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.
npx agentmods add skills/daloopa/investing/compsnpx skills add daloopa/investing --skill compsgit clone --depth 1 https://github.com/daloopa/investingWhat 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 | $0.00013 | $0.02508 |
| Opus 5 | $0.00006 | $0.01254 |
| Sonnet 5 | $0.00003 | $0.00502 |
| Haiku 4.5 | $0.00001 | $0.00251 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- comps — 100% identical, 0 lines differ
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_idlatest_calendar_quarter— anchor for all period calculations below (see../data-access.mdSection 1.5)latest_fiscal_quarter- Firm name for report attribution (default: "Daloopa") — see
../data-access.mdSection 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.
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.
- 3d ago First seen · 199 lines · 13 tokens per session scan A 3370261ec7cb
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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