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Peg Valuation

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agentii-ai
peg-valuation

PEG valuation, Price Earnings to Growth ratio, Peter Lynch PEG methodology, growth-adjusted valuation, earnings growth rate, PE ratio valuation, PEG sector comparison, undervalued growth stocks, fair value PEG

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill namepeg-valuation
Stars
207
Forks
16
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by agentii-ai on GitHub. Read the source before you install it.

Installation

Install the Peg Valuation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/quantitative-analysis/skills/agentii/peg-valuation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git /tmp/agentii-investment-intelligence
mkdir -p .claude/skills
cp -r /tmp/agentii-investment-intelligence/plugins/vertical-plugins/quantitative-analysis/skills/agentii/peg-valuation .claude/skills/peg-valuation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Peg Valuation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Peg Valuation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Peg Valuation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PEG Valuation

Peter Lynch PEG (Price/Earnings to Growth) methodology. PEG = P/E Ratio ÷ Earnings Growth Rate (%). Growth-adjusted valuation that answers: "Is this stock's growth justifying its multiple?"

Preflight

Run canonical pre-flight per contracts/preflight.md.

**get_realtime_quote availability **: If get_realtime_quote is not yet deployed, prompt user for current stock price. PE numerator from search_earnings_calendar (NTM consensus EPS × current price = PE) as fallback.

Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md — carry the _run_id from your first tool result and name yourself (and your parent, if you were spawned).

Triggers

  • PEG valuation for {ticker}
  • compute PEG ratio {ticker}
  • Peter Lynch PEG {ticker}
  • growth-adjusted valuation {ticker}
  • is {ticker} undervalued by PEG
  • PEG analysis {ticker}
  • price earnings growth {ticker}
  • compare PEG across peers
  • {ticker} PEG vs sector
  • growth at reasonable price {ticker}

Defaults

ParameterDefaultNotes
growth_sourceconsensusconsensus estimates preferred; fallback to historical CAGR
include_peerstrueSector PEG comparison
lookback_years3Historical CAGR computation window

Methodology

Retrieval Scope

structured_only — PEG uses XBRL earnings data + real-time price + earnings calendar for growth estimates.

Retrieval Strategy

See contracts/retrieval.md for the canonical decision tree; skill-specific retrieval detail is in references/methodology.md.

Temporal Scope

Default: 4 fiscal quarters (max 12). PEG uses trailing 4 quarters for LTM P/E; up to 12 for historical EPS CAGR if consensus unavailable.

Tool Allowlist

See frontmatter allowed_tools — 4 tools. get_realtime_quote for current price + PE (TTM). search_earnings_calendar for consensus EPS and long-term growth estimates. search_xbrl_facts for historical EPS to compute CAGR. search_companies for peer identification.

Protocol

Step-by-step execution detail is in references/methodology.md.

PEG Interpretation (Peter Lynch Framework)

PEG RangeRatingInvestment Implication
< 0.5Deeply UndervaluedGrowth vastly exceeds valuation; investigate for hidden risks
0.5 – 1.0UndervaluedClassic Lynch buy zone; growth justifies the multiple
1.0 – 1.5Fairly ValuedGrowth and valuation in equilibrium
1.5 – 2.0PremiumMarket paying up for growth; needs above-consensus execution
> 2.0OvervaluedGrowth insufficient to justify current multiple
NegativeN/ANegative earnings — PEG not meaningful; use revenue-based metrics

Output File

Write the final deliverable to {ticker}/{YYYY-MM-DD_HHMM}_peg-valuation_{affix}.md.

Output Structure

The deliverable is a structured markdown report written to the path in ## Output File. Full section-by-section template (headings, tables, and field definitions) lives in references/output-structure.md. Required elements:

  1. Executive Summary — headline conclusions (≤200 words).
  2. Core analysis sections — per this skill's methodology and analyst modes.
  3. Data classification — tag findings [FACT] / [DEDUCTED] / [VIEW] per contracts/snapshot-synthesis.md.
  4. Coverage Gaps & Citations — inline /v/ citations are PRIMARY (immediately after each fact); the bottom Citations section is a non-duplicative roll-up index.
  5. Output frontmatter — emit the FR-090 structured block per contracts/output-frontmatter-schema.md.

Citations & memory: follow contracts/citation-and-memory.md — ≥1 citation per 200 words; every material fact, table row, and metric is immediately followed by its inline clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link; a bottom Citations section provides a non-duplicative roll-up index; the closing TUI reply includes a compact Key Citations list (headline 5–10 facts) of clickable /v/ URLs; and append the run to agentii.md per contracts/agentii-md-schema.md.

Tool Fallbacks

Per-tool failure modes and fallback actions are tabulated in references/tool-fallbacks.md.

Memory & Snapshot

  • Memory load (pre-flight): load prior workspace context for the ticker before retrieval — see contracts/memory-load.md.
  • Structured output frontmatter: emit the FR-090 block (key_metrics, conclusions, facts_count, deducted_count, views_count, citation_count) per contracts/output-frontmatter-schema.md.
  • Snapshot synthesis: after writing the deliverable, update the two-tier snapshot and classify findings as [FACT]/[DEDUCTED]/[VIEW] — see contracts/snapshot-synthesis.md.
  • Session archival: record the run under sessions/{YYYY-MM-DD}/ and update sessions/INDEX.md per contracts/session-format.md.

Final Summary (TUI)

End the closing chat reply with a compact Key Citations list (headline 5–10 facts), each a clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link, so the user can cmd+click straight to the exact SEC page. See contracts/citation-and-memory.md.

Error Handling

Failure ModeDetectionActionUser-Facing Message
Missing dataNo consensus or historical EPSHalt; cannot compute growth rate"Insufficient earnings data to compute growth rate for {ticker}."
Negative earningsPE (TTM) < 0Compute only revenue-based metrics; flag PEG as N/A"PEG not applicable — {ticker} has negative earnings."
Zero growthCAGR ≈ 0%PEG = ∞; flag as "no growth" case"Zero historical EPS growth — PEG effectively infinite."
MCP unreachablePreflight probe failsHalt"agentii data plane unreachable; check connection."

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Peg Valuation AI skill do?

PEG valuation, Price Earnings to Growth ratio, Peter Lynch PEG methodology, growth-adjusted valuation, earnings growth rate, PE ratio valuation, PEG sector comparison, undervalued growth stocks, fair value PEG

Why use Peg Valuation on TypingMind?

Because you install it once and use it with any model. Peg Valuation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Peg Valuation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/quantitative-analysis/skills/agentii/peg-valuation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Peg Valuation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Peg Valuation?

As many as you like. As long as a model supports skills, you can use Peg Valuation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Peg Valuation AI skill free?

Yes. It is published on GitHub by agentii-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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