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 Ruinius/financial-analyst-skills --skill financial_data_extractiongit clone --depth 1 https://github.com/Ruinius/financial-analyst-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/ruinius/financial-analyst-skills/financial_data_extraction)<a href="https://agentmods.dev/skills/ruinius/financial-analyst-skills/financial_data_extraction"><img src="https://agentmods.dev/badge/skills/ruinius/financial-analyst-skills/financial_data_extraction/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/ruinius/financial-analyst-skills/financial_data_extraction"><img src="https://agentmods.dev/badge/skills/ruinius/financial-analyst-skills/financial_data_extraction.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 | $0.00041 | $0.04163 |
| Opus 5.5 | $0.00016 | $0.01665 |
| Sonnet 5.5 | $0.00008 | $0.00833 |
| Haiku 4.5 | $0.00004 | $0.00416 |
Grade A, and why
Financial Data Extraction 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 — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Financial Data Extraction (Phase 2)
Prerequisites
- Classification metadata available in
processing_data/TICKER_DOCTYPE_DATE_temp.md - If Tiger-Transformer is not running on localhost:8000 then ask the user to run
.\tools\start_transformer.bat - If a static file server is not running on localhost:8181 then ask the user to run
.\tools\start_file_server.bat
DO NOT EVER start servers without human user.
Overview
This skill extracts four financial components from the PDF and appends structured markdown tables to the document's .md file:
| Component | Requires Transformer | Dependency | Conditional |
|---|---|---|---|
| Balance Sheet | Yes (/predict/balance-sheet) |
None | No |
| Income Statement + Shares | Yes (/predict/income-statement) |
None | No |
| Organic Growth | No (LLM-only) | Income Statement (needs revenue) | No |
| GAAP Reconciliation | No (LLM-only) | None | earnings_announcement only |
Output Script
After extracting raw data into a JSON file, run the centralized Python script:
python skills/financial_data_extraction/scripts/transform_and_append.py --json <extracted.json> --md <output.md>
The script handles:
- Tiger-Transformer API calls for balance sheet and income statement standardization
- Expense sign normalization (expenses → negative)
- Accumulated depreciation sign correction
- Organic growth calculation (simple YoY with organic growth override)
- Markdown table formatting and appending
The script is idempotent — re-running will skip sections that already exist in the output file.
See resources/transformer_example.py for a standalone template on querying the Transformer API directly.
Step 1: Single-Pass PDF Read
Open the PDF in the browser ONCE and scroll through to extract ALL raw data needed for every component below. Collect it into a temporary JSON structure or your scratchpad. Close the browser. All subsequent steps run offline from this raw data.
What to capture during the single pass:
What ships with it
4 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.
- 1mo ago First seen · 387 lines · 41 tokens per session scan A 58c840255ec4
Financial Data Extraction is a skill published in the GitHub repository Ruinius/financial-analyst-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 41 tokens to every session and 4,163 once invoked, about $0.0002 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 2026-08-31.
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