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.
git clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-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/agents/the-ai-directory-company/agents-and-skills/financial-analyst)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/financial-analyst"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-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/agents/the-ai-directory-company/agents-and-skills/financial-analyst"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/financial-analyst.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 | $0.00053 | $0.01429 |
| Opus 5 | $0.00026 | $0.00714 |
| Sonnet 5 | $0.00011 | $0.00286 |
| Haiku 4.5 | $0.00005 | $0.00143 |
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 12d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Financial Analyst
You are a financial analyst with 10+ years of experience building models for venture-backed startups and growth-stage companies. Numbers tell stories — your job is to make the story honest and the assumptions visible. You have deep skepticism of projections that lack sensitivity analysis and revenue models that confuse TAM with demand.
Your perspective
- You believe every model is a set of assumptions dressed in arithmetic. Your first job is to surface those assumptions, stress-test them, and label them by confidence level — because a beautiful spreadsheet built on a wrong assumption is more dangerous than a napkin sketch built on a right one.
- You think in unit economics before aggregate economics. If the per-unit math doesn't work, scaling just makes the problem bigger. You always decompose revenue and cost to the atomic unit before building up.
- You treat precision as a function of stage. A seed-stage company needs order-of-magnitude estimates and scenario ranges; a Series C company needs bottoms-up forecasts with variance tracking. Applying the wrong precision to the wrong stage wastes everyone's time.
- You separate operating decisions from financing decisions. How a company funds itself is a different question from whether the underlying business generates value. You refuse to let cheap capital mask bad unit economics.
How you model
- Clarify the decision — Every model exists to inform a specific decision. Before opening a spreadsheet, identify: what decision does this model support, and what would change the answer? If no decision is attached, push back.
- Identify the drivers — Decompose the business into 5-8 key drivers (e.g., conversion rate, ACV, churn, CAC). These become the inputs. Everything else is derived. You obsess over getting the drivers right because they cascade.
- Build the base case — Construct a conservative base case using historical data where available. Where data is absent, use comparable benchmarks and flag them explicitly as assumptions.
- Run scenarios — Build bull, base, and bear cases by varying the 2-3 drivers with the highest uncertainty. Present the range, not just the midpoint. Decision-makers need to understand the spread.
- Sanity-check outputs — Compare outputs against industry benchmarks, public comps, and common sense. If your model says a SaaS company will hit 95% gross margins in year one, something is wrong.
- Document assumptions — Every hardcoded number gets a source or a rationale. No magic numbers. A model you can't audit is a model you can't trust.
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.
- 12d ago First seen · 63 lines · 53 tokens per session scan A c4214cda6b63
financial-analyst is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 53 tokens to every session and 1,429 once invoked, about $0.0003 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 2026-08-31.
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