Update to Data

{
  "name": "market-sizing",
  "description": "Estimate market, segment, or opportunity size with transparent assumptions and uncertainty. Use for TAM/SAM/SOM, sizing scenarios, or comparing the scale of possible opportunities.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 300
    }
  ],
  "skill_md_contents": "---\nname: market-sizing\ndescription: \"Estimate market, segment, or opportunity size with transparent assumptions and uncertainty. Use for TAM/SAM/SOM, sizing scenarios, or comparing the scale of possible opportunities.\"\n---\n\n# Market Sizing\n\nUse this skill to produce a defensible estimate of a market or opportunity from connected context, public sources, transparent assumptions, and auditable calculations. The job is to define the market, choose a sound sizing method, distinguish evidence from assumptions, test sensitivity, and state what would most improve confidence.\n\n## Overall Instructions\n\n- Follow the [shared Data instructions](../../shared/shared-skill-instructions.md) throughout this workflow.\n\n## Dependencies\n\nApply the shared [dependency resolution policy](../../shared/shared-skill-instructions.md#dependency-resolution) to the categories below.\n\n- Knowledge & Files: Market research, source publications, assumptions, and supplied sizing models.\n- Data Warehouse: Company-specific customer, revenue, adoption, and segment inputs.\n- Business Intelligence: Governed segment reports and existing market or business benchmarks.\n- Product Analytics: Usage and adoption evidence for estimating reachable segments and opportunity.\n\n## Related Skills\n\nPass chart-ready evidence to the selected response mode.\n\n## Skill Configuration\n\n### Source Discovery And Verification\n\nUse the relevant data context as a starting map, not a boundary.\n\n1. **Find the authoritative evidence.** Follow references from discussions and summaries to the original metric, query, reporting view, or source artifact. Inspect relevant schemas, datasets, tables, views, models, and metrics when source discovery is needed. Known sources and semantic mappings are starting points; expand the search when stronger or complementary evidence could materially change the answer.\n2. **Compare duplicates and conflicts.** When sources overlap or disagree, compare ownership, freshness, definition, grain, coverage, and directness. Use the best authoritative source, or combine complementary sources when needed. Note material conflicts, explain why the selected sources control the answer, and verify the data through source reads or the explicitly supplied evidence.\n\n### Source Access Guardrail\n\nApply the shared [dependency resolution policy](../../shared/shared-skill-instructions.md#dependency-resolution) to identify required evidence, offer missing integrations, and continue supported work. Pause only claims or actions that depend on unavailable evidence; do not treat weaker substitutes as equivalent.\n\nClarify with the user when a missing input would materially change the estimate or recommendation. Otherwise make a reasonable assumption, state it, and proceed.\n\n## Workflow\n\n### 1. Frame The Market Or Opportunity\n\nDefine the market or opportunity boundary before estimating:\n\n- What is being sized, for example a product category, workflow, problem, use case, or category of activity.\n- Where and when it applies, for example geography, segment scope, time horizon, or market maturity.\n- Who or what counts as part of the market, for example the relevant population, unit of demand, transaction type, or included activity.\n- How the opportunity is measured, for example spend, revenue, volume, value created, or another unit that fits the question.\n- What kind of sizing answer the user needs, for example TAM/SAM/SOM, market entry, expansion upside, spend pool, revenue pool, population count, or unit volume.\n\n### 2. Choose A Starting Sizing Approach And Inputs\n\nPick the simplest sound sizing approach for the question, then sketch the calculation chain and the major inputs the estimate will depend on.\n\nA top-down model works when reliable aggregate market data exists; a bottom-up model works when the market can be built from observable units and assumptions; a value-based model works when the estimate should start from the value created rather than a published market total. Use a mixed approach only when cross-checking would materially improve confidence. If more than one approach fits, briefly explain which one you trust most and why.\n\nExpect the first approach to change if source checks show that another model would be more defensible.\n\n### 3. Gather Sources For The Inputs\n\nChoose sources based on the inputs the estimate depends on most.\n\nStart with user-named sources when provided. Then use the strongest available evidence for each major input from the starting approach. Use `~~structured_data` when an input should come from the user's data warehouse or another structured data source. Use context lanes such as `~~company_docs`, `~~team_communication`, or `~~dashboards_or_bi` when an input needs business meaning, source-of-truth guidance, or assumptions that are not captured in structured data alone. When an input depends on the outside market, use public sources for benchmarks, population estimates, comparable markets, or proxy assumptions.\n\nUse $gather-business-context to resolve context lanes when the right source of truth, business meaning, or assumption set is unclear.\n\nIf the strongest source is unavailable or thin, continue with a transparent proxy assumption only when the estimate is still useful. Label the gap and explain how it affects confidence.\n\n### 4. Separate Facts From Assumptions\n\nKeep sourced facts, inferred estimates, and judgment calls distinct in the model. When exact data is unavailable, use a defensible proxy, explain why it is reasonable, and note the confidence level. Ground assumptions in evidence about how the market actually behaves, what can realistically change, and what determines the size of the opportunity.\n\n### 5. Build The Model\n\nMake the model easy to inspect and adjust.\n\nThe model should make these elements easy to audit or revise:\n\n- market definition and measurement unit\n- assumptions and source context\n- calculation chain and derived values\n- base case, material ranges, and sensitivity logic\n- validation priorities\n\nFor each major input, make the source path visible: structured data, context lane, public source, user-provided input, or proxy assumption.\n\nKeep derived values traceable to formulas or code rather than hardcoded outputs.\n\nUse $jupyter-notebooks when code is needed for source harmonization, calculations, sensitivity analysis, or reusable modeling logic. Keep formulas, inputs, intermediate calculations, and sensitivity logic inspectable.\n\nUse the `$Spreadsheets` skill when the user requests a spreadsheet, workbook, or Google Sheets deliverable, or when a market-sizing model would materially benefit from editable assumptions, sensitivity tables, charts, or polished workbook formatting.\n\n### 6. Test Sensitivity\n\nIdentify the assumptions that move the estimate most.\n\nShow how the estimate changes when those assumptions move up or down. Prefer simple, decision-useful sensitivity analysis over exhaustive scenario sprawl.\n\nUse ranges when uncertainty is material. Do not hide uncertainty behind a single point estimate when the inputs are thin.\n\n### 7. State The Estimate And Validation Priorities\n\nReturn the estimate, method, key assumptions, uncertainty, and next validation priorities using the response mode selected by the Data index.\n\nFor source-backed Desktop inline answers outside Work Mode, include the [Sources receipt](../visualize-data/references/inline-sources-receipt.md), even when no chart is needed.\n\nBefore handoff, make the market-sizing conclusion explicit:\n\n- market definition and measurement unit\n- estimate or range\n- method and calculation chain\n- key assumptions and source support\n- main uncertainty drivers and sensitivity takeaways\n- validation priorities and practical interpretation for the user's decision\n\nIf source coverage is thin, say which major inputs rely on proxy assumptions and what source would most improve them.\n\nBefore sharing, apply the shared [analysis quality criteria](../../shared/analysis-quality.md) to methodology, calculations, assumptions, caveats, and source support within this workflow.\n\nPass sensitivity, scenario, funnel, or market-breakdown visual intent and supporting evidence to the selected response mode.\n"
}