# Peer Readacross

> 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).

- **Type:** Skill
- **Install:** `agentstack add skill-makeev-alphai-claude-skills-peer-readacross`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [makeev](https://agentstack.voostack.com/s/makeev)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [makeev](https://github.com/makeev)
- **Source:** https://github.com/makeev/alphai-claude-skills/tree/main/skills/peer-readacross
- **Website:** https://alphai.io/mcp

## Install

```sh
agentstack add skill-makeev-alphai-claude-skills-peer-readacross
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [makeev](https://github.com/makeev)
- **Source:** [makeev/alphai-claude-skills](https://github.com/makeev/alphai-claude-skills)
- **License:** MIT
- **Homepage:** https://alphai.io/mcp

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-makeev-alphai-claude-skills-peer-readacross
- Seller: https://agentstack.voostack.com/s/makeev
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.