Agent skill

Tam Adj Peg

by haskaomni in haskaomni/serenity-skill

Evaluate a stock's valuation using TAM-Adj-PEG, adjusting traditional PEG by growth runway and quality.

MITAuto-check passedBusiness, Finance & HR

Install Tam Adj Peg

skills CLI
$ npx skills add haskaomni/serenity-skill --skill tam-adj-peg -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install haskaomni/serenity-skill tam-adj-peg --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
tam-adj-peg
GitHub stars
633
Token cost
~2.7k tokens
SKILL.md length
1,245 words
Files
3 (incl. references)
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

Evaluate a stock's valuation using TAM-Adj-PEG, adjusting traditional PEG by growth runway and quality.

  • Works in 9 steps: Can TAM growth actually accrue to this… → Does the company have pricing power? → Is customer concentration high? → …
  • The user provides a ticker
  • SKILL.md covers Core Idea, Required Inputs, Core Formula and TAM Runway Factor, plus 7 more sections
  • Calls pip and uv

What it does

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.

When your agent uses it

  • The user provides a ticker
  • Asks whether a growth stocks valuation is cheap
  • Runway-supported
  • Quality-adjusted

Example prompts

  • “/tam-adj-peg”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Can TAM growth actually accrue to this company?
  2. Does the company have pricing power?
  3. Is customer concentration high?
  4. Does frequent technology iteration require repeated requalification?
  5. Are gross margin and EBIT margin sustainable?
  6. Does growth require heavy capex?
  7. Can competitors quickly catch up or become second/third sources?
  8. Does growth depend on financing or share issuance?
  9. Can frontier AI models erode the product's workflow value, pricing model, or distribution moat?

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:

    • pip
    • uv

    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

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.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.2k

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, ~2,734 tokens.

Download SKILL.mdSave it as .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.
name
tam-adj-peg
description
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.

TAM-Adj-PEG

Core Idea

Traditional PEG asks:

text
Is the current valuation expensive relative to future EPS growth?

TAM-Adj-PEG asks a broader question:

text
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.

Required Inputs

Collect the newest available data before scoring:

  • Valuation: current PE or TTM PE, forward PE, and traditional PEG if available.
  • Growth: expected 2-3 year EPS CAGR, revenue CAGR, TAM CAGR, and current revenue / TAM penetration.
  • Profit quality: gross margin, EBIT margin, free cash flow profile, capex intensity, and dilution risk.
  • Business quality: competitive position, pricing power, customer concentration, technology iteration risk, cyclicality, and key milestones.
  • Preferred sources: company IR releases/presentations, earnings calls, SEC filings, consensus estimate providers, industry TAM reports, and reputable financial data sources.

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:

python
from edgar import Company

company = Company("AAPL")
financials = company.get_financials()
income = financials.income_statement()
cashflow = financials.cashflow_statement()

Use SEC data to support:

  • revenue scale, segment mix, gross margin, EBIT margin, free cash flow, capex intensity, debt/cash, dilution, and share-count trends
  • customer concentration, pricing language, backlog/order commentary, supply constraints, technology risk, and cyclicality disclosures
  • 8-K earnings releases or guidance disclosures when they provide the newest company-reported inputs

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.

Core Formula

Calculate:

text
TAM-Adj-PEG = Forward PE / (EPS CAGR x TAM Runway Factor x Quality Factor)
Adjusted Growth = EPS CAGR x TAM Runway Factor x Quality Factor

Use 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.

TAM Runway Factor

Use TAM Runway Factor to correct for how long high growth can continue:

text
TAM Runway Factor = sqrt(Growth Duration / 5)

Rough scoring:

High-growth durationFactorInterpretation
2 years0.6Short-cycle growth
3 years0.75Growth exists, but runway is short
5 years1.0Standard growth stock
8 years1.25Long-runway compounder
10 years1.4High-quality long runway
15 years1.7Super long-cycle opportunity
20+ years2.0 capRare 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.

Quality Factor

Use Quality Factor to correct for whether growth can remain in the company's income statement:

Quality FactorCompany Type
0.3-0.5Early-stage, loss-making, unproven orders, or high dilution risk
0.5-0.7Cyclical, customer-concentrated, or high execution risk
0.7-0.9High growth but competitive, with unstable margins
0.9-1.1Normal high-quality growth company
1.1-1.3Strong moat, pricing power, and customer stickiness
1.3-1.5Monopoly-like, platform, or ecosystem asset
1.5+Rare super-platform or AI-era bottleneck asset; use cautiously

Evaluate nine questions:

  1. Can TAM growth actually accrue to this company?
  2. Does the company have pricing power?
  3. Is customer concentration high?
  4. Does frequent technology iteration require repeated requalification?
  5. Are gross margin and EBIT margin sustainable?
  6. Does growth require heavy capex?
  7. Can competitors quickly catch up or become second/third sources?
  8. Does growth depend on financing or share issuance?
  9. Can frontier AI models erode the product's workflow value, pricing model, or distribution moat?

Result Interpretation

TAM-Adj-PEGValuation View
< 0.5Very cheap, but verify forecasts are not overly optimistic
0.5-0.8Clearly attractive
0.8-1.2Reasonable to slightly cheap
1.2-1.8Reasonable to slightly expensive; execution must continue
1.8-2.5Expensive unless the company has a super-long runway
> 2.5Very expensive, or EPS/PE inputs are distorted
Not applicableLoss-making, early-stage, or earnings too volatile; use option framework
Show full SKILL.md (492 more words)Show less

Special Cases

Loss-Making Companies
  • Do not directly use PE.
  • Mark PE/PEG as distorted.
  • Use normalized EPS, EV/Sales, milestone scenarios, or option-style analysis.
  • Focus on key milestones and financing/dilution risk.
Cyclical Companies
  • Do not use peak-cycle EPS mechanically.
  • Use normalized EPS.
  • Discount Quality Factor for cyclicality.
  • TAM runway can add support, but supply/demand cycle risk must be deducted.
  • For AI semiconductor supercycles, separate structural demand runway from inventory, capacity, and margin-cycle risk. A semiconductor leader can receive a long TAM Runway Factor if it is a durable bottleneck; the cyclical penalty should mainly flow through normalized EPS and Quality Factor, not an automatic short-runway cap.
Turnarounds

Show two versions:

ScenarioTreatment
Base caseScore current earnings power
Turnaround successScore normalized profit 2-3 years out

This avoids misclassifying a turnaround as a normal growth stock.

Position-Type Framework

TypeTAM-PEG TraitsPosition Framing
Core compounderTAM-PEG 0.5-1.2 with high Quality FactorCandidate for long-term core exposure
High-beta growthTAM-PEG 0.8-1.5 with high growth and volatilityMedium exposure, track results closely
TurnaroundCurrent TAM-PEG high, success-case TAM-PEG lowerSmall to medium exposure, milestone-driven
Option-likePE/PEG distorted, large TAM, early executionSmall exposure, accept binary outcomes
CyclicalLow PEG but discounted Quality FactorTrade supply/demand cycle, avoid linear extrapolation

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:

  1. A flowchart showing how EPS CAGR, TAM Runway Factor, and Quality Factor produce adjusted growth and TAM-Adj-PEG.
  2. A 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.
  3. An 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:

  • Use fenced mermaid blocks, match the report language, keep node IDs in simple ASCII, and keep labels short.
  • Prefer broadly supported 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 only evidence and values already stated in the report. Keep percentages, factor scales, valuation units, and scenario labels 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 formulas, assumptions, risk adjustments, and source trails.

Output Format

Use this structure for every ticker:

markdown
# 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:

Detailed Reference

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

Files

SKILL.md and 2 other files (references) in skills/tam-adj-peg of haskaomni/serenity-skill.

  • SKILL.md
  • agents/openai.yaml
  • references/original-framework.md

Open the folder on GitHubat commit dedcf8f

Compare with similar skills

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Questions about Tam Adj Peg

What does Tam Adj Peg do?

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.

When should I use Tam Adj Peg?

Tam Adj Peg fits situations like: the user provides a ticker; asks whether a growth stocks valuation is cheap; runway-supported; quality-adjusted.

How do I install Tam Adj Peg in Claude Code?

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.

How do I install Tam Adj Peg in Codex?

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.

Can I use Tam Adj Peg 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 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.

What does Tam Adj Peg need to run?

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.

Does Tam Adj Peg 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 Tam Adj Peg 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 Tam Adj Peg use?

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.

How many tokens does Tam Adj Peg use?

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.

What are the alternatives to Tam Adj Peg?

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.

Who maintains Tam Adj Peg?

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.