Firecrawl Web Agent is an open-source foundation for building AI agents that research the web with structured results. Developers can use its templates, agent core, web tools, and SDK to create applications that plan research tasks, interact with websites, and combine observations into answers. The catalogue entries are skills for this web-research agent.
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 agentmods add skills/firecrawl/web-agent/financial-researchnpx skills add firecrawl/web-agent --skill financial-researchgit clone --depth 1 https://github.com/firecrawl/web-agentWrote 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/firecrawl/web-agent/financial-research)<a href="https://agentmods.dev/skills/firecrawl/web-agent/financial-research"><img src="https://agentmods.dev/badge/skills/firecrawl/web-agent/financial-research.svg" alt="Measured on agentmods" 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 | $0.00093 | $0.01087 |
| Opus 5 | $0.00046 | $0.00544 |
| Sonnet 5 | $0.00019 | $0.00217 |
| Haiku 4.5 | $0.00009 | $0.00109 |
Grade A, and why
financial-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 5d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Financial Research
Extract official financials from SEC filings and cross-reference with analyst consensus. Public companies only.
When to use
- User asks for a company's financials: "revenue of NVIDIA", "Apple's latest 10-K"
- User asks for analyst sentiment: "what are analysts saying about TSLA?"
- User provides a ticker with no clear verb: "MSFT" → assume they want a financial overview
- User wants an earnings summary before or after a release
Do NOT use for private company research, crypto, or macro/market commentary — deep-research handles those better.
Strategy
-
Resolve the ticker.
- User gave a ticker: use it directly.
- User gave a company name: search
"<company> stock ticker"and confirm from the top result.
-
Get the latest SEC filing.
- Use the sec.gov playbook to locate the most recent 10-K (annual) or 10-Q (quarterly).
- Scrape the filing index page, then the primary filing document.
- Extract: revenue, net income, operating income, EPS (basic and diluted), gross margin, forward guidance if given.
-
Get analyst consensus.
- Use the finance.yahoo.com playbook for the analyst tab.
- Extract: consensus rating, average / low / high price target, number of analysts covering.
-
Cross-reference.
- If the user asked for a specific metric, verify it against at least two sources.
- Flag discrepancies between the filing and third-party aggregators.
-
Call
formatOutputwith the structured result.
Quick start
// Full financial overview
await agent.run({
prompt: 'Get a complete financial overview of NVIDIA',
skills: ['financial-research'],
format: 'json',
})
// Specific metric
await agent.run({
prompt: 'What was AAPL revenue in the most recent quarter?',
skills: ['financial-research'],
})
// Earnings preparation for multiple tickers — delegate per-ticker
await agent.run({
prompt: 'Get the latest reported revenue, EPS, and guidance for NVDA, AMD, and INTC',
skills: ['financial-research'],
format: 'json',
})
What ships with it
2 files 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.
- 5d ago First seen · 115 lines · 93 tokens per session scan A 8687a7aa605a
financial-research is a skill published in the GitHub repository firecrawl/web-agent (1,223 stars, last pushed 4mo ago), licensed MIT. It adds 93 tokens to every session and 1,087 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.
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