Finance Skills is a collection of agent skills for financial analysis and trading, covering activities such as company valuation, earnings research, market analysis, and options calculations. It is for users who want coding agents to perform structured finance workflows, and the catalogue contains its skills, plugins, instructions, and MCP integration.
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 himself65/finance-skills --skill sepa-strategygit clone --depth 1 https://github.com/himself65/finance-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/himself65/finance-skills/sepa-strategy)<a href="https://agentmods.dev/skills/himself65/finance-skills/sepa-strategy"><img src="https://agentmods.dev/badge/skills/himself65/finance-skills/sepa-strategy/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/himself65/finance-skills/sepa-strategy"><img src="https://agentmods.dev/badge/skills/himself65/finance-skills/sepa-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00194 | $0.03229 |
| Opus 5 | $0.00097 | $0.01614 |
| Sonnet 5 | $0.00039 | $0.00646 |
| Haiku 4.5 | $0.00019 | $0.00323 |
Grade A, and why
sepa-strategy 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 11d 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEPA Strategy Analysis
Analyze stocks using Mark Minervini's SEPA (Specific Entry Point Analysis) framework — a complete system for identifying high-probability growth stock entries with strict risk management.
Core philosophy: Buy the right stock, in the right stage, at a precise entry point, with strict risk controls. Win rate is ~50-55% — profitability comes from asymmetric risk/reward (small losses, large gains), not from predicting direction.
This skill is for educational/analytical purposes only. It does not constitute investment advice. Never execute trades based solely on this analysis.
Step 1: Gather Stock Data
Collect the following data for the stock. Use yfinance or any available market data tool.
| Data needed | Purpose |
|---|---|
| Current price | Trend template check |
| 50-day, 150-day, 200-day moving averages | MA alignment verification |
| 52-week high and low | Price position check |
| 200MA value from 1 month ago and 4-5 months ago | MA200 slope direction |
| 20-day average volume + today's volume | Volume ratio analysis |
| Recent quarterly EPS (last 3-4 quarters) | EPS growth & acceleration |
| Annual EPS (last 3 years) | Long-term growth trend |
| Recent quarterly revenue (last 3-4 quarters) | Revenue growth check |
| Gross margin and net margin trend | Margin health |
| Institutional ownership changes (if available) | Smart money signal |
| RS rating or 12-month relative performance vs S&P 500 | Relative strength |
| Price history for pattern recognition | VCP / chart pattern analysis |
If certain data is unavailable, note it and proceed with what you have. Missing RS rating is a significant gap — flag it.
Step 2: Stage Analysis — Identify the Current Stage
Every stock cycles through four stages. Read references/stage-analysis.md for full details.
Determine which stage the stock is in:
| Stage | Characteristics | Action |
|---|---|---|
| Stage 1 — Basing | Price near 200MA, MA flat/declining, MAs tangled, low volume | Do nothing, wait |
| Stage 2 — Advancing | Making higher highs/lows, bullish MA alignment, volume on up days | Only stage to buy |
| Stage 3 — Topping | Wide swings at highs, frequent false breakouts, heavy volume without progress | Reduce, no new positions |
| Stage 4 — Declining | Below all MAs, bearish alignment, bounces are selling opportunities | Full cash, stay away |
What ships with it
8 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.
- 11d ago First seen · 251 lines · 194 tokens per session scan A 0a696407966a
sepa-strategy is a skill published in the GitHub repository himself65/finance-skills (3,309 stars, last pushed 14d ago), licensed MIT. It adds 194 tokens to every session and 3,229 once invoked, about $0.0010 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 2026-08-30.
Other skills, from other repositories
quotient
Prediction-market intelligence for Polymarket agents. Quotient runs a multi-role AI forecasting pipeline over 1,600+ sources and publishes daily trade signals with side, entry prices, conviction tiers, capacity, and convergence reads. Pull forecasts (with what-changed deltas), recent sources (articles + X posts), the…
delu-oracle
Full-cognition token analysis for Base EVM tokens via the deluagent oracle. Pass a CA or cashtag, get back a flat decision header (action, conviction, entry/stop/size, read) plus full cognition report. Tiered x402 pricing — 100M+ DELU free, 50M+ 50k DELU, public 250k DELU. Sequential calls only.
lonestaroracle-data
Live pay-per-call data for crypto and DeFi protocol risk, funding rates, open interest, liquidations, stablecoin health, macro, equities, and on-chain intelligence — settled per query in USDC on Base via x402, no signup or API key.
backtesting-sim
Backtesting and simulation: vectorized backtesting, paper trading simulation, strategy A/B testing, automated strategy building, natural language to strategy, and trading plan generation. USE FOR: backtest, backtesting, paper trading, simulation, strategy builder, A/B test strategies, natural language strategy…
cross-asset-relationships
Cross-asset and quantitative analysis: pair correlations, correlation heatmaps, currency strength, cross-timeframe divergence, intermarket analysis, market breadth, carry trades, swap rates, risk premia, and multi-pair baskets. USE FOR: correlation, currency strength, intermarket, market breadth, carry trade, swap…
freqtrade-bot
Freqtrade — open-source Python crypto trading bot. Backtesting, hyperopt (ML parameter optimization), FreqAI (self-training adaptive strategies), Telegram + WebUI control. Supports Binance, Kraken, Bybit, OKX, Gate.io (spot + futures). SQLite trade h.