Technical Analyst
tradermonty/claude-trading-skills
This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs.
Evaluate a stock's valuation using TAM-Adj-PEG, adjusting traditional PEG by growth runway and quality.
$ npx skills add haskaomni/serenity-skill --skill tam-adj-peg -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install haskaomni/serenity-skill tam-adj-peg --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/tam-adj-peg .claude/skills/tam-adj-peg && 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 "tam-adj-peg" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/tam-adj-peg into .claude/skills/tam-adj-peg/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tam-adj-peg", 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/tam-adj-pegType 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 tam-adj-peg -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install haskaomni/serenity-skill tam-adj-peg --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/tam-adj-peg .agents/skills/tam-adj-peg && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tam-adj-peg" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/tam-adj-peg into .agents/skills/tam-adj-peg/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tam-adj-peg", 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 tam-adj-peg -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install haskaomni/serenity-skill tam-adj-peg --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/tam-adj-peg .cursor/skills/tam-adj-peg && 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 "tam-adj-peg" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/tam-adj-peg into .cursor/skills/tam-adj-peg/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tam-adj-peg", 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/tam-adj-peg--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 tam-adj-peg -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install haskaomni/serenity-skill tam-adj-peg --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/tam-adj-peg .gemini/skills/tam-adj-peg && 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 "tam-adj-peg" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/tam-adj-peg into .gemini/skills/tam-adj-peg/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tam-adj-peg", 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 tam-adj-pegInstalls 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 tam-adj-peg -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/tam-adj-peg .github/skills/tam-adj-peg && 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 "tam-adj-peg" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/tam-adj-peg into .github/skills/tam-adj-peg/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tam-adj-peg", 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 tam-adj-peg -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 tam-adj-peg --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/tam-adj-peg .opencode/skills/tam-adj-peg && 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 "tam-adj-peg" agent skill from https://github.com/haskaomni/serenity-skill/tree/main/skills/tam-adj-peg into .opencode/skills/tam-adj-peg/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tam-adj-peg", 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.
tam-adj-pegEvaluate a stock's valuation using TAM-Adj-PEG, adjusting traditional PEG by growth runway and quality.
Tam Adj Peg is an agent skill from haskaomni/serenity-skill. Evaluate a stock's valuation using TAM-Adj-PEG, adjusting traditional PEG by growth runway and quality. Use when the user provides a ticker or asks whether a growth stock's valuation is cheap, expensive, TAM-supported, runway-supported, quality-adjusted, or suitable as core growth, high-beta growth, turnaround, option-like, or cyclical exposure.
Its SKILL.md is about 2.7k 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.
9 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit dedcf8f. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
pipuvFrom the folder's file list and the shell code blocks in SKILL.md.
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.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Tam Adj Peg loads about 2.7k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 90 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.
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.
The full file from haskaomni/serenity-skill at commit dedcf8f, republished under its MIT licence (© haskaomni). 1,245 words, ~2,734 tokens.
.claude/skills/tam-adj-peg/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Traditional PEG asks:
Is the current valuation expensive relative to future EPS growth?TAM-Adj-PEG asks a broader question:
How long can this growth last, is the TAM large enough, and can the company convert TAM growth into durable profits?Use this framework for AI infrastructure, semiconductors, healthcare, SaaS, payment networks, high-growth manufacturers, bottleneck suppliers, early turnarounds, and option-like equities.
Treat results as research analysis, not investment advice. For latest/current scoring, verify valuation, estimates, TAM, margins, and company-specific data from current sources before calculating.
Collect the newest available data before scoring:
For U.S.-listed companies, use SEC filings as the baseline for reported fundamentals. edgartools is an optional helper for retrieving 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()
cashflow = financials.cashflow_statement()Use SEC data to support:
Do not rely on SEC data alone for forward PE, EPS CAGR, consensus revisions, current valuation, TAM size, TAM CAGR, or competitive market-share estimates. Keep reported SEC facts separate from forecasts and industry assumptions, and cite the filing form/date when using SEC evidence.
If PE or EPS CAGR is not meaningful because the company is loss-making or earnings are highly volatile, mark PE/PEG as distorted and use normalized earnings, EV/Sales, milestone scenarios, or an option-style framework.
Calculate:
TAM-Adj-PEG = Forward PE / (EPS CAGR x TAM Runway Factor x Quality Factor)
Adjusted Growth = EPS CAGR x TAM Runway Factor x Quality FactorUse EPS CAGR as a percentage number in the denominator. Example: if forward PE is 40 and adjusted growth is 50%, then TAM-Adj-PEG = 40 / 50 = 0.8.
Do not directly add TAM CAGR to EPS CAGR. EPS CAGR usually already reflects part of TAM expansion; TAM should mainly adjust growth duration and certainty.
Use TAM Runway Factor to correct for how long high growth can continue:
TAM Runway Factor = sqrt(Growth Duration / 5)Rough scoring:
| High-growth duration | Factor | Interpretation |
|---|---|---|
| 2 years | 0.6 | Short-cycle growth |
| 3 years | 0.75 | Growth exists, but runway is short |
| 5 years | 1.0 | Standard growth stock |
| 8 years | 1.25 | Long-runway compounder |
| 10 years | 1.4 | High-quality long runway |
| 15 years | 1.7 | Super long-cycle opportunity |
| 20+ years | 2.0 cap | Rare supercycle, platform, or monopoly-like asset |
Do not assign runway by sector label alone. AI-driven semiconductor bottlenecks can deserve a 15-20+ year runway when demand is structurally expanding, the company controls a hard-to-replicate choke point, and each technology generation reinforces its position. Conversely, SaaS or platform companies should not automatically receive a long runway if frontier AI models can compress workflow value, commoditize features, weaken seat-based pricing, or shift the profit pool to model/infrastructure providers.
Use Quality Factor to correct for whether growth can remain in the company's income statement:
| Quality Factor | Company Type |
|---|---|
| 0.3-0.5 | Early-stage, loss-making, unproven orders, or high dilution risk |
| 0.5-0.7 | Cyclical, customer-concentrated, or high execution risk |
| 0.7-0.9 | High growth but competitive, with unstable margins |
| 0.9-1.1 | Normal high-quality growth company |
| 1.1-1.3 | Strong moat, pricing power, and customer stickiness |
| 1.3-1.5 | Monopoly-like, platform, or ecosystem asset |
| 1.5+ | Rare super-platform or AI-era bottleneck asset; use cautiously |
Evaluate nine questions:
| TAM-Adj-PEG | Valuation View |
|---|---|
| < 0.5 | Very cheap, but verify forecasts are not overly optimistic |
| 0.5-0.8 | Clearly attractive |
| 0.8-1.2 | Reasonable to slightly cheap |
| 1.2-1.8 | Reasonable to slightly expensive; execution must continue |
| 1.8-2.5 | Expensive unless the company has a super-long runway |
| > 2.5 | Very expensive, or EPS/PE inputs are distorted |
| Not applicable | Loss-making, early-stage, or earnings too volatile; use option framework |
Show two versions:
| Scenario | Treatment |
|---|---|
| Base case | Score current earnings power |
| Turnaround success | Score normalized profit 2-3 years out |
This avoids misclassifying a turnaround as a normal growth stock.
| Type | TAM-PEG Traits | Position Framing |
|---|---|---|
| Core compounder | TAM-PEG 0.5-1.2 with high Quality Factor | Candidate for long-term core exposure |
| High-beta growth | TAM-PEG 0.8-1.5 with high growth and volatility | Medium exposure, track results closely |
| Turnaround | Current TAM-PEG high, success-case TAM-PEG lower | Small to medium exposure, milestone-driven |
| Option-like | PE/PEG distorted, large TAM, early execution | Small exposure, accept binary outcomes |
| Cyclical | Low PEG but discounted Quality Factor | Trade supply/demand cycle, avoid linear extrapolation |
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:
flowchart showing how EPS CAGR, TAM Runway Factor, and Quality Factor produce adjusted growth and TAM-Adj-PEG.quadrantChart comparing runway and quality for the company and peers only when at least three entities use comparable evidence and scales; retain the comparison table.xychart-beta comparing raw EPS growth with adjusted growth, or base and turnaround-success valuation outputs, only when the values share a clear unit.Apply these rules to every diagram:
mermaid blocks, match the report language, keep node IDs in simple ASCII, and keep labels short.flowchart, pie, and stateDiagram syntax. Use xychart-beta, quadrantChart, or timeline only as progressive enhancement and retain the adjacent Markdown table as the fallback.Use this structure for every ticker:
# TICKER: TAM-Adj-PEG 估值分析
公司:XXX
股票代码:XXX
1. 当前估值
- 当前 PE:
- Forward PE:
- 传统 PEG:
2. 增长拆解
- 未来 EPS CAGR:
- Revenue CAGR:
- TAM CAGR:
- 当前收入 / TAM:
- 高速增长 runway:
3. TAM Runway Factor
- 估计值:
- 原因:
4. Quality Factor
- 估计值:
- 加分项:
- 扣分项:
5. TAM-Adj-PEG
公式:
TAM-Adj-PEG = Forward PE / (EPS CAGR x TAM Runway Factor x Quality Factor)
计算:
- 修正后增长率:
- TAM-Adj-PEG:
加入 Mermaid flowchart 展示三个输入如何传导到修正后增长率和最终估值;保留完整公式与计算。
如有可比公司或双情景的同口径数据,可加入 runway—quality 矩阵或增长对比 xychart,并保留数据表。
6. 结论
- 估值档位:
- 主要上行驱动:
- 主要下行风险:
- 适合的仓位类型:Read references/original-framework.md when a task needs the full Chinese framework text, all scoring tables, or the original explanation of TAM-Adj-PEG logic.
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
SKILL.md and 2 other files (references) in skills/tam-adj-peg of haskaomni/serenity-skill.
Open the folder on GitHubat commit dedcf8f
Tam Adj Peg 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Tam Adj Peg this skillhaskaomni/serenity-skill | 633 | — | ~2.7k | 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 |
tradermonty/claude-trading-skills
This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs.
tradermonty/claude-trading-skills
Detect and analyze trending market themes across sectors. An agent skill from tradermonty/claude-trading-skills.
Chen-zexi/open-ptc-agent
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
zhangxiangliang/stock-api
Fetch real-time stock quotes, K-line (candlestick) history, and search symbols for China A-shares, Hong Kong, and US markets.
karanb192/itr-wala
File Indian income tax returns (ITR) for FY 2025-26 / AY 2026-27.
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求,转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
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.
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…
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…
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.
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…
Categories
Evaluate a stock's valuation using TAM-Adj-PEG, adjusting traditional PEG by growth runway and quality. Tam Adj Peg is an agent skill from haskaomni/serenity-skill. Evaluate a stock's valuation using TAM-Adj-PEG, adjusting traditional PEG by growth runway and quality.
Tam Adj Peg fits situations like: the user provides a ticker; asks whether a growth stocks valuation is cheap; runway-supported; quality-adjusted.
Run `npx skills add haskaomni/serenity-skill --skill tam-adj-peg -a claude-code`. Or copy the skill folder (skills/tam-adj-peg in haskaomni/serenity-skill) into .claude/skills/tam-adj-peg in your project. Claude Code loads it when a task matches its description.
Run `npx skills add haskaomni/serenity-skill --skill tam-adj-peg -a codex`. Or copy the skill folder (skills/tam-adj-peg in haskaomni/serenity-skill) into .agents/skills/tam-adj-peg in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add haskaomni/serenity-skill --skill tam-adj-peg -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tam-adj-peg, .gemini/skills/tam-adj-peg, .github/skills/tam-adj-peg and .opencode/skills/tam-adj-peg in your project.
Going by SKILL.md and its folder, Tam Adj Peg needs the command-line tools its instructions call (pip and uv). Our summary lists: Python 3.
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.
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.
Tam Adj Peg is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.7k tokens (SKILL.md is roughly 11k 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 2.5k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Tam Adj Peg: 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.
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.