peer-bench

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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

AI & Automation 204 stars 16 forks Updated today Apache-2.0

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Quality Score: 94/100

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Description 5%
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Skill Content

# 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...

Details

Author
agentii-ai
Repository
agentii-ai/agentii-investment-intelligence
Created
4 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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