Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/staskh/trading_skillsnpx agentmods add skills/staskh/trading_skills/report-stockWrote 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/staskh/trading_skills/report-stock)<a href="https://agentmods.dev/skills/staskh/trading_skills/report-stock"><img src="https://agentmods.dev/badge/skills/staskh/trading_skills/report-stock/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/staskh/trading_skills/report-stock"><img src="https://agentmods.dev/badge/skills/staskh/trading_skills/report-stock.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 | $0.00022 | $0.00877 |
| Opus 5.5 | $0.00009 | $0.00351 |
| Sonnet 5.5 | $0.00004 | $0.00175 |
| Haiku 4.5 | $0.00002 | $0.00088 |
Grade A, and why
report-stock 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stock Analysis Report Generator
Generates professional reports with comprehensive stock analysis including trend analysis, PMCC viability, and fundamental metrics. Supports both PDF and markdown output formats.
Instructions
Step 1: Gather Data
Run the report script for each symbol:
uv run python scripts/report.py SYMBOL
The script returns detailed JSON with:
recommendation- Overall recommendation with strengths/riskscompany- Company info (name, sector, industry, market cap)trend_analysis- Bullish scanner results (score, RSI, MACD, ADX, SMAs)pmcc_analysis- PMCC viability (score, LEAPS/short details, metrics)fundamentals- Valuation, profitability, dividend, balance sheet, earnings historypiotroski- F-Score breakdown with all 9 criteriaspread_strategies- Option spread analysis (vertical spreads, straddle, strangle, iron condor)
Step 2: Generate Report
Step 2a — Write markdown
Read templates/markdown-template.md for formatting instructions. Generate a markdown report from the JSON data and save to sandbox/ as:
sandbox/{SYMBOL}_Analysis_Report_{YYYY-MM-DD}_{HHmm}.md
Step 2b — Convert to PDF (if requested)
Invoke the markdown-to-pdf skill on the markdown file just created:
uv run python .claude/skills/markdown-to-pdf/scripts/markdown_to_pdf.py sandbox/{SYMBOL}_Analysis_Report_{YYYY-MM-DD}_{HHmm}.md
The PDF is written alongside the markdown file with the same basename.
Step 3: Report Results
After generating the report, tell the user:
- The recommendation (BUY/HOLD/AVOID)
- Key strengths and risks
- The report file path
Example
# Single symbol
uv run python scripts/report.py AAPL
# Multiple symbols - run separately
uv run python scripts/report.py AAPL
uv run python scripts/report.py MSFT
Report Contents
All sections defined in templates/markdown-template.md:
- Header — symbol, company name, generated timestamp
- Recommendation — BUY/HOLD/AVOID with strengths and risks
- Company Overview — sector, industry, market cap, beta
- Trend Analysis — bullish score, RSI, MACD, ADX, SMA distances, earnings date, signals list
- Fundamental Analysis — valuation (P/E, P/B, EPS), profitability (margins, ROE, ROA, growth), dividend & balance sheet, earnings history (up to 8 quarters)
- Piotroski F-Score — all 9 criteria with PASS/FAIL
- Insider Trading — net sentiment, buy/sell counts, recent transactions (omitted if no data)
- PMCC Viability — score, IV, LEAPS/short leg details, trade metrics (yield, capital required)
- Option Spread Strategies — bull call, bear put, straddle, strangle, iron condor
- Investment Summary — strengths and risk factors
- Disclaimer footer
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.
- 1mo ago First seen · 96 lines · 22 tokens per session scan A 5d06ead01fbc
report-stock is a skill published in the GitHub repository staskh/trading_skills (393 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 877 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 2026-08-30.
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