PEG Valuation

SkillDev tools

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

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the PEG Valuation skill

What this skill tells your AI

The instructions your AI receives, as published by agentii-ai/agentii-investment-intelligence in plugins/vertical-plugins/quantitative-analysis/skills/agentii/peg-valuation/SKILL.md and read by ahel’s review.

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 the canonical pre-flight sequence — MCP health probe, ticker resolution, workspace style.md override, memory load, and coverage check. See 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.

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."

Signals

GitHub stars
204
Forks
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Last commit
Sep 2026
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skill
Gateway key
peg-valuation
Source
github.com/agentii-ai/agentii-investment-intelligence