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/dirtybits/agent-skills/financial-analysisnpx skills add dirtybits/agent-skills --skill financial-analysisgit clone --depth 1 https://github.com/dirtybits/agent-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/dirtybits/agent-skills/financial-analysis)<a href="https://agentmods.dev/skills/dirtybits/agent-skills/financial-analysis"><img src="https://agentmods.dev/badge/skills/dirtybits/agent-skills/financial-analysis.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 5.1 | $0.00074 | $0.02222 |
| Opus 5 | $0.00037 | $0.01111 |
| Sonnet 5 | $0.00015 | $0.00444 |
| Haiku 4.5 | $0.00007 | $0.00222 |
Grade A, and why
financial-analysis 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Financial Analysis
Core Stack
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import openpyxl
CSV / Data Wrangling
Loading financial CSVs
df = pd.read_csv("data.csv", thousands=",", parse_dates=["Date"])
df.columns = df.columns.str.strip().str.lower().str.replace(" ", "_")
Common cleaning steps
df["revenue"] = pd.to_numeric(df["revenue"], errors="coerce")
df = df.dropna(subset=["revenue"])
df = df.sort_values("date").reset_index(drop=True)
Period aggregations
df["year"] = df["date"].dt.year
annual = df.groupby("year").agg({"revenue": "sum", "ebitda": "sum"})
annual["margin"] = annual["ebitda"] / annual["revenue"]
Financial Modeling Patterns
DCF skeleton
# Inputs
revenue_base = 100_000_000
growth_rates = [0.15, 0.12, 0.10, 0.08, 0.06]
ebitda_margin = 0.25
da_pct = 0.04
capex_pct = 0.05
nwc_pct = 0.03 # projected NWC as % of revenue; FCF uses the CHANGE in NWC
tax_rate = 0.25
wacc = 0.10
terminal_growth = 0.025
net_debt = 20_000_000
shares_out = 10_000_000
# Projections
revenues = [revenue_base * np.prod([1 + g for g in growth_rates[: i + 1]]) for i in range(5)]
ebitda = [r * ebitda_margin for r in revenues]
da = [r * da_pct for r in revenues]
ebit = [e - d for e, d in zip(ebitda, da)]
nopat = [x * (1 - tax_rate) for x in ebit]
capex = [r * capex_pct for r in revenues]
nwc = [r * nwc_pct for r in revenues]
change_nwc = [nwc[0] - revenue_base * nwc_pct] + [nwc[i] - nwc[i - 1] for i in range(1, len(nwc))]
fcf = [n + d - c - dnwc for n, d, c, dnwc in zip(nopat, da, capex, change_nwc)]
# Terminal value sanity: terminal_growth must be below WACC.
assert terminal_growth < wacc, "terminal growth must be below WACC"
terminal_value = fcf[-1] * (1 + terminal_growth) / (wacc - terminal_growth)
discount_factors = [(1 / (1 + wacc)) ** (i + 1) for i in range(5)]
enterprise_value = sum(f * d for f, d in zip(fcf, discount_factors)) + terminal_value * discount_factors[-1]
equity_value = enterprise_value - net_debt
implied_share_price = equity_value / shares_out
What ships with it
3 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 · 230 lines · 74 tokens per session scan A 4d020dba7060
financial-analysis is a skill published in the GitHub repository dirtybits/agent-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 74 tokens to every session and 2,222 once invoked, about $0.0004 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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