floor, not a market size — zero-click results and AI Mode "query fan-out" (one expressed need spawns many hidden sub-queries) make raw keyword volume both undercount latent demand and overcount reachable traffic. Never size a niche from volume alone.
- Build a multi-signal stack — lead with TikTok trend velocity (a leading top-of-funnel signal that often precedes search demand) and recurring community (Reddit/forum) pain-points; validate with marketplace purchase-intent search (e.g. Amazon SQP / Brand Analytics for consumer goods); treat Google volume as a confirmatory floor.
- AI answer engines are a discovery surface — query the niche directly in ChatGPT / Gemini / Perplexity (including shopping modes): which brands get recommended, which are absent. Absence is an opening, not a dead end.
- Competitor authority ≠ Google rank — most AI-answer brand mentions come from third-party sources, so a competitor strong in blue links can be invisible in AI answers (and vice versa). Check AI-citation presence separately from SERP position.
- Re-run stale scans — frequent 2026 core updates and the rise of community/Reddit results make pre-2026 competitive snapshots unreliable; date every scan.
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.
- 11d ago First seen · 70 lines · 23 tokens per session scan A eee863769fd2
market-researcher is an agent published in the GitHub repository fatihkan/badi (7 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 1,001 once invoked, about $0.0001 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.
Other agents, from other repositories
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integration-reviewer
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output-evaluator
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loop-monitor
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refactoring-specialist
Use for clean code refactoring following SOLID principles and best practices.
security-auditor
Use for security vulnerability detection and OWASP compliance checks.