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 seaworld008/Commonly-used-high-value-skills --skill financial-analystgit clone --depth 1 https://github.com/seaworld008/Commonly-used-high-value-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/seaworld008/commonly-used-high-value-skills/financial-analyst)<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/financial-analyst"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/financial-analyst/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/seaworld008/commonly-used-high-value-skills/financial-analyst"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/financial-analyst.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 | $0.00027 | $0.01639 |
| Opus 5.5 | $0.00011 | $0.00656 |
| Sonnet 5.5 | $0.00005 | $0.00328 |
| Haiku 4.5 | $0.00003 | $0.00164 |
Grade A, and why
financial-analyst 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Financial Analyst Skill
When to Use
Use this skill when the user wants to:
- run ratio analysis or valuation work
- build or review a DCF
- analyze budget variance or forecast performance
- turn financial statement data into executive insights
Usage
Recommended flow:
scope analysis goal
-> collect and validate inputs
-> run the appropriate model
-> interpret outputs
-> present insights and follow-up actions
Overview
Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial analysts with 3-6 years experience performing financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis.
5-Phase Workflow
Phase 1: Scoping
- Define analysis objectives and stakeholder requirements
- Identify data sources and time periods
- Establish materiality thresholds and accuracy targets
- Select appropriate analytical frameworks
Phase 2: Data Analysis & Modeling
- Collect and validate financial data (income statement, balance sheet, cash flow)
- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
- Build DCF models with WACC and terminal value calculations
- Construct budget variance analyses with favorable/unfavorable classification
- Develop driver-based forecasts with scenario modeling
Phase 3: Insight Generation
- Interpret ratio trends and benchmark against industry standards
- Identify material variances and root causes
- Assess valuation ranges through sensitivity analysis
- Evaluate forecast scenarios (base/bull/bear) for decision support
Phase 4: Reporting
- Generate executive summaries with key findings
- Produce detailed variance reports by department and category
- Deliver DCF valuation reports with sensitivity tables
- Present rolling forecasts with trend analysis
Phase 5: Follow-up
- Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)
- Monitor report delivery timeliness (target: 100% on time)
- Update models with actuals as they become available
- Refine assumptions based on variance analysis
What ships with it
12 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.
- assets/dcf_analysis_template.md 5.8 KB
- assets/expected_output.json 5.1 KB
- assets/forecast_report_template.md 5.1 KB
- assets/sample_financial_data.json 6.3 KB
- assets/variance_report_template.md 3.8 KB
- references/financial-ratios-guide.md 9.0 KB
- references/forecasting-best-practices.md 9.8 KB
- references/valuation-methodology.md 7.7 KB
- scripts/budget_variance_analyzer.py 14 KB runs code
- scripts/dcf_valuation.py 16 KB runs code
- scripts/forecast_builder.py 19 KB runs code
- scripts/ratio_calculator.py 16 KB runs code
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 Changed 98cc5c391ae3
- 1mo ago First seen · 210 lines · 27 tokens per session scan A 141a5093749c
financial-analyst is a skill published in the GitHub repository seaworld008/Commonly-used-high-value-skills (73 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 1,639 once invoked, about $0.0001 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 2026-09-03.
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