---
name: Peer Read-Across
slug: peer-read-across
category: Data
description: Peer Read-Across compares two tickers and surfaces shared news plus each name’s own context. Use it when you want to understand how a peer, competitor, supplier, or customer may affect another ticker.
github: "https://github.com/ccplugins/awesome-claude-code-plugins/tree/main/plugins/alphai/skills/peer-readacross"
language: JavaScript
stars: 922
forks: 392
install: "npx degit https://github.com/ccplugins/awesome-claude-code-plugins/tree/main/plugins/alphai/skills/peer-readacross ~/.claude/skills/peer-readacross"
installs_to: ~/.claude/skills/peer-readacross
source_path: plugins/alphai/skills/peer-readacross/SKILL.md
collection_size: 25
category_size: 668
collection_url: "https://dirskills.com/collections/ccplugins/awesome-claude-code-plugins"
added: 2026-08-22T05:20:42.562Z
last_synced: 2026-08-22T05:20:42.562Z
canonical_url: "https://dirskills.com/skills/peer-read-across"
---

# Peer Read-Across

Peer Read-Across compares two tickers and surfaces shared news plus each name’s own context. Use it when you want to understand how a peer, competitor, supplier, or customer may affect another ticker.

**Install:**

```bash
npx degit https://github.com/ccplugins/awesome-claude-code-plugins/tree/main/plugins/alphai/skills/peer-readacross ~/.claude/skills/peer-readacross
```

## README

# 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.