Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligencenpx agentmods add commands/agentii-ai/agentii-investment-intelligence/peer-benchWrote 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/commands/agentii-ai/agentii-investment-intelligence/peer-bench)<a href="https://agentmods.dev/commands/agentii-ai/agentii-investment-intelligence/peer-bench"><img src="https://agentmods.dev/badge/commands/agentii-ai/agentii-investment-intelligence/peer-bench/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/agentii-ai/agentii-investment-intelligence/peer-bench"><img src="https://agentmods.dev/badge/commands/agentii-ai/agentii-investment-intelligence/peer-bench.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00016 | $0.00147 |
| Opus 5 | $0.00008 | $0.00073 |
| Sonnet 5 | $0.00003 | $0.00029 |
| Haiku 4.5 | $0.00002 | $0.00015 |
Grade A, and why
peer-bench 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 10d 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
Workflow
- Validate ticker argument.
- Delegate to the
peer-benchskill bundled underindustry-analysis. - Return the structured deliverable produced by the skill. Output written to
{ticker}/{YYYY-MM-DD_HHMM}_peer-bench_{affix}.md.
See Mode syntax for
--mode=/--modes=/--peers=invocation rules.
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.
- 10d ago First seen · 13 lines · 16 tokens per session scan A 75d780097aba
peer-bench is a command published in the GitHub repository agentii-ai/agentii-investment-intelligence (203 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 147 once invoked, about $0.0001 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.
Other commands, from other repositories
earnings
Post-earnings analysis for a stock — fast in-chat summary card by default, full Markdown research report on request.
monitor
AML transaction monitoring and suspicious activity reporting.
pricing-proposal
Propose a pricing structure with the value, margin, and competitive cases argued, and the migration cost stated.
budget
Marks the cheapest Claude model and effort that still clears this task with high confidence. Still shows the full Budget, Optimal, and Splurge spread.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.