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 tam-adj-peggit 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/tam-adj-peg)<a href="https://agentmods.dev/skills/haskaomni/serenity-skill/tam-adj-peg"><img src="https://agentmods.dev/badge/skills/haskaomni/serenity-skill/tam-adj-peg/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/tam-adj-peg"><img src="https://agentmods.dev/badge/skills/haskaomni/serenity-skill/tam-adj-peg.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 | $0.00077 | $0.02597 |
| Opus 5 | $0.00039 | $0.01299 |
| Sonnet 5 | $0.00015 | $0.00519 |
| Haiku 4.5 | $0.00008 | $0.00260 |
Grade A, and why
tam-adj-peg 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 12d 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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TAM-Adj-PEG
Core Idea
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.
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 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()
cashflow = financials.cashflow_statement()
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.
- 12d ago First seen · 245 lines · 77 tokens per session scan A bcf7e94f9b60
tam-adj-peg is a skill published in the GitHub repository haskaomni/serenity-skill (632 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 2,597 once invoked, about $0.0004 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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