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Business Model

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agentii-ai
business-model

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

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill namebusiness-model
Stars
207
Forks
16
Bundled files
7
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by agentii-ai on GitHub. Read the source before you install it.

Installation

Install the Business Model AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/marketplace-beachhead/plugins/equity-research-core/skills/agentii/business-model
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git /tmp/agentii-investment-intelligence
mkdir -p .claude/skills
cp -r /tmp/agentii-investment-intelligence/marketplace-beachhead/plugins/equity-research-core/skills/agentii/business-model .claude/skills/business-model
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Business Model in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Business Model on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Business Model is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Preflight

Run canonical pre-flight per contracts/preflight.md.

Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md — carry the _run_id from your first tool result and name yourself (and your parent, if you were spawned).

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

ParameterDefaultNotes
lookback_quarters4Trailing 4 fiscal quarters (latest 10-K + trailing 10-Qs); a single-quarter snapshot is INSUFFICIENT for business-model classification
include_management_changestrueWhether to surface leadership-change analysis (mode 1_5)
include_market_sizingtrueWhether to surface TAM/SAM/SOM (mode 1_4)
peer_setnoneBusiness-model analysis is single-issuer by default; peers are added by /agentii:competitive

Production Grounding

Production data-plane grounding (scale, locators) is in references/methodology.md.

Data Source Priority (mandatory order)

  1. XBRL FIRST (grounding truth) — search_xbrl_facts with the detailed/segment view for revenue & margins broken down by product line, segment, geography, and distribution channel (concepts Revenues, GrossProfit, OperatingIncomeLoss, SegmentReportingInformation). Retrieve XBRL BEFORE any document discovery.
  2. SEC filings — search_documents / search_sec_filings for the 10-K (annual, richest business overview), trailing 10-Q, and 20-F for foreign issuers → read_source_pages enrichment.
  3. Web search = LAST RESORT — only when SEC/XBRL coverage is genuinely insufficient (e.g., third-party TAM estimates, very recent unfiled events), and it MUST be flagged in Coverage Gaps. Web search is NOT an MCP tool and is never a substitute for filings.

Methodology

1. Retrieval Scope

This skill performs unstructured document search at scale (10-K, 10-Q, 8-K filings and earnings call transcripts). 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

See contracts/retrieval.md for the canonical decision tree; skill-specific retrieval detail is in references/methodology.md.

Read before you quote. A page's description is platform-generated — it is never the issuer's words and must never be quoted as the filing's. When a description identifies a relevant page, read_source_pages that page and quote its page_content. The description is a pointer, not a source: a citation written from one is the fabricated quote this skill's output standard exists to prevent (contracts/retrieval.md § Three-Layer Document Protocol).

3. Temporal Scope

Default: 4 fiscal quarters (max 8). A single-quarter snapshot is INSUFFICIENT for business-model classification — use the latest 10-K (annual) plus trailing 10-Qs. Extend the window 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.

  • 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

Step-by-step execution detail is in references/methodology.md.

Moat Assessment: When evaluating competitive advantage durability, apply the Sustainable Value Creation framework in references/moat-methodology.md. Quantify the ROIC−WACC spread magnitude, calibrate sustainability using sector-level ROIC autocorrelation data (Consumer Staples r=0.46 → Energy r=0.15), classify industry structure (Fragmented/Oligopoly/Dominant/Network/Commodity), and apply the 67-item Moat Checklist. High current ROIC in a rapid-mean-reversion industry is not a moat.

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.

Analyst Modes

This skill exposes addressable analysis modes (--mode=<slug> / --modes=<s1>,<s2> / --mode=all; see Mode syntax). The full mode definitions and their output templates live in references/modes.md. The default invocation runs the essentials subset.

Tool Fallbacks

Per-tool failure modes and fallback actions are tabulated in references/tool-fallbacks.md.

Output File

Write the final deliverable to {ticker}/{YYYY-MM-DD_HHMM}_business-model_{affix}.md.

Output Directory Rule: write to {ticker}/ (e.g. NVDA/) — NEVER {ticker}-recent-quarter/ or any dimension-suffixed variant. The directory is the bare uppercase ticker; the skill name appears only in the filename, never the directory.

Output Structure

The deliverable is a structured markdown report written to the path in ## Output File. Full section-by-section template (headings, tables, and field definitions) lives in references/output-structure.md. Required elements:

  1. Executive Summary — headline conclusions (≤200 words).
  2. Core analysis sections — per this skill's methodology and analyst modes.
  3. Data classification — tag findings [FACT] / [DEDUCTED] / [VIEW] per contracts/snapshot-synthesis.md.
  4. Coverage Gaps & Citations — coverage gaps are required; inline /v/ citations are the citation surface (immediately after each fact). A bottom roll-up index is optional, and where kept it must not repeat a link the prose already carries.
  5. Output frontmatter — emit the FR-090 structured block per contracts/output-frontmatter-schema.md.

Citations & memory: follow contracts/citation-and-memory.md — ≥1 citation per 200 words; every material fact, table row, and metric is immediately followed by its inline clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link; citations belong inline, a bottom roll-up index is optional and never a repeat of a link already given; the closing TUI reply includes a compact Key Citations list (headline 5–10 facts) of clickable /v/ URLs; and append the run to agentii.md per contracts/agentii-md-schema.md.

Memory & Snapshot

  • Memory load (pre-flight): load prior workspace context for the ticker before retrieval — see contracts/memory-load.md.
  • Structured output frontmatter: emit the FR-090 block (key_metrics, conclusions, facts_count, deducted_count, views_count, citation_count) per contracts/output-frontmatter-schema.md.
  • Snapshot synthesis: after writing the deliverable, update the two-tier snapshot and classify findings as [FACT]/[DEDUCTED]/[VIEW] — see contracts/snapshot-synthesis.md.
  • Session archival: record the run under sessions/{YYYY-MM-DD}/ and update sessions/INDEX.md per contracts/session-format.md.

Final Summary (TUI)

End the closing chat reply with the summary shape in contracts/citation-and-memory.md — Citation Placement Policy item 3: title · key conclusions · key metrics · Executive Summary · Key Citations (the headline 5–10 facts, each a clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link), so the user can cmd+click straight to the exact SEC page without opening the file.

Error Handling

Failure ModeDetectionActionUser-Facing Message
Missing dataData API returns empty result setWiden date range and retry once"No data available for {ticker} in requested window."
Partial dataData API returns <80% expected recordsProceed with coverage gaps section"Analysis based on partial data; see Coverage Gaps section."
Foreign issuerform_type=["10-K"] emptyRetry with form_type=["20-F"] for annual + form_type=["6-K"] for material events"Foreign issuer; using 20-F + 6-K instead of 10-K + 8-K."
Insufficient historyTicker <3 years on public marketsDowngrade to limited-history profile (skip channel-mix evolution)"Limited historical data; mode 1_2 channel-mix trend skipped."
MCP unreachablePreflight probe failsHalt with actionable error"agentii data plane unreachable; check connection."

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Business Model AI skill do?

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

Why use Business Model on TypingMind?

Because you install it once and use it with any model. Business Model is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Business Model in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/marketplace-beachhead/plugins/equity-research-core/skills/agentii/business-model. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Business Model?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Business Model?

As many as you like. As long as a model supports skills, you can use Business Model with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Business Model AI skill free?

Yes. It is published on GitHub by agentii-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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