comps

comps is a skill for Claude Code from agentii-ai/agentii-investment-intelligence. It costs 39 tokens per session (1,766 once invoked), scanned A, original, Apache-2.0.

A financial comparison of companies in the same or related industries. It uses measures such as enterprise value, EBITDA, and price-to-earnings ratios to compare how the market values them.

In plain words
What is it for?
Creating trading-comps tables, peer comparisons, valuation benchmarks, and investment analyses using filings such as annual, quarterly, and current reports.
Why use it?
It provides a structured way to judge whether a company looks expensive or cheap compared with selected peers. It gathers figures from company filings and applies a defined retrieval process.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the models-and-pitches plugin — 8 skills, 9 commands shipped together

Good fit Creating trading-comps tables, peer comparisons, valuation benchmarks, and investment analyses using filings such as annual, quarterly, and current reports.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agentii-ai/agentii-investment-intelligence/comps
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 agentii-ai/agentii-investment-intelligence --skill comps
Clone the repo
git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence

Made for: Claude Code.

Or install models-and-pitches, the plugin that ships this one along with the rest of its 8 skills, 9 commands.

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 comps

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/comps/github.svg)](https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/comps)
Your own site
<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/comps"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/comps/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 comps

Your own site · 80×15
<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/comps"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/comps.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,766 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 $0.00039 $0.01766
Opus 5 $0.00019 $0.00883
Sonnet 5 $0.00008 $0.00353
Haiku 4.5 $0.00004 $0.00177

Measured 4d ago against content hash e222c832a45d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 4d 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.

plugins/vertical-plugins/models-and-pitches/skills/agentii/comps/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md.

Triggers

  • analyze comps analysis
  • run comps analysis analysis
  • produce comps analysis report
  • comps analysis breakdown
  • comps analysis deep dive
  • build a comps analysis
  • assess comps analysis
  • quantify comps analysis
  • compare comps analysis across peers
  • review comps analysis for
  • generate comps analysis on
  • comps analysis for investment decision

Defaults

Parameter Default Notes
lookback_years 3 Historical data window
include_peers false Whether to surface a peer comparison block

Methodology

Retrieval Scope

This skill performs unstructured document search at scale across SEC filings and earnings call transcripts (10-K, 10-Q, 8-K). The three-layer agent-use-ready retrieval protocol (Document Discovery → Page Map → Deep Read) applies to all unstructured document search at scale.

Retrieval Strategy

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

Temporal Scope

Default: 12 fiscal quarters (max 20). Financial modeling: trailing 12 quarters (3 fiscal years) for long-range projection inputs.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

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

Deliverable Chain

InputsBuildValidateOutputNext

  1. Inputs: resolved ticker + peers via search_companies + search_xbrl_facts for all tickers (revenue, EBITDA, EPS, multiples) + get_company_financials.
  2. Build: write a self-contained Python script using openpyxl that creates the comps workbook (peer profiles, trading multiples, valuation summary) per ## Output Structure. Execute via Bash: python3 script.py. Verify the .xlsx exists. If import openpyxl fails, fall back to .md summary with data_availability: degraded (see contracts/office-tooling.md).
  3. Validate: run LibreOffice recalc; audit per ## Validation Gates.
  4. Output: write the artifact path per ## Output File.
  5. Next: append to agentii.md; hand off to a downstream pitch/review skill if requested.

Read the full file on GitHub · 137 lines

Files

What ships with it

8 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. 4d ago Changed e222c832a45d
  2. 10d ago First seen · 137 lines · 39 tokens per session scan A a70a01d6ee7f

Subscribe to this mod's changes

comps is a skill published in the GitHub repository agentii-ai/agentii-investment-intelligence (203 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 1,766 once invoked, about $0.0002 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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