Agent skill

Comp Sheet

Build an industry comp sheet Excel model with deep operational KPIs

Apache-2.0Auto-check passedDocuments & Office

Install Comp Sheet

skills CLI

$ npx skills add daloopa/investing --skill comp-sheet -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install daloopa/investing comp-sheet --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/daloopa/investing.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/comp-sheet .claude/skills/comp-sheet && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "comp-sheet" agent skill from https://github.com/daloopa/investing/tree/main/.claude/skills/comp-sheet into .claude/skills/comp-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "comp-sheet", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/daloopa/investing/tree/main/.claude/skills/comp-sheet

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add daloopa/investing --skill comp-sheet -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install daloopa/investing comp-sheet --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/daloopa/investing.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/comp-sheet .agents/skills/comp-sheet && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "comp-sheet" agent skill from https://github.com/daloopa/investing/tree/main/.claude/skills/comp-sheet into .agents/skills/comp-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "comp-sheet", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add daloopa/investing --skill comp-sheet -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install daloopa/investing comp-sheet --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/daloopa/investing.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/comp-sheet .cursor/skills/comp-sheet && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "comp-sheet" agent skill from https://github.com/daloopa/investing/tree/main/.claude/skills/comp-sheet into .cursor/skills/comp-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "comp-sheet", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/daloopa/investing.git --path .claude/skills/comp-sheet

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add daloopa/investing --skill comp-sheet -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install daloopa/investing comp-sheet --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/daloopa/investing.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/comp-sheet .gemini/skills/comp-sheet && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "comp-sheet" agent skill from https://github.com/daloopa/investing/tree/main/.claude/skills/comp-sheet into .gemini/skills/comp-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "comp-sheet", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install daloopa/investing comp-sheet

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add daloopa/investing --skill comp-sheet -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/daloopa/investing.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/comp-sheet .github/skills/comp-sheet && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "comp-sheet" agent skill from https://github.com/daloopa/investing/tree/main/.claude/skills/comp-sheet into .github/skills/comp-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "comp-sheet", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add daloopa/investing --skill comp-sheet -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install daloopa/investing comp-sheet --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/daloopa/investing.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/comp-sheet .opencode/skills/comp-sheet && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "comp-sheet" agent skill from https://github.com/daloopa/investing/tree/main/.claude/skills/comp-sheet into .opencode/skills/comp-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "comp-sheet", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: comp-sheet
GitHub stars: 489
Token cost: ~2k tokens
SKILL.md length: 850 words
Files: 1
Skills in repo: 22
Repo updated: Jul 22, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Build an industry comp sheet Excel model with deep operational KPIs

Works in 7 steps: Company & Peer Setup → Deep Data Gathering → KPI Discovery & Mapping → …
Tasks that involve Excel spreadsheets
SKILL.md covers 1. Company & Peer Setup, 2. Deep Data Gathering, 3. KPI Discovery & Mapping and 4. Compute Derived Metrics, plus 3 more sections
Calls python3

What it does

Comp Sheet is an agent skill from daloopa/investing. Build an industry comp sheet Excel model with deep operational KPIs

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Documents & Office, covering Excel spreadsheets and OKRs and executive reporting. The licence is Apache-2.0.

When your agent uses it

Tasks that involve Excel spreadsheets
Tasks that involve OKRs and executive reporting

Example prompts

“/comp-sheet”

Requirements

Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

Company & Peer Setup
Deep Data Gathering
KPI Discovery & Mapping
Compute Derived Metrics
Build Context JSON
Render Excel
Output

What it can do on your machine

Read from SKILL.md and the folder at commit f46f350. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- daloopa.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Comp Sheet loads about 2k tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 850 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~20
When it runs · the whole SKILL.md, loaded when a task matches: ~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from daloopa/investing at commit f46f350, republished under its Apache-2.0 licence (© daloopa). 850 words, ~2,018 tokens.

Download SKILL.mdSave it as .claude/skills/comp-sheet/SKILL.md (or your agent's skills folder).

name: comp-sheet
description: Build an industry comp sheet Excel model with deep operational KPIs
argument-hint: TICKER

Build a multi-company industry comp sheet Excel model for the company specified by the user: $ARGUMENTS

This produces an interactive .xlsx workbook — the kind of comp sheet every analyst on a coverage team maintains. Multi-company, multi-tab, with deep operational KPIs alongside standard financials.

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 & Peer Setup

Look up the target company by ticker using discover_companies. Capture company_id, latest_calendar_quarter (anchor for all period calculations — see ../data-access.md Section 1.5), and latest_fiscal_quarter. Note the firm name for report attribution (default: "Daloopa") — see ../data-access.md Section 4.5.

Then identify 6-10 comparable companies using the same logic as /comps:

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

Look up all peer company_ids via Daloopa. If a peer isn't available in Daloopa, include it with market data only and note the limitation.

List the full peer group with brief justification for each.

2. Deep Data Gathering

For each company (target + all peers), pull from Daloopa:

Calculate 8 quarters backward from latest_calendar_quarter. Pull financials:

Revenue, Gross Profit, Operating Income, Net Income, Diluted EPS
Operating Cash Flow, Capital Expenditures, D&A
Free Cash Flow (compute as OCF - CapEx)
R&D Expense, SG&A (where available)

Segment revenue breakdown (all available segments, 8 quarters)

Company-specific operational KPIs — use the 9-sector taxonomy to know what to search for:

SaaS/Cloud: ARR, net revenue retention, RPO/cRPO, customers >$100K, cloud gross margin
Consumer Tech: DAU/MAU, ARPU, engagement metrics, installed base, paid subscribers
E-commerce/Marketplace: GMV, take rate, active buyers/sellers, order frequency
Retail: same-store sales, store count, average ticket, transactions
Telecom/Media: subscribers, churn, ARPU, content spend
Hardware: units shipped, ASP, attach rate, installed base
Financial Services: AUM, NIM, loan growth, credit quality metrics, fee income ratio
Pharma/Biotech: pipeline stage, patient starts, scripts, market share
Industrials/Energy: backlog, book-to-bill, utilization, production volumes, reserves

Stock prices & valuation multiples: Use get_stock_prices (see ../data-access.md Section 1.7) to pull prices for ALL companies in a single batch call. Get:

Current price: dates = 3 most recent calendar days for all company_ids
Quarter-end prices: dates = quarter-end dates matching the financial periods (for historical multiples)

Then compute valuation metrics by combining stock prices with Daloopa fundamentals:

Market Cap = Close price × Diluted shares outstanding
Enterprise Value = Market Cap + Total Debt - Cash
P/E (trailing) = Market Cap / Net Income (trailing 4Q)
EV/EBITDA = EV / EBITDA (trailing 4Q)
P/S = Market Cap / Revenue (trailing 4Q)
P/B = Market Cap / Total Equity
EV/FCF = EV / Free Cash Flow (trailing 4Q)
FCF Yield = FCF (trailing 4Q) / Market Cap
Dividend Yield = Dividends Paid (trailing 4Q) / Market Cap

For beta, use infra scripts or web search (see ../data-access.md Section 2). For forward multiples, use consensus estimates if available (Section 3).

3. KPI Discovery & Mapping

After pulling data, build the KPI mapping:

Which KPIs are available for which companies? Build a coverage matrix.
Group KPIs into categories:
- Segment Revenue: product/service line breakdowns
- Growth KPIs: subscriber growth, unit growth, same-store sales growth
- Unit Economics: ARPU, ASP, take rate, retention
- Efficiency: R&D % of revenue, SBC % of revenue, CapEx % of revenue
- Engagement: DAU/MAU, retention, churn
Flag KPIs that are comparable across peers vs company-specific

Show full SKILL.md (315 more words)Show less

4. Compute Derived Metrics

For each company, calculate:

Margins:

Gross Margin, Operating Margin, Net Margin, FCF Margin (each quarter)

Growth rates:

Revenue YoY, EPS YoY, segment revenue YoY (each quarter where year-ago data exists)

Capital metrics:

Net Debt (Total Debt - Cash)
Net Debt/EBITDA
Shareholder Yield (Buybacks + Dividends) / Market Cap

Historical multiples (from quarter-end prices pulled in Section 2):

Compute P/E, EV/EBITDA, P/S, EV/FCF at each quarter-end to show how multiples have trended
This lets the reader see whether the current multiple is elevated or depressed vs. the company's own history

Implied valuation:

For each valuation methodology (P/E, EV/EBITDA, P/S, EV/FCF):
- Peer median multiple × target metric = implied value
- Convert to implied share price
Compute median implied price across methodologies

5. Build Context JSON

Structure the data as a multi-company context JSON for the comp_builder:

json

{
  "target_ticker": "AAPL",
  "as_of_date": "YYYY-MM-DD",
  "companies": [
    {
      "ticker": "AAPL",
      "name": "Apple Inc.",
      "is_target": true,
      "market_data": {
        "price": ..., "market_cap": ..., "enterprise_value": ...,
        "shares_outstanding": ..., "beta": ...,
        "trailing_pe": ..., "forward_pe": ...,
        "ev_ebitda": ..., "price_to_sales": ...,
        "ev_fcf": ..., "dividend_yield": ...
      },
      "periods": ["2024Q1", "2024Q2", ...],
      "financials": {
        "Revenue": {"2024Q1": ..., ...},
        "Gross Profit": {...}, ...
      },
      "margins": {
        "Gross Margin": {"2024Q1": ..., ...}, ...
      },
      "growth": {
        "Revenue Growth YoY": {"2024Q1": ..., ...}, ...
      },
      "kpis": {
        "iPhone Revenue": {"2024Q1": ..., ...}, ...
      },
      "kpi_categories": {
        "Segment Revenue": ["iPhone Revenue", "Services Revenue", ...],
        "Growth KPIs": ["Services Growth YoY"],
        "Efficiency": ["R&D % Revenue", "SBC % Revenue"]
      }
    },
    ...more companies...
  ],
  "implied_valuation": {
    "pe_implied": ...,
    "ev_ebitda_implied": ...,
    "ps_implied": ...,
    "ev_fcf_implied": ...,
    "median_implied": ...
  }
}

Save to reports/.tmp/{TICKERS}_comp_context.json.

6. Render Excel

Build the comp sheet workbook (see ../data-access.md Section 5 for infrastructure): python3 infra/comp_builder.py --context reports/.tmp/{TICKERS}_comp_context.json --output reports/{TICKERS}_comp_sheet.xlsx

The builder creates 8 tabs:

Comp Summary — one-pager with all companies, multiples, implied valuation
Revenue Drivers — unit economics decomposition per company (trailing 4Q)
Operating KPIs — cross-company KPI comparison matrix
Financial Summary — side-by-side income statements (trailing 4Q)
Growth & Margins — trend analysis (up to 8Q)
Valuation Detail — implied prices by methodology, premium/discount
Balance Sheet & Capital — leverage and capital returns
Raw Data — full quarterly appendix for each company

7. Output

Tell the user where the .xlsx was saved.

Highlight in your summary:

Target positioning vs peers: Where does it rank on growth, margins, and valuation?
Most differentiated KPIs: Which operational metrics set the target apart (positive or negative)?
Implied valuation range: What does the peer group suggest the stock is worth?
Key risk: What's the biggest vulnerability the comp sheet reveals (e.g., premium valuation with decelerating KPIs, margins below peers, etc.)?

All financial figures in the summary must use Daloopa citation format: $X.XX million

Files

Just SKILL.md in .claude/skills/comp-sheet of daloopa/investing.

Open the folder on GitHubat commit f46f350

Compare with similar skills

Comp Sheet next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Comp Sheet compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Comp Sheet this skilldaloopa/investing	489	—	~2k	Automated safety check: Pass	Apache-2.0	2 mo ago
Officecli Data DashboardFerroxLabs/wayland	608	4 repos	~9.2k	Automated safety check: Pass	AGPL-3.0	yesterday
Dashboard BuilderTheCraigHewitt/skills	156	—	~1.5k	Automated safety check: Pass	MIT	4 mo ago
Quality ReportLeoYeAI/openclaw-master-skills	2.2k	—	~5.2k	Automated safety check: Pass	MIT	2 mo ago
Data ReportZJU-REAL/Easel	3.2k	—	~479	Automated safety check: Pass	Apache-2.0	2 days ago
Executive Dashboard Generatormanojbajaj95/claude-gtm-plugin	104	2 repos	~3.4k	Automated safety check: Pass	MIT	19 days ago

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Questions about Comp Sheet

What does Comp Sheet do?

Build an industry comp sheet Excel model with deep operational KPIs. Comp Sheet is an agent skill from daloopa/investing.

When should I use Comp Sheet?

Comp Sheet fits situations like: tasks that involve Excel spreadsheets; tasks that involve OKRs and executive reporting.

How do I install Comp Sheet in Claude Code?

Run `npx skills add daloopa/investing --skill comp-sheet -a claude-code`. Or copy the skill folder (.claude/skills/comp-sheet in daloopa/investing) into .claude/skills/comp-sheet in your project. Claude Code loads it when a task matches its description.

How do I install Comp Sheet in Codex?

Run `npx skills add daloopa/investing --skill comp-sheet -a codex`. Or copy the skill folder (.claude/skills/comp-sheet in daloopa/investing) into .agents/skills/comp-sheet in your project. Codex loads it when a task matches its description.

Can I use Comp Sheet in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add daloopa/investing --skill comp-sheet -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/comp-sheet, .gemini/skills/comp-sheet, .github/skills/comp-sheet and .opencode/skills/comp-sheet in your project.

What does Comp Sheet need to run?

Going by SKILL.md and its folder, Comp Sheet needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Comp Sheet access the network?

SKILL.md names 1 domain. As links in the text: daloopa.com. This is read from the text; nothing was executed.

Is Comp Sheet safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Comp Sheet use?

Comp Sheet is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Comp Sheet use?

About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Comp Sheet?

Skills that share tags, products or a category with Comp Sheet: Officecli Data Dashboard (FerroxLabs/wayland, 608 stars), Dashboard Builder (TheCraigHewitt/skills, 156 stars), Quality Report (LeoYeAI/openclaw-master-skills, 2.2k stars) and Data Report (ZJU-REAL/Easel, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Comp Sheet?

daloopa (a GitHub organization) maintains it in daloopa/investing, which has 489 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on July 22, 2026.

Source: daloopa/investing on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Comp Sheet

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

1. Company & Peer Setup

2. Deep Data Gathering

3. KPI Discovery & Mapping

4. Compute Derived Metrics

5. Build Context JSON

6. Render Excel

7. Output

Files

Compare with similar skills

Similar skills

More from daloopa/investing

Related

Questions about Comp Sheet