Mmcp.market

market-research-reports skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Build evidence-traceable market research reports and assumption-driven market sizing or forecast scenarios. Use for market definition, industry and customer evidence, competitive landscapes, TAM/SAM/SOM reconciliation, forecast sensitivity, and auditable report scaffolds.

A100/100content scan

Is the market-research-reports skill safe?

Clean: nothing in its files matched our rules. We read 24 files in the folder on 2026-09-28.

No findings.

Install the market-research-reports skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/market-research-reports ~/.claude/skills/market-research-reports
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

measure, unit, denominator, currency/base year, and taxonomy.

  1. Define before sizing. Fix product, customer, geography, channel, period,

exact source IDs.

  1. Map every claim. Every factual or quantitative claim has a claim ID and

forecasts, opinions, and recommendations.

  1. Separate statement types. Distinguish facts, estimates, calculations,

filed company disclosures, and transparent original studies before secondary synthesis.

  1. Prefer primary evidence. Use official statistics, regulator records,

ranges, sensitivity, and limitations.

  1. Preserve uncertainty. Retain source conflicts, revisions, scenario

calculations when practical.

  1. Keep methods reproducible. Use local structured inputs and deterministic

circumvention, confidential material, or trade-secret acquisition.

  1. Collect lawfully and ethically. No deception, PII disclosure, access

Workflow

1. Establish the research contract

Clarify:

another measure;

  • 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
  • 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/reportstructureguide.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/officialdatasources.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/sourceledgertemplate.csv and validate it:

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:

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 coveragekey and one shared denominatorid. 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:

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:

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:

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.

More skills from K-Dense-AI/scientific-agent-skills

  • AadaptyvHow to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
  • AaeonThis skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
  • AalphagenomeLook up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.
  • Aanalytical-method-validationPlan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
  • AanndataData structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
  • AarborAutonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
  • AarboretoInfer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
  • AastropyCore Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
  • AautoskillObserve the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
  • Abenchling-integrationBenchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
  • Abgpt-paper-searchSearch scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
  • AbidsUse this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.

All agent skills → · MCP servers