# Market Research Reports

> Crea informes de investigación de mercado con evidencia trazable y escenarios de tamaño de mercado o previsión basados en supuestos: definición de mercado, TAM/SAM/SOM, panoramas competitivos y sensibilidad.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/market-research-reports
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/market-research-reports.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: MIT
Actualizado: hace 28 días
Coste de contexto: 68 tok instalada, 3.3k tok al activarse, 46.5k tok con todos los archivos del bundle
Bundle: 26 archivos, 182 KB
Permisos que pide: ninguno declarado

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill market-research-reports --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill market-research-reports --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill market-research-reports --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill market-research-reports --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill market-research-reports --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill market-research-reports --agent cline
```

## Qué hace

- Construye informes de mercado donde cada afirmación tiene un ID de fuente auditable (source ledger + claims ledger)
- Calcula TAM/SAM/SOM top-down y bottom-up por separado y concilia la diferencia entre ambos métodos
- Genera escenarios de previsión (downside/base/upside) con sensibilidad y supuestos explícitos
- Analiza competidores y concentración (HHI/CRn) con una matriz validada de evidencia lícita
- Valida ledgers de evidencia, matrices de competidores y consistencia de unidades con scripts locales en Python

## Cuándo usarla

- Definir el mercado y calcular TAM/SAM/SOM con evidencia trazable
- Crear un panorama competitivo o matriz de competidores auditable
- Construir escenarios de previsión con sensibilidad e incertidumbre explícita
- Redactar un informe de investigación de mercado con estructura auditable

## Cuándo no

- Para imitar o insinuar afiliación con una consultora, analista o marca de investigación
- Para dar asesoramiento de inversión, legal, antimonopolio, fiscal o regulatorio

## Qué la activa

- "Calcula el TAM/SAM/SOM de este mercado con escenarios"
- "Crea la matriz de competidores validada con fuentes"
- "Genera el andamiaje de mi informe de mercado en markdown"
- "Revisa la sensibilidad de mi previsión de mercado"

## Antes de instalar

- Usa Python 3.11+ estándar para los scripts opcionales; la plantilla LaTeX opcional necesita XeLaTeX o LuaLaTeX, y la investigación online requiere acceso a red aprobado por el usuario.
- Necesita en el PATH: python3

## Archivos

- SKILL.md — 13 KB
- assets/FORMATTING_GUIDE.md — 5 KB
- assets/claims_ledger_template.csv — 957 B
- assets/competitor_feature_matrix_template.csv — 683 B
- assets/consistency_check_template.csv — 432 B
- assets/forecast_sensitivity_template.json — 2 KB
- assets/market_report_template.tex — 10 KB
- assets/market_research.sty — 6 KB
- assets/market_sizing_scenarios_template.json — 3 KB
- assets/report_manifest_template.json — 882 B
- assets/source_ledger_template.csv — 1 KB
- references/data_analysis_patterns.md — 10 KB
- references/evidence_model.md — 6 KB
- references/methods_and_ethics.md — 7 KB
- references/official_data_sources.md — 10 KB
- references/report_structure_guide.md — 8 KB
- references/sources.md — 9 KB
- references/visual_generation_guide.md — 5 KB
- scripts/_common.py — 11 KB
- scripts/audit_claim_citations.py — 11 KB
- scripts/calculate_market_sizing.py — 13 KB
- scripts/check_unit_consistency.py — 7 KB
- scripts/forecast_sensitivity.py — 11 KB
- scripts/generate_report_scaffold.py — 14 KB
- scripts/validate_competitor_matrix.py — 8 KB
- scripts/validate_evidence_ledger.py — 9 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo MIT. Esta sección es el documento original y está en inglés.

# Market Research Reports

## Purpose

Create decision-focused market reports whose claims, calculations, assumptions,
and uncertainties can be audited. Match depth and format to the question and
evidence. There is no required length, chapter count, visual count, or output
format.

Do not:

- imitate or imply affiliation with a consulting, analyst, or research brand;
- invent citations, quotes, market shares, or paid-market figures;
- present TAM/SAM/SOM or a forecast as one certain truth;
- treat a framework, chart, or fluent narrative as evidence;
- provide investment, legal, antitrust, tax, accounting, or regulatory advice.

## Operating principles

1. **Define before sizing.** Fix product, customer, geography, channel, period,
   measure, unit, denominator, currency/base year, and taxonomy.
2. **Map every claim.** Every factual or quantitative claim has a claim ID and
   exact source IDs.
3. **Separate statement types.** Distinguish facts, estimates, calculations,
   forecasts, opinions, and recommendations.
4. **Prefer primary evidence.** Use official statistics, regulator records,
   filed company disclosures, and transparent original studies before
   secondary synthesis.
5. **Preserve uncertainty.** Retain source conflicts, revisions, scenario
   ranges, sensitivity, and limitations.
6. **Keep methods reproducible.** Use local structured inputs and deterministic
   calculations when practical.
7. **Collect lawfully and ethically.** No deception, PII disclosure, access
   circumvention, confidential material, or trade-secret acquisition.

## Workflow

### 1. Establish the research contract

Clarify:

- decision, audience, deadline, and materiality threshold;
- formal market definition and adjacent exclusions;
- buyer, payer, user, transaction, and value-chain level;
- geography and treatment of imports, exports, and channels;
- historical period, forecast period, and retrieval cutoff;
- revenue/expenditure, gross output/value added, units, capacity, users, or
  another measure;
- stock/flow, gross/net, taxes, and denominator;
- currency, base year, and nominal/real/current/constant basis;
- industry and product classification with version;
- permitted data sources, primary research, confidentiality, and output format.

Ask a focused question when a missing choice would materially change the
denominator or result. Otherwise state a provisional scope and proceed.

Use `references/report_structure_guide.md` for modular report design.

### 2. Build the evidence plan

Route each question to the source closest to the underlying event:

1. primary law, regulator decision, official filing, or official statistic;
2. original company filing or attributable first-party disclosure;
3. transparent survey/study with inspectable methods;
4. institutional or peer-reviewed research using identifiable primary data;
5. industry association data with disclosed coverage;
6. reputable secondary synthesis;
7. lawfully accessed paid estimate with inspectable scope and method;
8. news/commentary for leads or attributable events.

For company data, prefer the official filing system in the relevant
jurisdiction. For industry, labor, prices, population, trade, and national
accounts, prefer the responsible national statistical agency or central bank.
For cross-country work, use harmonized World Bank, IMF, OECD, or Eurostat data
only after checking definitions and original-source lineage.

Read `references/official_data_sources.md` before using public APIs. API rules
and limits are a dated snapshot: verify current official terms before automated
or high-volume retrieval. Never put an API key in a report or bundled script.

### 3. Create the source ledger

Assign stable IDs (`S-001`, `S-002`, ...). Record:

- title, publisher, URL/persistent ID, source type;
- publication date and retrieval date;
- original producer when accessed through an aggregator;
- geography, covered population, period, and vintage;
- currency, base year, price basis, measure type, unit, and denominator;
- taxonomy and version;
- preliminary/revised/final/current status;
- method, sample, imputation, suppression, and limitations;
- license/terms and lawful local snapshot path.

Use `assets/source_ledger_template.csv` and validate it:

```bash
python3 scripts/validate_evidence_ledger.py data/source_ledger.csv
```

If publication date is unavailable, record `not-stated`; do not guess.

### 4. Maintain a claims ledger

Assign IDs (`C-001`, ...). Keep the exact claim text, statement type, source
IDs, report location, as-of date, geography, currency/base, measure/unit,
taxonomy, revision status, confidence, calculation ID, and assumption IDs.

Rules:

- one end-of-paragraph citation does not support unrelated sentences;
- split compound claims that rely on different evidence;
- a calculation cites its inputs, not a source that never published the result;
- an aggregator and its original source are not independent corroboration;
- an interview theme is not population prevalence;
- absence of public feature evidence means `unknown`, not `no`.

Audit mappings:

```bash
python3 scripts/audit_claim_citations.py \
  data/claims.csv data/source_ledger.csv
```

See `references/evidence_model.md`.

### 5. Size the market as scenarios

#### Measurement guardrails

Give every component a disjoint `coverage_key` and one shared
`denominator_id`. Do not add:

- manufacturer revenue to distributor or end-customer spend;
- production, imports, and sales without trade/inventory reconciliation;
- parent and subsidiary revenue;
- bundles and their included components;
- gross output and value added;
- installed-base stock and annual transaction flow;
- overlapping customer or geographic segments.

Use product classifications and supply-use logic when industry codes are too
broad. Preserve an unknown/residual category instead of forcing totals.

#### Top-down and bottom-up

Compute independently:

```text
TAM_top = sum(disjoint in-scope component values)

TAM_bottom =
  sum(customer_count
      * addressable_fraction
      * annual_quantity_per_customer
      * price_per_unit)
```

Then apply scenario-specific serviceability and capture assumptions:

```text
SAM_s = TAM * serviceable_fraction_s
SOM_s = SAM_s * obtainable_share_s
```

Use at least two genuinely different scenarios; a downside/base/upside set is
usually useful. State horizon, constraints, evidence, and assumptions. SOM is
not a guaranteed revenue forecast.

Run the deterministic calculator:

```bash
python3 scripts/calculate_market_sizing.py \
  assets/market_sizing_scenarios_template.json
```

Report both methods, midpoint-relative gap, scope differences, sensitivity, and
unresolved reconciliation. Do not average incompatible methods.

### 6. Forecast with explicit uncertainty

Separate observed, estimated, and forecast periods. Record series ID,
frequency, units, seasonal adjustment, transformations, taxonomy breaks,
retrieval date, and vintage/revisions.

For each scenario:

- provide an annual rate path or driver equations;
- state demand, price, supply, regulation, competition, capacity, and timing
  assumptions;
- list evidence and assumption IDs;
- identify conditions that invalidate the scenario.

Do not call scenario bounds confidence or prediction intervals. Do not assign
probabilities without a validated probabilistic model and diagnostics.

Run:

```bash
python3 scripts/forecast_sensitivity.py \
  assets/forecast_sensitivity_template.json
```

Show the range by year, endpoint sensitivity, influential assumptions, and
switching values. See `references/data_analysis_patterns.md`.

### 7. Analyze customers and primary research

For survey evidence, disclose sponsor, target population, frame,
probability/non-probability design, recruitment, mode/language, field dates,
unweighted sample, subgroup bases, weighting, response/participation,
instrument wording, precision, processing, and limitations.

For interviews/focus groups, disclose recruitment, consent, role coverage,
dates/mode, guide, coding, divergent evidence, privacy controls, and limits to
generalization.

Never:

- collect more personal data than necessary;
- place direct identifiers or raw recordings in report artifacts;
- use research as disguised selling or lead generation;
- misrepresent identity/purpose;
- pressure participants to reveal employer/customer secrets;
- report qualitative mention counts as market prevalence.

Follow `references/methods_and_ethics.md`.

### 8. Analyze competitors and concentration

Define product and geographic scope from the customer perspective before
selecting competitors or calculating shares. Consider non-price dimensions,
channels, imports, digital/multi-sided features, innovation, and dynamic change
where relevant.

Use lawful public evidence and a common product edition, geography, and as-of
date. Validate a complete matrix:

```bash
python3 scripts/validate_competitor_matrix.py \
  assets/competitor_feature_matrix_template.csv \
  --source-ledger assets/source_ledger_template.csv
```

For shares, state revenue/units/capacity/users or other metric, denominator,
period, residual share, and source coverage. HHI/CRn are descriptive screens,
not legal conclusions. A TAM category is not automatically a relevant antitrust
market.

### 9. Normalize units and definitions

Before combining values:

- align geography, period, stock/flow, gross/net, unit, and denominator;
- convert currencies with an identified source and rate convention;
- align base year and nominal/real basis;
- do not force chained-dollar additivity;
- preserve taxonomy versions and document concordance uncertainty;
- record every conversion as a calculation.

Check comparison groups:

```bash
python3 scripts/check_unit_consistency.py \
  assets/consistency_check_template.csv
```

### 10. Draft and review

Lead with findings and uncertainty, not frameworks. Use optional frameworks
only to organize questions; do not force scores or a fixed number of factors.
Keep recommendations separate from evidence and include dependencies,
trade-offs, decision thresholds, and disconfirming evidence.

Visuals are optional. If used, build them from validated local data and include
scope, units, source IDs, calculation ID, observed/forecast distinction, and
limitations. See `references/visual_generation_guide.md`.

Generate a Markdown workspace:

```bash
python3 scripts/generate_report_scaffold.py \
  assets/report_manifest_template.json ./market-report-workspace
```

Or use the optional LaTeX assets:

- `assets/market_report_template.tex`
- `assets/market_research.sty`
- `assets/FORMATTING_GUIDE.md`

## Release gate

- Market boundary, taxonomy, denominator, geography, and period are explicit.
- Every factual/quantitative claim maps to exact source IDs.
- Publication/retrieval dates, revisions, method, and limitations are recorded.
- Currency/base year, nominal/real basis, stock/flow, and units are consistent.
- Top-down and bottom-up methods use disjoint coverage and are reconciled.
- TAM/SAM/SOM and forecasts are conditional scenarios with sensitivity.
- Survey/interview evidence carries method, privacy, and inference limits.
- Competitor evidence is lawful, dated, scoped, and uses `unknown` honestly.
- Source conflicts and revisions remain visible.
- No fabricated/unsupported paid figures, PII, trade secrets, deceptive
  collection, brand impersonation, or investment-advice framing appears.

## Bundled resources

### References

- `references/report_structure_guide.md` — modular report architecture.
- `references/evidence_model.md` — claim-source mapping and provenance.
- `references/data_analysis_patterns.md` — sizing, forecast, consistency,
  survey, and concentration methods.
- `references/official_data_sources.md` — current official source/API routing.
- `references/methods_and_ethics.md` — survey, interview, privacy, competitor,
  and antitrust safeguards.
- `references/visual_generation_guide.md` — optional evidence-led displays.
- `references/sources.md` — dated authoritative source ledger.

### Templates and CLIs

Use the templates in `assets/` as synthetic schemas, not real-world evidence.
All scripts in `scripts/` are standard-library, bounded, local-only tools. They
reject oversized or malformed input, do not follow symlink inputs, do not
overwrite outputs without explicit permission, and make no network, LLM, image,
dynamic-evaluation, or pickle calls.

## Dónde encaja

- Categoría: [Investigación](https://skillsagentes.com/categorias/investigacion.md) — Investigación estructurada, búsqueda de fuentes y síntesis.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.
- [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.
- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

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