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 oyi77/1ai-skills --skill market-research-agentgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote 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/oyi77/1ai-skills/market-research-agent)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/market-research-agent"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/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/skills/oyi77/1ai-skills/market-research-agent"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/market-research-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.00736 |
| Opus 5.5 | $0.00023 | $0.00294 |
| Sonnet 5.5 | $0.00012 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00074 |
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 1mo 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Research Agent
When to Use
Trigger phrases:
-
"market research agent"
-
"Analyze markets, competitors, user segments, and trends to produce evidence-base"
-
Evaluating market opportunity before building a product
-
Analyzing competitors before launching or pivoting
-
Pricing strategy research for new or existing products
-
Understanding user segments and their needs
-
Tracking market trends and technology shifts
-
Due diligence for investment or acquisition decisions
-
Go-to-market planning for new features or products
When NOT to Use
- When the task is simple enough for a single command
- When real-time human judgment is required
- When the agent lacks access to required tools or data
Overview
Market Research Agent is an AI agent skill for agent orchestration. It enables autonomous execution of complex tasks with minimal human intervention.
Capabilities
- Autonomous operation — Execute multi-step market research agent workflows independently
- Context awareness — Adapt behavior based on current state and history
- Error recovery — Handle failures gracefully with retry and fallback logic
- Integration — Connect with external tools and services as needed
Workflow
# Example: Agent orchestration
from dataclasses import dataclass
@dataclass
class Task:
name: str
priority: int
assigned_agent: str
def orchestrate(tasks: list[Task]) -> dict:
results = {}
for task in sorted(tasks, key=lambda t: t.priority):
results[task.name] = execute(task)
return results
- Initialize — Set up the agent context and load required resources
- Plan — Break down the task into executable steps
- Execute — Run each step, monitoring for errors and adapting as needed
- Verify — Validate results against acceptance criteria
- Report — Summarize outcomes and suggest next steps
Configuration
- Define task objectives and constraints clearly
- Set appropriate timeout and retry limits
- Configure tool access and permissions
- Enable logging for debugging and audit
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.
- 1mo ago First seen · 109 lines · 58 tokens per session scan A a89c96566f98
market-research-agent is a skill published in the GitHub repository oyi77/1ai-skills (17 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 736 once invoked, about $0.0002 per session on Opus 5.5. 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.
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agent-creation-skill
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orchestration
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foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.