comps

comps is a skill for Claude Code, Codex from daloopa/investing. It costs 13 tokens per session (2,508 once invoked), scanned A, original, Apache-2.0.

A trading-comparables analysis that values a company by comparing it with similar public companies. Valuation multiples are ratios such as price-to-earnings or enterprise-value-to-revenue used to compare companies.

In plain words
What is it for?
Use it to identify five to ten peers and analyze their business models, sectors, size, growth, competitive position, and valuation multiples.
Why use it?
It structures the peer selection and comparison work needed to estimate how the target company may be valued relative to relevant competitors and other similar businesses.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/daloopa/investing/comps
Any agent
npx skills add daloopa/investing --skill comps
Clone the repo
git clone --depth 1 https://github.com/daloopa/investing

Made for: Claude Code, Codex.

Per session 13 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,508 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00013 $0.02508
Opus 5 $0.00006 $0.01254
Sonnet 5 $0.00003 $0.00502
Haiku 4.5 $0.00001 $0.00251

Measured 3d ago against content hash 3370261ec7cb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

comps 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 3d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

  • comps — 100% identical, 0 lines differ
.claude/skills/comps/SKILL.md · 199 lines

How it starts

The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Build a trading comparables analysis for the company specified by the user: $ARGUMENTS

Before starting, read ../data-access.md for data access methods and ../design-system.md for formatting conventions. Follow the data access detection logic and design system throughout this skill.

Follow these steps:

1. Company Lookup

Look up the company by ticker using discover_companies. Capture:

  • company_id
  • latest_calendar_quarter — anchor for all period calculations below (see ../data-access.md Section 1.5)
  • latest_fiscal_quarter
  • Firm name for report attribution (default: "Daloopa") — see ../data-access.md Section 4.5

2. Identify Peer Group

Based on the company's business model, sector, size, and competitive landscape, identify 5-10 comparable companies. Consider:

  • Direct competitors in the same market
  • Business model peers (similar revenue model even if different sector)
  • Size peers (similar market cap range)
  • Growth profile peers (similar growth rate)

Prioritize relevance over size matching. A direct competitor at a different scale is more useful than a similar-sized company in a different industry.

List the peer tickers and briefly justify each selection (1 sentence).

3. Target Company Fundamentals

Calculate 4 quarters backward from latest_calendar_quarter. Pull from Daloopa for the target company:

  • Revenue (compute trailing 4Q total)
  • EBITDA (compute trailing 4Q; if not available, use Op Income + D&A, label "(calc.)")
  • Net Income (trailing 4Q)
  • Diluted EPS (trailing 4Q sum)
  • Free Cash Flow (trailing 4Q; compute as OCF - CapEx, label "(calc.)")
  • Revenue YoY growth (most recent quarter)
  • Operating Margin (most recent quarter)
  • Net Margin (most recent quarter)

4. Stock Prices & Valuation Multiples

Use get_stock_prices (see ../data-access.md Section 1.7) to pull current prices for the target AND all peers in a single batch call — pass all company_ids together with dates = 3 most recent calendar days.

Read the full file on GitHub · 199 lines

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. 3d ago First seen · 199 lines · 13 tokens per session scan A 3370261ec7cb

Subscribe to this mod's changes

comps is a skill published in the GitHub repository daloopa/investing (486 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 13 tokens to every session and 2,508 once invoked, about $0.0001 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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