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/ccplugins/awesome-claude-code-plugins/peer-readacrossnpx skills add ccplugins/awesome-claude-code-plugins --skill peer-readacrossgit clone --depth 1 https://github.com/ccplugins/awesome-claude-code-pluginsWhat 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 | $0.00073 | $0.00495 |
| Opus 5 | $0.00036 | $0.00247 |
| Sonnet 5 | $0.00015 | $0.00099 |
| Haiku 4.5 | $0.00007 | $0.00049 |
Grade A, and why
peer-readacross 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 2d 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.
What it actually says
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
- Resolve both tickers. If given company names, map them with
alphai_tickers(q=...). Any symbol that isn't a recognized active ticker comes back inunknown_tickersand contributes no rows — surface that. - 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. - Tighten on request. Raise
min_relevance(default 4) for only the strongest items, orlimitfor 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_analysisreturns 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.
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.
- 2d ago First seen · 41 lines · 73 tokens per session scan A e204cea15c61
peer-readacross is a skill published in the GitHub repository ccplugins/awesome-claude-code-plugins (929 stars, last pushed 20d ago), licensed Apache-2.0. It adds 73 tokens to every session and 495 once invoked, about $0.0004 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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