Technical Analyst
tradermonty/claude-trading-skills
This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs.
Score a stock's current valuation/trend health using the GF-DMA Health Index, combining fundamental growth speed, 20/50/100/200DMA trend speed, price-to-DMA divergence, ATR divergence, escape ratio…
Install Gf Dma Health Index
$ npx skills add haskaomni/serenity-skill --skill gf-dma-health-index -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install haskaomni/serenity-skill gf-dma-health-index --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/haskaomni/serenity-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gf-dma-health-index .claude/skills/gf-dma-health-index && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "gf-dma-health-index" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/gf-dma-health-index into .claude/skills/gf-dma-health-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gf-dma-health-index", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/haskaomni/serenity-skill/tree/main/skills/gf-dma-health-indexType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add haskaomni/serenity-skill --skill gf-dma-health-index -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install haskaomni/serenity-skill gf-dma-health-index --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/haskaomni/serenity-skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/gf-dma-health-index .agents/skills/gf-dma-health-index && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gf-dma-health-index" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/gf-dma-health-index into .agents/skills/gf-dma-health-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gf-dma-health-index", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add haskaomni/serenity-skill --skill gf-dma-health-index -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install haskaomni/serenity-skill gf-dma-health-index --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/haskaomni/serenity-skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/gf-dma-health-index .cursor/skills/gf-dma-health-index && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gf-dma-health-index" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/gf-dma-health-index into .cursor/skills/gf-dma-health-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gf-dma-health-index", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/haskaomni/serenity-skill.git --path skills/gf-dma-health-index--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add haskaomni/serenity-skill --skill gf-dma-health-index -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install haskaomni/serenity-skill gf-dma-health-index --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/haskaomni/serenity-skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/gf-dma-health-index .gemini/skills/gf-dma-health-index && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gf-dma-health-index" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/gf-dma-health-index into .gemini/skills/gf-dma-health-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gf-dma-health-index", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install haskaomni/serenity-skill gf-dma-health-indexInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add haskaomni/serenity-skill --skill gf-dma-health-index -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/haskaomni/serenity-skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/gf-dma-health-index .github/skills/gf-dma-health-index && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gf-dma-health-index" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/gf-dma-health-index into .github/skills/gf-dma-health-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gf-dma-health-index", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add haskaomni/serenity-skill --skill gf-dma-health-index -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install haskaomni/serenity-skill gf-dma-health-index --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/haskaomni/serenity-skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/gf-dma-health-index .opencode/skills/gf-dma-health-index && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gf-dma-health-index" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/gf-dma-health-index into .opencode/skills/gf-dma-health-index/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gf-dma-health-index", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
gf-dma-health-index- GitHub stars
- 633
- Token cost
- ~3k tokens
- SKILL.md length
- 1,245 words
- Files
- 3 (incl. references)
- Skills in repo
- 6
- Repo updated
- First seen
- Licence
- MIT
At a glance
Score a stock's current valuation/trend health using the GF-DMA Health Index, combining fundamental growth speed, 20/50/100/200DMA trend speed, price-to-DMA divergence, ATR divergence, escape ratio…
- Works in 6 steps: Fundamental Speed → DMA Speed → Fundamental-DMA Match → …
- The user provides a ticker
- SKILL.md covers Core Idea, Required Inputs, Calculation Workflow and Divergence Module Scoring, plus 4 more sections
- Calls pip and uv
What it does
Gf Dma Health Index is an agent skill from haskaomni/serenity-skill. Score a stock's current valuation/trend health using the GF-DMA Health Index, combining fundamental growth speed, 20/50/100/200DMA trend speed, price-to-DMA divergence, ATR divergence, escape ratio, and estimate revisions. Use when the user provides a ticker or asks for GF-DMA scoring, valuation health, trend health, healthy momentum, overheated/escape risk, or whether a rising/falling stock is fundamentally supported.
Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `agents/openai.yaml` and `references/original-framework.md`).
It sits in Business, Finance & HR. The licence is MIT.
When your agent uses it
- The user provides a ticker
- Asks for GF-DMA scoring
- Valuation health
- Healthy momentum
Example prompts
- “/gf-dma-health-index”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit dedcf8f. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
pipuv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip and uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Gf Dma Health Index loads about 3k tokens when it runs, and up to ~6.5k if it reads all its reference files. Until then it costs about 111 tokens; SKILL.md has 1,245 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from haskaomni/serenity-skill at commit dedcf8f, republished under its MIT licence (© haskaomni). 1,245 words, ~2,972 tokens.
Download SKILL.mdSave it as
.claude/skills/gf-dma-health-index/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- gf-dma-health-index
- description
- Score a stock's current valuation/trend health using the GF-DMA Health Index, combining fundamental growth speed, 20/50/100/200DMA trend speed, price-to-DMA divergence, ATR divergence, escape ratio, and estimate revisions. Use when the user provides a ticker or asks for GF-DMA scoring, valuation health, trend health, healthy momentum, overheated/escape risk, or whether a rising/falling stock is fundamentally supported.
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@example.com" in the environment or call from edgar import set_identity; set_identity("name@example.com") 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
Do not use SEC data for the technical module or revision module. Price, 20/50/100/200DMA, ATR20, 5-day price slope, consensus estimates, and 30-day estimate revisions still require market-data and estimate sources. When SEC data is used, state the filing form and filing date so the user can judge freshness.
If a required field is unavailable, say which field is missing and use the simplified formula only when appropriate.
Calculation Workflow
1. Fundamental Speed
Calculate:
G_f = 0.35G_Revenue + 0.25G_GrossProfit + 0.30G_EPS + 0.10G_RevisionWhere:
G_Revenue = next-quarter revenue guidance / latest-quarter revenue - 1G_GrossProfit = next-quarter gross profit / latest-quarter gross profit - 1G_EPS = next-quarter EPS guidance / latest-quarter EPS - 1G_Revision = 30-day consensus estimate revision
Fallbacks:
- If gross profit or EPS is missing:
G_f = 0.5G_Revenue + 0.5G_EPS - If only revenue guidance is available:
G_f = G_Revenue
2. DMA Speed
Calculate quarterly annualized-equivalent moving-average speed for each DMA:
G_DMAx = ((SMA_x(t) - SMA_x(t-k)) / SMA_x(t-k)) * (63 / k)Use k = 5 or 10 trading days by default. If only price-change data is available, approximate:
DailySlope_x ~= (P_t - P_t-x) / x
G_DMAx ~= DailySlope_x * 63 / P_tCompute G_DMA20, G_DMA50, G_DMA100, and G_DMA200.
3. Fundamental-DMA Match
Calculate:
R_x = G_DMAx / G_fInterpret R_50 and R_100 first:
| R_x | Status |
|---|---|
| < 0.5 | Trend clearly below fundamental speed |
| 0.5-0.8 | Under-reflected or cheap versus trend |
| 0.8-1.3 | Healthy match |
| 1.3-2.0 | Hot but potentially explainable |
| > 2.0 | Overheated / FOMO escape risk |
Core DMA emphasis:
| Stock type | Key DMA |
|---|---|
| Mega-cap growth leaders like NVDA, AVGO, MSFT | 50DMA |
| Memory/cyclical semis like MU, SNDK | 100DMA |
| High-elasticity optical names like LITE, AAOI | 20DMA + 50DMA |
| Industrial AI/power names like ETN, VRT, TEL | 100DMA + 200DMA |
| Small-cap hard-manufacturing names like SIVE, CPSH | 20DMA + ATR divergence |
| Semiconductor ETFs like SOXX, SMH | 50DMA + 100DMA |
4. Price-DMA Divergence
Calculate:
D_x = P_t / SMA_x(t) - 1
Z_x = (P_t - SMA_x) / ATR20Interpretation:
| Signal | Status |
|---|---|
| 0%-5% above 20DMA | Healthy close-to-line trend |
| 5%-12% above 20DMA | Strong trend, mild valuation stretch |
| 12%-20% above 20DMA | Hot; divergence score should fall |
| >20% above 20DMA | Short-term escape; divergence score should fall sharply |
| >30% above 50DMA | Medium-term overheat |
| >50% above 100DMA | Major repricing |
| >100% above 200DMA | Extreme long-cycle repricing |
| 0%-5% below 20/50DMA with stable fundamentals | Healthy pullback; divergence score can rise |
| 5%-15% below 50DMA with stable/improving fundamentals | Better valuation entry, but verify trend damage separately |
| Below 100/200DMA with deteriorating fundamentals | Trend damage; do not treat as cheap automatically |
ATR divergence is asymmetric:
| Z_x | Status |
|---|---|
| 0 to 2 | Healthy |
| 2 to 3 | Hot |
| 3 to 4 | Very hot |
| >4 | Escape; reduce divergence score sharply |
| -1 to 0 with stable fundamentals | Mild pullback; can improve valuation-health score |
| -3 to -1 with stable/improving fundamentals | Discounted pullback; score can be high, but check trend parallelism |
| < -3 or below key long DMA with estimate cuts | Possible breakdown; score should fall |
Important: S_Divergence is a valuation-health score, not a pure momentum score. Upward price-DMA divergence lowers the score because the stock is more stretched. Downward divergence raises the score only when fundamental speed and revision confirmation are stable or improving; if fundamentals are deteriorating, downward divergence is trend damage rather than an opportunity.
5. Trend Parallelism / Escape Ratio
Calculate:
EscapeRatio = 5-day price slope / 50DMA daily slopeInterpretation:
| EscapeRatio | Status |
|---|---|
| 0.8-1.2 | Price and 50DMA are parallel; healthy |
| 1.2-1.8 | Short-term acceleration; acceptable |
| 1.8-2.5 | Clearly hot |
| >2.5 | FOMO escape |
| 0-0.5 | Momentum decay |
| <0 | Short-term reversal; trend damage |
Show full SKILL.md (509 more words)Show less
6. Revision Confirmation
Score estimate revisions:
| Revision state | Score |
|---|---|
| Revenue and EPS estimates rising; company guide above consensus | 85-100 |
| Mild upward revisions; guide slightly above consensus | 70-85 |
| Stable expectations; limited upward revision | 55-70 |
| Revisions starting to fall | 35-55 |
| Guide below consensus; analysts cutting estimates | <35 |
Divergence Module Scoring
Use asymmetric scoring for S_Divergence:
| State | Score |
|---|---|
| Price close to 20/50DMA, above 100/200DMA | 80-95 |
| Stable/improving fundamentals; price below 20DMA but near 50DMA | 85-100 |
| Stable/improving fundamentals; price 5%-15% below 50DMA while long DMAs remain healthy | 75-95 |
| Price 5%-12% above 20DMA | 65-80 |
| Price 12%-20% above 20DMA | 50-70 |
| Price >20% above 20DMA or >30% above 50DMA | 25-55 |
| Price below 50DMA with weakening fundamentals or estimate cuts | 35-60 |
| Price below 100DMA with estimate cuts | 15-45 |
| Price below 200DMA with fundamental deterioration | 0-30 |
When price is below key DMAs, explicitly state whether the lower price is a healthy pullback or a breakdown. The deciding gate is fundamental speed plus revision confirmation.
Final Scoring
Calculate total score out of 100:
HealthScore = 40S_GrowthMatch + 25S_Divergence + 20S_Parallel + 15S_RevisionModule scoring:
| Module | Weight |
|---|---|
| Fundamental speed match | 40% |
| Price-DMA divergence / pullback opportunity | 25% |
| Trend parallelism | 20% |
| Revision confirmation | 15% |
Final interpretation:
| Score | State | Meaning |
|---|---|---|
| 85-100 | Healthy Momentum | Healthy main uptrend |
| 75-85 | Strong but Watch | Strong trend; continue monitoring |
| 65-75 | Hot but Supported | Hot, but fundamentals can still support it |
| 55-65 | Damaged / Overheated | Trend damage or local overheat |
| 40-55 | High Risk | Risk clearly rising |
| <40 | Broken / Escaping | Broken trend or post-escape pullback |
Mermaid Visualizations
For a full report, include 2-4 Mermaid diagrams when they materially improve comprehension. A short answer or data-limited analysis may use fewer. Do not create a diagram merely to meet a quota.
Prioritize these views:
- An
xychart-betacomparing latest price with 20/50/100/200DMA values, with the source values preserved in the adjacent table. - An
xychart-betaof the four 0-100 module scores, clearly separated from their percentage weights. - A compact
flowchartexplaining why a below-DMA state is a healthy pullback or a breakdown, using fundamental speed and revision confirmation as the gate.
Apply these rules to every diagram:
- Use fenced
mermaidblocks, match the report language, keep node IDs in simple ASCII, and keep labels short. - Prefer broadly supported
flowchart,pie, andstateDiagramsyntax. Usexychart-beta,quadrantChart, ortimelineonly as progressive enhancement and retain the adjacent Markdown table as the fallback. - Use only evidence and values already stated in the report. Keep price currency, periods, scores, weights, and units consistent with the surrounding tables; never fill missing data for visual completeness.
- Place each diagram beside the analysis it explains and follow it with a one-sentence takeaway. Keep citations, URLs, dates, and detailed caveats outside the diagram.
- Keep a diagram focused: normally no more than 12 nodes or 8 plotted values. Diagrams supplement rather than replace calculations, score tables, caveats, and source trails.
Output Format
Use this structure for every ticker:
# TICKER: GF-DMA Health Index 评分
最终评分：XX / 100
状态：Healthy Momentum / Strong but Watch / Hot but Supported / Damaged / High Risk / Broken
一句话判断：
...
1. 基本面速度
- 最新季度营收：
- 下一季度营收指引：
- 营收 QoQ：
- EPS QoQ：
- 毛利润 QoQ：
- Fundamental Speed：
2. 均线速度匹配
| 均线 | 季度化斜率 | 相对基本面速度 | 判断 |
|---|---:|---:|---|
| 20DMA | | | |
| 50DMA | | | |
| 100DMA | | | |
| 200DMA | | | |
若价格和四条均线数据完整，在表后加入 Mermaid xychart；表格继续作为数值和兼容性回退。
3. 股价-均线背离
| 指标 | 当前背离 | 判断 |
|---|---:|---|
| P / 20DMA - 1 | | |
| P / 50DMA - 1 | | |
| P / 100DMA - 1 | | |
| P / 200DMA - 1 | | |
4. 趋势平行度
- Escape Ratio：
- 判断：
5. 预期上修确认
- 公司指引 vs 市场预期：
- 过去 30 天预期变化：
- 判断：
6. 综合评分
| 模块 | 权重 | 分数 |
|---|---:|---:|
| 基本面速度匹配 | 40% | |
| 股价-均线背离 | 25% | |
| 趋势平行度 | 20% | |
| 预期上修确认 | 15% | |
在表后加入四个模块分数的 Mermaid xychart，不要把模块分数与模块权重混画在同一坐标轴。
结论：
...
当价格位于关键均线下方时，可加入 Mermaid flowchart，展示健康回撤与趋势破坏的判断门槛。Detailed Reference
Read references/original-framework.md when a task needs the full Chinese framework text, examples, source priority list, or scoring tables in their original form.
When the reference format differs, preserve its analytical intent but follow this SKILL.md's current output and visualization rules.
© haskaomni, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files (references) in skills/gf-dma-health-index of haskaomni/serenity-skill.
- SKILL.md
- agents/openai.yaml
- references/original-framework.md
Open the folder on GitHubat commit dedcf8f
Compare with similar skills
Gf Dma Health Index next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
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| Gf Dma Health Index this skillhaskaomni/serenity-skill | 633 | — | ~3k | Automated safety check: Pass | MIT | |
| Technical Analysttradermonty/claude-trading-skills | 3k | 4 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Theme Detectortradermonty/claude-trading-skills | 3k | 2 repos | ~4.9k | Automated safety check: Pass | MIT | |
| Creating Financial ModelsChen-zexi/open-ptc-agent | 729 | 3 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Stock APIzhangxiangliang/stock-api | 2k | — | ~507 | Automated safety check: Pass | MIT | |
| Itr Walakaranb192/itr-wala | 871 | — | ~3.6k | Automated safety check: Pass | MIT |
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haskaomni/serenity-skill
Use a Bayesian intrinsic-growth valuation model to evaluate whether a company's market value sufficiently, excessively, or insufficiently prices its real 3-5 year growth.
Buy Side Equity Research Memo
haskaomni/serenity-skill
Generate source-backed buy-side equity research memos from a ticker, starting with investment view, target-price scenarios, SEC and IR-backed financial statement analysis, industry chain…
Juglar Cycle Stock Stage
haskaomni/serenity-skill
Classify a listed company and its core industry into Juglar fixed-asset investment cycle stages with probabilities, evidence, counter-evidence, migration signals, and investment implications.
Serenity Alpha
haskaomni/serenity-skill
Translate market-moving news into investable alpha hypotheses by mapping observed demand changes to revenue lines, supply chains, small-cap financial elasticity, market misclassification, validation…
Tam Adj Peg
haskaomni/serenity-skill
Evaluate a stock's valuation using TAM-Adj-PEG, adjusting traditional PEG by growth runway and quality.
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Questions about Gf Dma Health Index
What does Gf Dma Health Index do?
Score a stock's current valuation/trend health using the GF-DMA Health Index, combining fundamental growth speed, 20/50/100/200DMA trend speed, price-to-DMA divergence, ATR divergence, escape ratio…. Gf Dma Health Index is an agent skill from haskaomni/serenity-skill. Score a stock's current valuation/trend health using the GF-DMA Health Index, combining fundamental growth speed, 20/50/100/200DMA trend speed, price-to-DMA divergence, ATR divergence, escape ratio, and estimate revisions.
When should I use Gf Dma Health Index?
Gf Dma Health Index fits situations like: the user provides a ticker; asks for GF-DMA scoring; valuation health; healthy momentum.
How do I install Gf Dma Health Index in Claude Code?
Run `npx skills add haskaomni/serenity-skill --skill gf-dma-health-index -a claude-code`. Or copy the skill folder (skills/gf-dma-health-index in haskaomni/serenity-skill) into .claude/skills/gf-dma-health-index in your project. Claude Code loads it when a task matches its description.
How do I install Gf Dma Health Index in Codex?
Run `npx skills add haskaomni/serenity-skill --skill gf-dma-health-index -a codex`. Or copy the skill folder (skills/gf-dma-health-index in haskaomni/serenity-skill) into .agents/skills/gf-dma-health-index in your project. Codex loads it when a task matches its description.
Can I use Gf Dma Health Index in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add haskaomni/serenity-skill --skill gf-dma-health-index -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gf-dma-health-index, .gemini/skills/gf-dma-health-index, .github/skills/gf-dma-health-index and .opencode/skills/gf-dma-health-index in your project.
What does Gf Dma Health Index need to run?
Going by SKILL.md and its folder, Gf Dma Health Index needs the command-line tools its instructions call (pip and uv). Our summary lists: Python 3.
Does Gf Dma Health Index access the network?
SKILL.md contains no URLs. Its commands use pip and uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Gf Dma Health Index safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Gf Dma Health Index use?
Gf Dma Health Index is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Gf Dma Health Index use?
About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.5k tokens, read only when the agent opens those files.
What are the alternatives to Gf Dma Health Index?
Skills that share tags, products or a category with Gf Dma Health Index: Technical Analyst (tradermonty/claude-trading-skills, 3k stars), Theme Detector (tradermonty/claude-trading-skills, 3k stars), Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars) and Stock API (zhangxiangliang/stock-api, 2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Gf Dma Health Index?
haskaomni (a GitHub user) maintains it in haskaomni/serenity-skill, which has 633 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on July 15, 2026.
Source: haskaomni/serenity-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.