Borrowing it
Nothing to install: this file belongs to lglucas/ai-dev-operating-system. 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/lglucas/ai-dev-operating-system/main/.claude/agents/market-research-agent.mdgit clone --depth 1 https://github.com/lglucas/ai-dev-operating-systemWrote 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/lglucas/ai-dev-operating-system/market-research-agent)<a href="https://agentmods.dev/agents/lglucas/ai-dev-operating-system/market-research-agent"><img src="https://agentmods.dev/badge/agents/lglucas/ai-dev-operating-system/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.
<a href="https://agentmods.dev/agents/lglucas/ai-dev-operating-system/market-research-agent"><img src="https://agentmods.dev/badge/agents/lglucas/ai-dev-operating-system/market-research-agent.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 | $0.00092 | $0.00144 |
| Opus 5 | $0.00046 | $0.00072 |
| Sonnet 5 | $0.00018 | $0.00029 |
| Haiku 4.5 | $0.00009 | $0.00014 |
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 9d 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.
What it actually says
Market Research Agent
Research the market and niche. Prefer credible reports, associations, official data, sector publications, and primary sources.
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.
- 9d ago First seen · 11 lines · 92 tokens per session scan A 31cd2adffe8f
market-research-agent is an agent published in the GitHub repository lglucas/ai-dev-operating-system (11 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 144 once invoked, about $0.0005 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.
Other agents, from other repositories
planner
Plan execution: turn approved intent/specs into a sequenced plan scaled to size. Full subagent.
verifier
Skeptical, read-only validator. Use after work is claimed complete to confirm it actually works — runs checks, tests edge cases, and reports what passed vs. what is incomplete or broken.
troth-researcher
Deep-dive codebase researcher. Use this subagent to explore large/broad questions ("how does auth work across the repo", "find every place X is called") without polluting the main session's context. Returns a dense synthesis, not raw dumps.
system-architect
Design MVP-first architectures with opensource preference.
orchestrator
Route to agents, execute workflows, discover resources.
tech-spec
Specialized agent for generating technical specifications based on requirements. Creates architecture documents, database schemas, API specs, and UI wireframes only when needed, ensuring technical details support user story implementation.