business-model

name: business-model multi_ticker_semantics: target_with_optional_peers description: Business model classification, business model analysis, structural analysis of how a company makes money, product offering decomposition, distribution channel analysis, customer segment analysis, revenue model identification, market sizing TAM SAM SOM, competitive positioning, business unit performance, management team & leadership analysis, what does the company sell, how does the company go to market, business model type platform service product, channel mix direct vs indirect, revenue concentration risk, CEO CFO executive backgrounds and changes temporal_scope: default_quarters: 4 max_quarters: 8 description: "Business-model analysis is a structural snapshot. Default lookback is 1 fiscal quarter (the most recent disclosure). Max 8 quarters when analyzing channel mix evolution or management changes over a 2-year window." allowed_tools: - search_companies - search_xbrl_facts - search_documents - search_sec_filings - read_source_outline - read_source_deep_outline - read_source_pages - search_keyword_in_source - get_company_financials - get_company_profile - get_company_fiscal_calendar - get_ticker_coverage - list_xbrl_concepts retrieval_scope: unstructured_document_search min_tool_diversity: 7

Preflight

!curl -s -o /dev/null -w "%{http_code}" --max-time 2 https://mcp.agentii.ai/mcp/health 2>/dev/null || echo "UNREACHABLE"

**Ticker resolution **: Before any data retrieval, resolve the ticker via the three-layer fallback per retrieval.md Pre-Flight Step 0: (1) exact match via search_companies(ticker=<input>), (2) pg_trgm fuzzy alias match via gold.entity_aliases (6,721 rows), (3) share class normalization for multi-class tickers (GOOG/GOOGL→GOOG, BRK.A/BRK.B→BRK.B). Return canonical ticker, match method, and confidence indicator.

**Workspace style.md override check **: Check ./style.md in the workspace root for per-workspace overrides (default_lookback_quarters, reporting_currency, sector_focus, output_verbosity, peer_universe). Apply overrides to output formatting and temporal scope. Precedence: workspace style.md > package style.md > skill defaults.

Agent Call Tracing: The first tool you call will return a _run_id in its result. On every subsequent tool call, include HTTP header X-Agentii-Trace: agent={skill_name}; parent={caller_name}; instance={instance_label}. The MCP server will inject run_id, depth, and user_id automatically. When spawning parallel sub-agents of the same type, assign each a unique instance label (e.g., equity-research-1, equity-research-2). See contracts/x-agentii-trace-header.md for the full contract.

Triggers

• analyze the business model of {ticker}
• run business model analysis on {ticker}
• decompose the business model of {ticker}
• what does {ticker} sell and how does it make money
• classify the business model type of {ticker}
• analyze {ticker} product offerings and distribution channels
• assess {ticker} revenue composition and concentration risk
• evaluate {ticker} TAM SAM SOM and market positioning
• analyze {ticker} management team and leadership changes
• review {ticker} go-to-market strategy
• breakdown {ticker} customer segments and distribution model
• structural analysis of {ticker}
• business unit performance analysis for {ticker}
• competitive positioning of {ticker}

Defaults

Production Grounding

The Neon production database and api.agentii.ai REST/MCP surfaces are LIVE and AUTHORITATIVE as of 2026-05-25. All retrieval planning MUST treat these as ground truth. Production scale: 4.17M gold.xbrl_facts (with is_primary partial index), 11,575 pipeline.src_documents (100% non-null description, GIN-indexed secondary_labels), 243K pipeline.src_silver_pages (covering ALL 5 SEC form types — 8-K/10-K/10-Q/6-K/20-F; labels JSONB with general.description + general.keywords), 4,653 pipeline.earnings_calendar rows, 79 gold.launch_ticker_registry tickers at 100% processing. Always call get_ticker_coverage/{ticker} before retrieval planning.

Methodology

1. Retrieval Scope

This skill performs unstructured document search at scale (10-K, 10-Q, 8-K filings). The three-layer agent-use-ready retrieval protocol applies (Layer 1 → Layer 2 → Layer 3). For foreign issuers, use 20-F (annual) and 6-K (material events) instead of 10-K and 8-K respectively.

2. Retrieval Strategy

Follow the retrieval strategy decision tree in retrieval.md:

• Branch (a) — search_xbrl_facts for revenue concentration, gross margin profile, segment-level P&L (concepts Revenues, GrossProfit, OperatingIncomeLoss, SegmentReportingInformation).
• Branch (b) — search_cross_period when analyzing channel-mix evolution over 2+ years (mode 1_2) or management-change continuity (mode 1_5).
• Branch (c) — single-period three-layer for the most recent 10-K/10-Q business-overview pages (modes 1_1, 1_3, 1_4).

**Layer 1 narrowing — secondary_labels filter **: For 8-K-driven business-model-relevant disclosures, prefer ?secondary_label=other_events_8_01 (item 8.01 covers business-strategy events) AND ?secondary_label=financial_results_2_02 (item 2.02 surfaces segment-level revenue commentary). The GIN-indexed filter on pipeline.src_documents.secondary_labels is the preferred narrowing axis BEFORE Layer 2.

Layer 2 page-relevance signal: Score pages using labels->>'general'->>'description' (~100-char LLM-generated page summary) AND labels->>'general'->>'keywords' (extracted entity terms). Both fields are populated on 96%+ of pipeline.src_silver_pages rows. For business-model analysis, prefer pages whose keywords contain entity terms like product names, segment names, geographies, channel partners, executive names.

3. Temporal Scope

Default: 1 fiscal quarter (max 8). Business-model analysis is a structural snapshot — historical lookback only when explicitly tracking channel-mix evolution (mode 1_2 default 12 quarters) or management changes (mode 1_5 default 4 quarters).

4. Tool Allowlist

See frontmatter allowed_tools — 12 tools declared:

• search_companies, get_company_profile — issuer resolution and sector classification.
• search_xbrl_facts, list_xbrl_concepts, get_company_financials — segment-level P&L and revenue concentration metrics.
• search_documents, search_sec_filings — Layer 1 document discovery (with secondary_labels filter).
• read_source_outline, read_source_pages, search_keyword_in_source — Layer 2/3 deep read for business-overview pages.
• get_company_fiscal_calendar, get_ticker_coverage — pre-flight (mandatory first step).

5. Protocol

1. ** Pre-flight (mandatory)**: call get_company_fiscal_calendar/{ticker} for fiscal orientation, then get_ticker_coverage/{ticker} to discover which data sources are populated. Route based on coverage: sec_filings populated → standard three-layer protocol; xbrl_facts-only → structural inferences from segment data only, flag data_availability: degraded.

2. Layer 1 Document Discovery: search_documents(ticker={T}, form_type=["10-K","20-F"], limit=3) to find the most recent annual report (richest business-overview content). Add ?secondary_labels=financial_results_2_02,other_events_8_01 to also surface relevant 8-Ks. Document identifiers returned in citation_id form (e.g., sec135) — pass these directly to Layer 2.

3. Layer 2 Page Map: read_source_outline/{ticker}/{citation_id} — scan description + keywords for each page. Identify pages covering: Business overview / Item 1 (mode 1_1), Distribution & sales channels (mode 1_2), Revenue by segment & geography (mode 1_3), Industry / market context (mode 1_4), Directors & executive officers (mode 1_5). Bare page_no integers are forbidden in LLM-facing output — always use {ticker} {citation_id} page<N> format (e.g., "LLY sec135 page12").

4. Layer 3 Deep Read: read_source_pages/{ticker}/{citation_id}?row_numbers=page<N1>,page<N2> — load full page_content for ONLY the 3-5 pages identified in Layer 2.

5. XBRL retrieval: search_xbrl_facts(ticker, concept=["Revenues","GrossProfit","OperatingIncomeLoss"], fiscal_year=[<latest>]) — returns is_primary: true rows by default ( superseded; ?include_all_sources=true only for audit). The source_authority field (3=10-K, 2=10-Q, 1=8-K) is returned for fact-provenance transparency.

6. Evidence-pack handoff: produce evidence-pack.json + evidence-digest.md . All citations use the v1.0 frozen format with {ticker} {citation_id} page<N> references.

Modes (5 — structural equity analysis)

This skill delivers analyst-grade output via 5 addressable mode(s); invoke with --mode=<slug> / --modes=<slug1>,<slug2> / --mode=all (see Mode syntax. The default invocation (no flag) runs the essentials_modes subset declared in this skill's frontmatter.

Mode: business-model-classification (1_1 — anchor)

Display name: Business Model & Offerings Classification

Objective: Determine business-model type (product / service / platform), core offering, and market positioning (low-end / mid-tier / high-end) using the most recent quarter's disclosures, sell-side research, and media sentiment.

Output structure:

• Business Model: [Product / Service / Platform]
• Core Offering: [e.g., Connected Wearable, Diagnostic Consumables, SaaS Subscription, Drug Pipeline, Cloud Platform]
• Positioning: [Low-end / Mid-tier / High-end] + brief rationale (e.g., "High-end based on >70% gross margin and premium ARPU vs. peers")
• Citation density: ≥1 citation per 200 words, format {ticker} {citation_id} page<N>.

Mode: distribution-channel-analysis (1_2)

Display name: Distribution Channels & Go-to-Market Analysis

Objective: Assess primary distribution model (direct sales / channel partners / hybrid), channel mix evolution (3-year trailing), and strategic implications for pricing power and customer intimacy.

Output structure:

• Distribution Model: [Direct Sales / Channel Partners / Hybrid]
• Distribution Partners: [list disclosed channel types and representative partners]
• Current Channel Mix: Direct : Indirect = 1 : XX (latest available data)
• Historical Channel Mix Trend (Trailing 3 Years):
• Year -2: [Direct : Indirect = 1 : XX]
• Year -1: [Direct : Indirect = 1 : XX]
• Current: [Direct : Indirect = 1 : XX]
• Strategic Implication: e.g., "Shift toward direct sales has enhanced pricing control and customer intimacy but increased SG&A".

Mode: revenue-composition-and-concentration (1_3)

Display name: Revenue Composition & Concentration Risk Analysis

Objective: Decompose revenue by product line / customer type / geography / end market, identify concentration risk (any single product or client >20% of total revenue), and trace temporal mix evolution.

Output structure:

• Latest Quarter Revenue Breakdown:
• By Product Line: top 3 contributors with %
• By User Type (B2B vs. B2C): mix with %
• By Geography (NA / EMEA / APAC): top 3 regions with %
• By End Market: top 3 markets with %
• Concentration Risk Matrix: any product/client >20% flagged.
• Temporal Comparison: vs. prior quarter and YoY same-period.

Mode: market-sizing-and-relative-growth (1_4)

Display name: Market Sizing (TAM/SAM/SOM) & Relative Growth

Objective: Quantify TAM, SAM, SOM for key verticals, project 3-5 year CAGR, assess relative growth (company vs. addressable market) and historical SOM evolution.

Output structure:

• Market Sizing & Growth:
• TAM (Current Year): USD XXX bn
• SAM (Current Year): USD XXX bn
• SOM (Current Year): XX%
• TAM CAGR (Past 3 Years): XX%
• TAM CAGR (Forward 3-5 Years): XX%
• Relative Growth Table: Company Revenue Growth vs. TAM CAGR for past 3Y and next 3Y.
• Historical SOM Trajectory: 3-year evolution with execution-strength assessment.

Mode: management-and-leadership (1_5)

Display name: Management Team & Leadership Analysis

Objective: Assess executive team backgrounds, track records, and recent leadership changes (CEO/CFO/COO/CMO) for strategic-execution implications.

Output structure:

• Key Executives & Track Record: [Name, Role, Tenure, Notable Prior Experience, Industry Expertise, Capital Allocation Record]
• Recent Management Changes (Trailing 1-2 Quarters): [Name, Role, Effective Date, Reason, Successor Background]
• Strategic Implications of Changes: e.g., "New CFO brings strong M&A background, suggesting a shift toward inorganic growth".

Tool Fallbacks

Tool errors are retried ONCE with the fallback action before escalating to the retrieval gaps failure policy. If both Layer 2 and Layer 3 tools are unavailable, enter document access degradation mode (structured data + metadata only, flag output as degraded).

Output File

Write the final deliverable to {{ticker}}/{{YYYY-MM-DD_HHMM}}_business-model_product-line-decomp.md .

Output Structure

The final deliverable MUST be written as a markdown file to the workspace using the convention :

{ticker}/{YYYY-MM-DD_HHMM}_business-model_{affix}.md

Where affix is a short descriptive slug (e.g., product-line-decomp, channel-mix, market-sizing, management-changes). Examples:

• LLY/2026-05-25_1430_business-model_product-line-decomp.md
• NVDA/2026-05-25_1545_business-model_platform-classification.md

The deliverable file MUST contain (in order):

1. Executive Summary (≤200 words) — business-model classification + headline structural insights.
2. Business Model Type (mode 1_1) — Product / Service / Platform classification with rationale.
3. Product Line Decomposition (mode 1_3) — revenue breakdown by product, top-3 contributors, concentration risk.
4. Distribution Channel Analysis (mode 1_2) — direct vs. indirect mix, partners, 3-year evolution.
5. Customer Segment Analysis (mode 1_3) — B2B vs. B2C, geography, end markets, concentration risk.
6. Revenue Model — recurring vs. one-time, pricing power, unit economics, gross-margin profile.
7. Business Unit Performance — segment-level P&L where available (XBRL or narrative).
8. Market Sizing & Competitive Positioning (mode 1_4) — TAM/SAM/SOM, relative growth vs. market.
9. Management & Leadership (mode 1_5) — executive team, recent changes, strategic implications.
10. Coverage Gaps & Citations — list of dimensions not retrievable + full citation index in {ticker} {citation_id} page<N> format.

Citation density: ≥1 citation per 200 words. Bare page_no integers are forbidden — always use {ticker} {citation_id} page<N>. **Citation link format **: use clickable links: [📄 {ticker} {form_type} p.{N}](https://agentii.ai/v/{ticker}/{citation_id}/{N}). Example: [📄 LLY 10-K p.42](https://agentii.ai/v/LLY/sec175/42).

**agentii.md append **: After writing the output file, append a YAML block to agentii.md at the workspace root with ticker, date, skill, output_file, and key_conclusions. Create the file with a # Project Memory Index heading if it doesn't exist. See contracts/agentii-md-schema.md.

Error Handling