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 haskaomni/serenity-skill --skill gf-dma-health-indexgit clone --depth 1 https://github.com/haskaomni/serenity-skillWrote 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/haskaomni/serenity-skill/gf-dma-health-index)<a href="https://agentmods.dev/skills/haskaomni/serenity-skill/gf-dma-health-index"><img src="https://agentmods.dev/badge/skills/haskaomni/serenity-skill/gf-dma-health-index/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/haskaomni/serenity-skill/gf-dma-health-index"><img src="https://agentmods.dev/badge/skills/haskaomni/serenity-skill/gf-dma-health-index.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 | $0.00097 | $0.03264 |
| Opus 5 | $0.00048 | $0.01632 |
| Sonnet 5 | $0.00019 | $0.00653 |
| Haiku 4.5 | $0.00010 | $0.00326 |
Grade A, and why
gf-dma-health-index 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 13d 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 — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GF-DMA Health Index
Core Idea
Evaluate whether a stock's current price trend is supported by fundamental speed and moving-average structure.
Use the index to answer:
Is the current price trend supported by revenue growth, profit growth, estimate revisions, and the 20/50/100/200DMA system?
Treat results as research analysis, not investment advice. For latest/current scoring, verify data from current sources before calculating.
Required Inputs
Collect the newest available data before scoring:
- Price/technical data: latest price, 20DMA, 50DMA, 100DMA, 200DMA, ATR20, 5-day price change, and 20/50/100/200-day price changes or historical prices.
- Fundamental data: latest quarterly revenue, EPS, gross margin or gross profit, next-quarter company guidance, consensus revenue/EPS estimates, and 30-day estimate revisions.
- Preferred sources: company IR releases/presentations, earnings calls, Yahoo Finance historical prices/analysis, TradingView technicals/estimates, Barchart technical analysis, Seeking Alpha estimates, Koyfin, FactSet, Bloomberg, TIKR, or Visible Alpha.
For U.S.-listed companies, SEC filings can improve the fundamental side of the score. edgartools is an optional helper for retrieving the latest 10-K, 10-Q, 8-K, XBRL financial statements, filing text, insider transactions, and ownership filings.
If the environment does not already have it, install with pip install edgartools or uv pip install edgartools. The import package is edgar, not edgartools. SEC access requires an identity; set EDGAR_IDENTITY="Name [email protected]" in the environment or call from edgar import set_identity; set_identity("[email protected]") before requests.
Minimal usage pattern:
from edgar import Company
company = Company("AAPL")
financials = company.get_financials()
income = financials.income_statement()
filings = company.get_filings(form="8-K")
Use SEC data for:
- reported quarterly revenue, gross profit, EPS, cash flow, balance sheet, share count, and historical trend baselines
- management language on demand, backlog, pricing, capacity, inventory, customer concentration, and risks
- 8-K earnings releases or guidance disclosures when they contain the newest company-provided numbers
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.
- 13d ago First seen · 323 lines · 97 tokens per session scan A f2ea17494540
gf-dma-health-index is a skill published in the GitHub repository haskaomni/serenity-skill (632 stars, last pushed 1mo ago), licensed MIT. It adds 97 tokens to every session and 3,264 once invoked, about $0.0005 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.
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