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 wentorai/research-plugins --skill finsight-research-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/finsight-research-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/finsight-research-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/finsight-research-guide/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/wentorai/research-plugins/finsight-research-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/finsight-research-guide.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 | $0.00017 | $0.00789 |
| Opus 5 | $0.00009 | $0.00394 |
| Sonnet 5 | $0.00003 | $0.00158 |
| Haiku 4.5 | $0.00002 | $0.00079 |
Grade A, and why
finsight-research-guide 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 6d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FinSight Research Guide
Overview
FinSight is a deep research agent designed specifically for financial analysis. Developed by RUC-NLPIR, it combines multi-source data retrieval, financial reasoning, and report generation to produce publication-ready financial research. It handles market analysis, company fundamentals, sector comparisons, and macroeconomic assessment through specialized agents.
Installation
git clone https://github.com/RUC-NLPIR/FinSight.git
cd FinSight && pip install -e .
Core Capabilities
Research Query to Report
from finsight import FinSightAgent
agent = FinSightAgent(llm_provider="anthropic")
# Generate comprehensive financial analysis
report = agent.research(
"Analyze the competitive landscape of the global EV battery "
"market. Compare CATL, LG Energy, and Panasonic on market "
"share, technology, margins, and growth outlook."
)
print(report.summary)
report.save("ev_battery_analysis.pdf")
Agent Architecture
| Agent | Role |
|---|---|
| Retrieval Agent | Fetches data from SEC filings, financial APIs, news |
| Data Agent | Processes financial statements, ratios, time series |
| Analysis Agent | Performs fundamental, technical, and comparative analysis |
| Reasoning Agent | Synthesizes findings, identifies trends and risks |
| Report Agent | Generates structured research reports with citations |
Financial Data Sources
# FinSight integrates with multiple data sources
config = {
"sec_edgar": True, # SEC filings (free)
"fred": True, # Federal Reserve economic data
"yahoo_finance": True, # Market data (free)
"news_api": True, # Financial news
"world_bank": True, # Macro indicators
}
Analysis Types
# Company fundamental analysis
report = agent.research(
"Provide a fundamental analysis of NVIDIA including "
"revenue trends, margin analysis, valuation multiples, "
"and competitive moat assessment."
)
# Sector analysis
report = agent.research(
"Compare the top 5 cloud computing companies by revenue "
"growth, operating margins, and R&D investment intensity."
)
# Macro analysis
report = agent.research(
"Analyze the impact of rising interest rates on US "
"commercial real estate valuations since 2022."
)
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.
- 6d ago First seen · 114 lines · 17 tokens per session scan A 71291352fea7
finsight-research-guide is a skill published in the GitHub repository wentorai/research-plugins (291 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 789 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 2026-09-03.
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