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 skills add agentii-ai/agentii-investment-intelligence --skill peer-benchgit clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligenceWrote 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/skills/agentii-ai/agentii-investment-intelligence/peer-bench)<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/peer-bench"><img src="https://agentmods.dev/badge/skills/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/skills/agentii-ai/agentii-investment-intelligence/peer-bench"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/peer-bench.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.01764 |
| Opus 5 | $0.00022 | $0.00882 |
| Sonnet 5 | $0.00009 | $0.00353 |
| Haiku 4.5 | $0.00004 | $0.00176 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
peer-bench
Triggers
- Peer benchmarking
- multi-ticker financial comparison
- growth value matrix
- composite z-score ranking
- industry peer comparison
- competitive benchmarking
- sector relative performance
- peer group analysis
- industry leader comparison
- financial ratio benchmarking
Defaults
| Parameter | Default Value | Rationale |
|---|---|---|
| ticker | (required) | Stock symbol to analyze |
| lookback_quarters | 4 | Standard lookback for this skill type |
Methodology
1. Retrieval Scope
This skill operates with retrieval_scope: unstructured_document_search. It performs unstructured document search at scale via the three-layer retrieval protocol (Layer 1→2→2.5→3), escalating to read_source_deep_outline only when lightweight labels cannot disambiguate pages, plus structured XBRL where needed.
2. Retrieval Strategy
Follows the retrieval strategy decision tree in contracts/retrieval.md. Primary branch: (b)/(c) Unstructured Query via the three-layer protocol. Resolve the canonical ticker first (exact → fuzzy alias → share-class) before any data call.
3. Temporal Scope
Default lookback: 4 fiscal quarter(s); maximum: 12. The default balances recency against the trend window this analysis requires.
4. Tool Allowlist
Per frontmatter allowed_tools:
search_companies— ticker resolution + company context (entity-alias fuzzy match)search_xbrl_facts— primary structured financial facts (is_primary default)search_documents— Layer 1 document discovery (page-level silver records)search_sec_filings— Layer 1 SEC filing metadata indexget_company_financials— consolidated IS/BS/CF highlightsbatch_search— consolidate 3+ same-tool queries into one metered calllist_coverage— universe-level coverage discoveryread_source_outline— Layer 2 lightweight page map (description + keywords)read_source_deep_outline— Layer 2.5a deep page map (table_titles/drivers/metrics)list_xbrl_concepts— XBRL concept discovery for non-standard line items (namespaceparam; defaultus-gaap— useifrs-fullfor foreign filers)read_source_pages— Layer 3 deep read of selected pages with table markerssearch_keyword_in_source— Layer 2.5b keyword page filter for large documents
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · -7 lines · -4 tokens per session 0379137c2187
- 7d ago Changed · +12 lines · +4 tokens per session d4195cb53537
- 12d ago First seen · 133 lines · 44 tokens per session scan A 9272517c0d4a
peer-bench is a skill published in the GitHub repository agentii-ai/agentii-investment-intelligence (204 stars, last pushed yesterday), licensed Apache-2.0. It adds 44 tokens to every session and 1,764 once invoked, about $0.0002 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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