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 serejaris/kimi-skills --skill financial-statement-analyzergit clone --depth 1 https://github.com/serejaris/kimi-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/serejaris/kimi-skills/financial-statement-analyzer)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/financial-statement-analyzer"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/financial-statement-analyzer/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/serejaris/kimi-skills/financial-statement-analyzer"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/financial-statement-analyzer.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 | $0.00065 | $0.02143 |
| Opus 5 | $0.00032 | $0.01071 |
| Sonnet 5 | $0.00013 | $0.00429 |
| Haiku 4.5 | $0.00006 | $0.00214 |
Grade A, and why
financial-statement-analyzer 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 9d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Financial Statement Analysis — YoY/QoQ Trends + Anomaly Detection
Perform structured analysis of a company's income statement, balance sheet, and cash flow statement: automatically compute year-over-year (YoY) and quarter-over-quarter (QoQ) changes, run multi-dimensional anomaly detection rules (AR surge, cash flow divergence from profit, inventory buildup, gross margin shifts, etc.), and produce a readable report.
Capabilities
| Capability | Description |
|---|---|
| YoY Analysis | Compare same-period data (e.g., 2024Q1 vs 2023Q1) to identify trend changes |
| QoQ Analysis | Compare consecutive periods (e.g., 2024Q2 vs 2024Q1) to capture short-term fluctuations |
| Financial Ratios | Gross margin, net margin, debt-to-asset ratio, current ratio, DSO, and more |
| Anomaly Detection | 10 built-in rules with automatic scanning, risk severity levels, and explanations |
Quick Start
# Basic usage: analyze financial data in JSON format
python scripts/analyze_financials.py data.json
# Output results in JSON format
python scripts/analyze_financials.py data.json --json
# Export to a file
python scripts/analyze_financials.py data.json --output report.json
# Generate sample data file (for testing)
python scripts/analyze_financials.py --sample > sample_data.json
# Customize anomaly detection thresholds
python scripts/analyze_financials.py data.json --ar-threshold 0.25 --ocf-ratio 0.4
Input Data Format
The script accepts a JSON file in the following format:
{
"company": "Acme Corp",
"currency": "USD",
"unit": "thousands",
"periods": ["2023Q1","2023Q2","2023Q3","2023Q4","2024Q1","2024Q2","2024Q3","2024Q4"],
"income_statement": {
"revenue": [5000, 5200, 4800, 6000, 5500, 5800, 5100, 6500],
"cost_of_revenue": [3000, 3100, 2900, 3500, 3400, 3600, 3200, 4100],
"operating_income": [800, 850, 750, 1000, 780, 820, 700, 900],
"net_income": [600, 650, 560, 780, 580, 620, 520, 680]
},
"balance_sheet": {
"accounts_receivable": [2000, 2100, 2200, 2300, 2800, 3200, 3600, 4200],
"inventory": [1000, 1050, 1100, 1200, 1100, 1150, 1200, 1300],
"total_current_assets": [5000, 5200, 5400, 5800, 6000, 6500, 7000, 7500],
"goodwill": [500, 500, 500, 500, 500, 500, 500, 500],
"total_assets": [15000, 15500, 16000, 16500, 17000, 17500, 18000, 18500],
"accounts_payable": [1500, 1600, 1550, 1700, 1650, 1750, 1700, 1800],
"total_current_liabilities": [4000, 4200, 4100, 4500, 4300, 4600, 4500, 4900],
"total_liabilities": [8000, 8200, 8400, 8600, 8800, 9000, 9200, 9500],
"total_equity": [7000, 7300, 7600, 7900, 8200, 8500, 8800, 9000]
},
"cash_flow": {
"operating_cash_flow": [700, 750, 620, 850, 300, 280, 250, 200],
"investing_cash_flow": [-200, -180, -250, -300, -400, -350, -300, -280],
"financing_cash_flow": [-100, -50, -80, -120, 200, 150, 100, 50],
"capex": [180, 160, 230, 280, 380, 330, 280, 260]
}
}
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.
- 9d ago First seen · 157 lines · 65 tokens per session scan A 90b3634b2e73
financial-statement-analyzer is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 2,143 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 2026-09-03.
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