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/daloopa-plugin-claude/compsnpx skills add daloopa/daloopa-plugin-claude --skill compsgit clone --depth 1 https://github.com/daloopa/daloopa-plugin-claudeWrote 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/daloopa-plugin-claude/comps)<a href="https://agentmods.dev/skills/daloopa/daloopa-plugin-claude/comps"><img src="https://agentmods.dev/badge/skills/daloopa/daloopa-plugin-claude/comps.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00013 | $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 6d 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.
This is a copy
100% identical to comps — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 6d ago First seen · 199 lines · 13 tokens per session scan A 3370261ec7cb
comps is a skill published in the GitHub repository daloopa/daloopa-plugin-claude (8 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. It is 100% identical to comps, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
sector-rotation
行业轮动分析——申万行业景气度评分、行业动量排名、产业链传导、估值/盈利/资金流多维比较框架.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
Use when evaluating A-share limit-up (涨停板) setups through Chen Hao's sentiment and momentum lens: market emotion cycles, board strength, follow-through, and short-term aggressive momentum trading.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.