stock-peer-comparison-review

stock-peer-comparison-review is a skill for Claude Code from johnqtcg/awesome-skills. It costs 127 tokens per session (3,114 once invoked), scanned A, original, MIT.

A stock-comparison research skill for comparing a US-listed company with two to four similar companies across 12 financial measures. These measures cover growth, profits, debt, capital use, shareholder returns, and valuation.

In plain words
What is it for?
Use it to compare a stock with peers when assessing valuation, competitive strength, profitability, leverage, growth, or market-share claims.
Why use it?
It provides a consistent peer comparison instead of judging a company from its own claims or from a hand-picked competitor.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to compare a stock with peers when assessing valuation, competitive strength, profitability, leverage, growth, or market-share claims.

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Install with agentmods
npx agentmods add skills/johnqtcg/awesome-skills/stock-peer-comparison-review
Install

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.

Any agent
npx skills add johnqtcg/awesome-skills --skill stock-peer-comparison-review
Clone the repo
git clone --depth 1 https://github.com/johnqtcg/awesome-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for stock-peer-comparison-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/johnqtcg/awesome-skills/stock-peer-comparison-review/github.svg)](https://agentmods.dev/skills/johnqtcg/awesome-skills/stock-peer-comparison-review)
Your own site
<a href="https://agentmods.dev/skills/johnqtcg/awesome-skills/stock-peer-comparison-review"><img src="https://agentmods.dev/badge/skills/johnqtcg/awesome-skills/stock-peer-comparison-review/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.

agentmods 80×15 button for stock-peer-comparison-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/johnqtcg/awesome-skills/stock-peer-comparison-review"><img src="https://agentmods.dev/badge/skills/johnqtcg/awesome-skills/stock-peer-comparison-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 127 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,114 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00127 $0.03114
Opus 5 $0.00063 $0.01557
Sonnet 5 $0.00025 $0.00623
Haiku 4.5 $0.00013 $0.00311

Measured 13d ago against content hash 11002fc2e03e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

stock-peer-comparison-review 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 13d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/run_regression.sh, scripts/tests/test_skill_contract.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/stock-peer-comparison-review/SKILL.md · 243 lines

How it starts

The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Stock Peer Comparison Review

Purpose

Most single-stock analyses fall into one of two failure modes:

  1. Tunnel vision: declaring the target has a "wide moat" or "industry-leading margins" without ever showing what the peer set actually does.
  2. Peer-set selection bias: comparing only against weaker peers to flatter the target.

This worker fixes both. It loads a fixed 12-item ratio panel, computes the target and each peer on the same definition, and surfaces the rank position. The Industry and Business workers can claim "Azure is gaining share" — this worker reports the ratios that either confirm or contradict the claim independent of narrative.

The output is a small, dense, rank-ordered comparison table. It is not a recommendation; it is the independent data the orchestrator uses to validate the moat and market-share claims of other workers.

When To Use

  • Orchestrator dispatches in Standard or Strict depth (Tier-1 always-on at those depths).
  • Lite depth and a peer set of >=2 names exists and the question is valuation- or moat-shaped ("expensive", "cheap", "fair price", "moat", "vs peers", "losing share") -> dispatched in Lite mode: General 12-item panel only, no archetype-specific extension. See stock-analysis-lead/references/dispatch-protocol.md Part 1 Tier 1 for the authoritative trigger.
  • User explicitly asks "how does X compare to peers".
  • A specific competitive claim ("losing share", "best-in-class margins") needs independent verification.

When NOT To Use

  • Lite depth with no peer set, or a Lite question that is not valuation/moat-shaped — the Tier-1 trigger does not fire and the orchestrator scores the peer-dependent items UNSCORED rather than guessing.
  • Companies with no comparable peers (rare, but e.g., single-issuer ADRs, novel asset classes).
  • Sector ETFs or funds (compose peer index instead).

Mandatory Gates

1) Peer Set Validation Gate

Must use 2–4 peers identified by the orchestrator (typically from the 10-K Item 1 Competition section + WebSearch). Reject peer-set selections that are obviously cherry-picked weaker:

  • All peers materially smaller than target (>10× revenue gap)
  • All peers in distressed states
  • Peer set excludes the obvious #1 in the category

Read the full file on GitHub · 243 lines

Files

What ships with it

3 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.

Changes

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.

  1. 13d ago First seen · 243 lines · 127 tokens per session scan A 11002fc2e03e

Subscribe to this mod's changes

stock-peer-comparison-review is a skill published in the GitHub repository johnqtcg/awesome-skills (30 stars, last pushed yesterday), licensed MIT. It adds 127 tokens to every session and 3,114 once invoked, about $0.0006 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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