# Peer Readacross

Compare two tickers and surface the cross-read between them using the AlphaAI MCP.

- Repository: https://github.com/ccplugins/awesome-claude-code-plugins (991 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-08-12
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add ccplugins/awesome-claude-code-plugins --skill peer-readacross -g -a claude-code -y`
Codex: `npx skills add ccplugins/awesome-claude-code-plugins --skill peer-readacross -g -a codex -y`
Cursor: `npx skills add ccplugins/awesome-claude-code-plugins --skill peer-readacross -g -a cursor -y`
Gemini CLI: `npx skills add ccplugins/awesome-claude-code-plugins --skill peer-readacross -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add ccplugins/awesome-claude-code-plugins`

## Description

Compare two tickers and surface the cross-read between them using the AlphaAI MCP. Use when the user asks to "compare X and Y", "NVDA vs AMD", "what does <peer>'s news mean for <ticker>", or wants the read-across between two related names (competitors, supplier/customer, same theme).

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Peer read-across

When two names are linked, competitors, a supplier and its customer, two plays
on one theme, the interesting signal is the **read-across**: a peer's print that
resets the other's setup. `alphai_pair_analysis` is built for exactly this.

## Steps

1. **Resolve both tickers.** If given company names, map them with
   `alphai_tickers(q=...)`. Any symbol that isn't a recognized active ticker
   comes back in `unknown_tickers` and contributes no rows, surface that.
2. **Run the comparison.** Call `alphai_pair_analysis(ticker_a, ticker_b)`. It
   returns three things: news naming **both** companies (the shared read-across),
   plus each ticker's **own** recent news for context.
3. **Tighten on request.** Raise `min_relevance` (default 4) for only the
   strongest items, or `limit` for more rows per list.

## Output

- **Shared story**, the news naming both names: what links them right now and
  which way the read-across cuts (does A's news help or hurt B?).
- **{Ticker A}**, 2, 3 of its own top stories, one-liners.
- **{Ticker B}**, same.
- **Net read**, one or two sentences: are they moving together or diverging, and
  what's the single linking factor (a shared customer, a sector catalyst, a
  head-to-head product)?

## Guardrails

- If `alphai_pair_analysis` returns no shared rows, say so, the two names may
  simply not be in the same story flow right now; fall back to summarizing each
  side's own news rather than forcing a connection.
- Lead with `relevance_score`.
- Describe the read-across the reporting supports; don't manufacture a causal
  link. News, not advice.