Borrowing it
Nothing to install: this file belongs to daloopa/investing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/daloopa/investing/main/.claude/skills/comp-sheet/SKILL.mdgit clone --depth 1 https://github.com/daloopa/investingWrote 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.
[](https://agentmods.dev/skills/daloopa/investing/comp-sheet)<a href="https://agentmods.dev/skills/daloopa/investing/comp-sheet"><img src="https://agentmods.dev/badge/skills/daloopa/investing/comp-sheet/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.
<a href="https://agentmods.dev/skills/daloopa/investing/comp-sheet"><img src="https://agentmods.dev/badge/skills/daloopa/investing/comp-sheet.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00015 | $0.02061 |
| Opus 5.5 | $0.00006 | $0.00824 |
| Sonnet 5.5 | $0.00003 | $0.00412 |
| Haiku 4.5 | $0.00002 | $0.00206 |
Grade A, and why
comp-sheet 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 2mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- comp-sheet — 91% identical, 32 lines differ
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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.
- 2mo ago First seen · 187 lines · 15 tokens per session scan A fe34a24b94d2
comp-sheet is a skill published in the GitHub repository daloopa/investing (488 stars, last pushed 8d ago), licensed Apache-2.0. It adds 15 tokens to every session and 2,061 once invoked, about $0.0001 per session on Opus 5.5. 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.
comp-sheet compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| comp-sheetdaloopa/investing | Skill | 488 | 8d ago | Apache-2.0 | A | 15 | Claude Code |
| dcf-modelNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 14 | Claude Code, Codex |
| comps-analysisNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 13 | Claude Code, Codex |
| lbo-modelNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 19 | Claude Code, Codex |
| lbo-modelanthropics/financial-services | Skill | 41k | 9d ago | Apache-2.0 | A | 57 | Claude Code |
| 3-statement-modelNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 18 | Claude Code, Codex |
| merger-modelNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 18 | Claude Code, Codex |