Med Market Sizing logo

Med Market Sizing

Community
agentii-ai
med-market-sizing

Patient-flow market sizing for med products — prevalence → diagnosed → interested → affordable → covered → treated → persistent, priced as price × duration per cohort, with capacity constraints, named historical analog anchoring, and always bull/base/bear named-driver grids. Use to size a therapy area or product market before any valuation or share work.

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill namemed-market-sizing
Stars
207
Forks
16
Bundled files
2
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.

  • 2 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 Med Market Sizing 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/bio-pharm/skills/agentii/med-market-sizing
  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/bio-pharm/skills/agentii/med-market-sizing .claude/skills/med-market-sizing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Med Market Sizing 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 Med Market Sizing 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 Med Market Sizing 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.

Methodology inspired by publicly taught patient-flow sizing approaches; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
funnel_stages7prevalence → diagnosed → interested → affordable → covered → treated → persistent
affordability_rule~10% monthly budgetIncome-share rule per cohort and country
scenario_gridbull/base/bearNever a single point number
analog_anchorsnamedStatins for penetration, tech S-curves for adoption
capacity_stateexplicitSupply-constrained vs demand-limited + flip condition

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). Confirm ticker resolution via search_companies before market queries.

Triggers

  • "What is the TAM for [therapy area]?"
  • "Size the market for [drug/device] in [indication]."
  • "What has to be true for the bear case in [class]?"
  • "Walk me through the patient funnel for [indication]."
  • "Is [class] supply-constrained or demand-limited?"
  • "What capacity ceiling do [company]'s plants impose?"
  • "Anchor [class] adoption to a historical analog."
  • "Bull/base/bear sizing for [indication] with drivers."
  • "How does the income-share rule cap affordability?"
  • "What penetration rate is realistic for [class]?"
  • "How much API does [class] need per patient-year?"

Production Grounding

  • Funnel: prevalence → diagnosed → interested → affordable (income-share rule, ~10% of monthly budget) → covered → treated → persistent; every stage carries an explicit rate or a coverage_gap. Price as price × duration per cohort and country; never skip a stage silently.
  • Capacity check is mandatory: plants × units/yr, API kg/patient/yr, fill-finish lead times. State whether the market is supply-constrained or demand-limited and the condition that flips it.
  • Analog anchoring: named historical analogs (statins for penetration, tech S-curves for adoption); every anchor carries its citation.
  • Scenario discipline: bull/base/bear with a named-driver assumption grid; uncertainty at market level, POS at asset level; model-vs-consensus delta always explicit; abstain where unquantifiable.
  • Badges: [FACT]/[DEDUCTED]/[VIEW] with the summary table.

Data Source Priority

  1. Prevalence/epidemiology: search_documents (epidemiology sources) + read_source_outline / read_source_pages.
  2. Universe + mechanism: search_universe_drugs / search_universe_devices; search_drug_knowledge / search_drugs_by_indication (spec 054 silver).
  3. Pricing/coverage: search_sec_filings (net price, GTN) + payer coverage via search_documents.
  4. Capacity: filings and transcripts (plants, API supply, fill-finish).
  5. Knowledge: search_investment_strategies / search_investment_cases (sectors=med) for sizing frameworks and prior TAM cases.

Methodology

Retrieval Scope

unstructured_document_search

Retrieval Strategy

  1. Resolve indication and product scope via search_companies + search_universe_drugs / search_universe_devices.
  2. Collect prevalence/diagnosis rates from epidemiology documents; cite each stage rate.
  3. Pull pricing (net price, GTN) and coverage evidence from filings and payer documents.
  4. Pull capacity evidence (plants, API supply, fill-finish) from filings/transcripts.
  5. Anchor penetration to named analogs; retrieve prior TAM cases (sectors=med); cite /v/ records.
  6. Build the bull/base/bear grid.

Temporal Scope

See frontmatter temporal_scope block. Ramp and capacity paths may extend to max_quarters.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

  1. Indication framing
  2. Funnel build (stage rates cited or coverage_gap)
  3. Cohort pricing (price × duration)
  4. Capacity check + constraint state
  5. Analog anchoring
  6. Bull/base/bear grid

Modes

  • Drug / biologic (default): prevalence-based funnel with payer/coverage stages.
  • Device: placements/procedures and installed-base stages replace script stages.
  • Vaccine: healthy-population cohorts, ACIP-gated uptake, procurement value.
  • Single indication: one indication × country with full cohort splits.

Tool Fallbacks

FailureFallback
Prevalence source absentcoverage_gap at the stage; list required inputs — never fabricate a rate
No pricing disclosureBracket net price from filings; label [DEDUCTED] / [VIEW]
Drug absent from silver layerUniverse + filings; annotate knowledge coverage_gap
Capacity undisclosedState the flip-condition logic qualitatively [VIEW]

Output File

{ticker}/{YYYY-MM-DD_HHMM}_med-market-sizing_{affix}.md (market-level: _sector/{YYYY-MM-DD_HHMM}_med-market-sizing_{affix}.md)

Output Structure

  1. Executive Summary — TAM stance + constraint state, 2-3 sentences
  2. Patient-Flow Funnel — stage-by-stage rates, each cited or coverage_gap
  3. Cohort Pricing — price × duration per cohort/country
  4. Capacity Check — ceiling + supply-constrained vs demand-limited + flip condition
  5. Analog Anchors — named penetration/adoption analogs with citations
  6. Scenario Grid — bull/base/bear with named-driver assumptions
  7. Coverage Gaps — missing sources and degraded stages

Error Handling

ErrorFallback
Missing prevalence datacoverage_gap at the stage; state the required input — never fabricate a rate
Conflicting prevalence sourcesShow both with provenance; use the range in the scenario grid
No consensus comparatorState model vs consensus as unavailable; keep the bull/base/bear internal consistency

Memory Load

See contracts/memory-load.md.

Snapshot

See contracts/snapshot-synthesis.md.

Final Summary (TUI)

Include ### Key Citations block with 0-10 clickable /v/ URLs.

References

  • contracts/citation-and-memory.md
  • contracts/output-frontmatter-schema.md
  • contracts/memory-load.md
  • contracts/snapshot-synthesis.md
  • contracts/preflight.md
  • references/knowledge-frameworks.md

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 Med Market Sizing AI skill do?

Patient-flow market sizing for med products — prevalence → diagnosed → interested → affordable → covered → treated → persistent, priced as price × duration per cohort, with capacity constraints, named historical analog anchoring, and always bull/base/bear named-driver grids. Use to size a therapy area or product market before any valuation or share work.

Why use Med Market Sizing on TypingMind?

Because you install it once and use it with any model. Med Market Sizing 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 Med Market Sizing 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/bio-pharm/skills/agentii/med-market-sizing. 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 Med Market Sizing?

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 Med Market Sizing?

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

Is the Med Market Sizing 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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