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Use for market definition, industry and customer evidence, competitive landscapes, TAM\u002FSAM\u002FSOM reconciliation, forecast sensitivity, and auditable report scaffolds.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1566,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"research","market research reports","market research","Market","reports","access","definition","source","lualatex","make","scripts","reconciliation","template","writing","analysis","network","library","offline","customer","image","2026-11-10 10:12:30",false,"---\nname: market-research-reports\ndescription: 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\u002FSAM\u002FSOM reconciliation, forecast sensitivity, and auditable report scaffolds.\nlicense: MIT\ncompatibility: Python 3.11+ standard library for optional offline CLIs. The optional LaTeX template uses XeLaTeX or LuaLaTeX. Online research requires user-approved network access and source-specific terms; bundled scripts make no network, LLM, or image calls.\nmetadata:\n  version: '1.3'\n  category: research-writing\n  maintainer: Kalaris Labs\n  tags: market research, market sizing, TAM SAM SOM, competitive analysis, reports\n---\n\n# Market Research Reports\n\n## Purpose\n\nCreate decision-focused market reports whose claims, calculations, assumptions,\nand uncertainties can be audited. Match depth and format to the question and\nevidence. There is no required length, chapter count, visual count, or output\nformat.\n\nDo not:\n\n- imitate or imply affiliation with a consulting, analyst, or research brand;\n- invent citations, quotes, market shares, or paid-market figures;\n- present TAM\u002FSAM\u002FSOM or a forecast as one certain truth;\n- treat a framework, chart, or fluent narrative as evidence;\n- provide investment, legal, antitrust, tax, accounting, or regulatory advice.\n\n## Operating principles\n\n1. **Define before sizing.** Fix product, customer, geography, channel, period,\n   measure, unit, denominator, currency\u002Fbase year, and taxonomy.\n2. **Map every claim.** Every factual or quantitative claim has a claim ID and\n   exact source IDs.\n3. **Separate statement types.** Distinguish facts, estimates, calculations,\n   forecasts, opinions, and recommendations.\n4. **Prefer primary evidence.** Use official statistics, regulator records,\n   filed company disclosures, and transparent original studies before\n   secondary synthesis.\n5. **Preserve uncertainty.** Retain source conflicts, revisions, scenario\n   ranges, sensitivity, and limitations.\n6. **Keep methods reproducible.** Use local structured inputs and deterministic\n   calculations when practical.\n7. **Collect lawfully and ethically.** No deception, PII disclosure, access\n   circumvention, confidential material, or trade-secret acquisition.\n\n## Workflow\n\n### 1. Establish the research contract\n\nClarify:\n\n- decision, audience, deadline, and materiality threshold;\n- formal market definition and adjacent exclusions;\n- buyer, payer, user, transaction, and value-chain level;\n- geography and treatment of imports, exports, and channels;\n- historical period, forecast period, and retrieval cutoff;\n- revenue\u002Fexpenditure, gross output\u002Fvalue added, units, capacity, users, or\n  another measure;\n- stock\u002Fflow, gross\u002Fnet, taxes, and denominator;\n- currency, base year, and nominal\u002Freal\u002Fcurrent\u002Fconstant basis;\n- industry and product classification with version;\n- permitted data sources, primary research, confidentiality, and output format.\n\nAsk a focused question when a missing choice would materially change the\ndenominator or result. Otherwise state a provisional scope and proceed.\n\nUse `references\u002Freport_structure_guide.md` for modular report design.\n\n### 2. Build the evidence plan\n\nRoute each question to the source closest to the underlying event:\n\n1. primary law, regulator decision, official filing, or official statistic;\n2. original company filing or attributable first-party disclosure;\n3. transparent survey\u002Fstudy with inspectable methods;\n4. institutional or peer-reviewed research using identifiable primary data;\n5. industry association data with disclosed coverage;\n6. reputable secondary synthesis;\n7. lawfully accessed paid estimate with inspectable scope and method;\n8. news\u002Fcommentary for leads or attributable events.\n\nFor company data, prefer the official filing system in the relevant\njurisdiction. For industry, labor, prices, population, trade, and national\naccounts, prefer the responsible national statistical agency or central bank.\nFor cross-country work, use harmonized World Bank, IMF, OECD, or Eurostat data\nonly after checking definitions and original-source lineage.\n\nRead `references\u002Fofficial_data_sources.md` before using public APIs. API rules\nand limits are a dated snapshot: verify current official terms before automated\nor high-volume retrieval. Never put an API key in a report or bundled script.\n\n### 3. Create the source ledger\n\nAssign stable IDs (`S-001`, `S-002`, ...). Record:\n\n- title, publisher, URL\u002Fpersistent ID, source type;\n- publication date and retrieval date;\n- original producer when accessed through an aggregator;\n- geography, covered population, period, and vintage;\n- currency, base year, price basis, measure type, unit, and denominator;\n- taxonomy and version;\n- preliminary\u002Frevised\u002Ffinal\u002Fcurrent status;\n- method, sample, imputation, suppression, and limitations;\n- license\u002Fterms and lawful local snapshot path.\n\nUse `assets\u002Fsource_ledger_template.csv` and validate it:\n\n```bash\npython3 scripts\u002Fvalidate_evidence_ledger.py data\u002Fsource_ledger.csv\n```\n\nIf publication date is unavailable, record `not-stated`; do not guess.\n\n### 4. Maintain a claims ledger\n\nAssign IDs (`C-001`, ...). Keep the exact claim text, statement type, source\nIDs, report location, as-of date, geography, currency\u002Fbase, measure\u002Funit,\ntaxonomy, revision status, confidence, calculation ID, and assumption IDs.\n\nRules:\n\n- one end-of-paragraph citation does not support unrelated sentences;\n- split compound claims that rely on different evidence;\n- a calculation cites its inputs, not a source that never published the result;\n- an aggregator and its original source are not independent corroboration;\n- an interview theme is not population prevalence;\n- absence of public feature evidence means `unknown`, not `no`.\n\nAudit mappings:\n\n```bash\npython3 scripts\u002Faudit_claim_citations.py \\\n  data\u002Fclaims.csv data\u002Fsource_ledger.csv\n```\n\nSee `references\u002Fevidence_model.md`.\n\n### 5. Size the market as scenarios\n\n#### Measurement guardrails\n\nGive every component a disjoint `coverage_key` and one shared\n`denominator_id`. Do not add:\n\n- manufacturer revenue to distributor or end-customer spend;\n- production, imports, and sales without trade\u002Finventory reconciliation;\n- parent and subsidiary revenue;\n- bundles and their included components;\n- gross output and value added;\n- installed-base stock and annual transaction flow;\n- overlapping customer or geographic segments.\n\nUse product classifications and supply-use logic when industry codes are too\nbroad. Preserve an unknown\u002Fresidual category instead of forcing totals.\n\n#### Top-down and bottom-up\n\nCompute independently:\n\n```text\nTAM_top = sum(disjoint in-scope component values)\n\nTAM_bottom =\n  sum(customer_count\n      * addressable_fraction\n      * annual_quantity_per_customer\n      * price_per_unit)\n```\n\nThen apply scenario-specific serviceability and capture assumptions:\n\n```text\nSAM_s = TAM * serviceable_fraction_s\nSOM_s = SAM_s * obtainable_share_s\n```\n\nUse at least two genuinely different scenarios; a downside\u002Fbase\u002Fupside set is\nusually useful. State horizon, constraints, evidence, and assumptions. SOM is\nnot a guaranteed revenue forecast.\n\nRun the deterministic calculator:\n\n```bash\npython3 scripts\u002Fcalculate_market_sizing.py \\\n  assets\u002Fmarket_sizing_scenarios_template.json\n```\n\nReport both methods, midpoint-relative gap, scope differences, sensitivity, and\nunresolved reconciliation. Do not average incompatible methods.\n\n### 6. Forecast with explicit uncertainty\n\nSeparate observed, estimated, and forecast periods. Record series ID,\nfrequency, units, seasonal adjustment, transformations, taxonomy breaks,\nretrieval date, and vintage\u002Frevisions.\n\nFor each scenario:\n\n- provide an annual rate path or driver equations;\n- state demand, price, supply, regulation, competition, capacity, and timing\n  assumptions;\n- list evidence and assumption IDs;\n- identify conditions that invalidate the scenario.\n\nDo not call scenario bounds confidence or prediction intervals. Do not assign\nprobabilities without a validated probabilistic model and diagnostics.\n\nRun:\n\n```bash\npython3 scripts\u002Fforecast_sensitivity.py \\\n  assets\u002Fforecast_sensitivity_template.json\n```\n\nShow the range by year, endpoint sensitivity, influential assumptions, and\nswitching values. See `references\u002Fdata_analysis_patterns.md`.\n\n### 7. Analyze customers and primary research\n\nFor survey evidence, disclose sponsor, target population, frame,\nprobability\u002Fnon-probability design, recruitment, mode\u002Flanguage, field dates,\nunweighted sample, subgroup bases, weighting, response\u002Fparticipation,\ninstrument wording, precision, processing, and limitations.\n\nFor interviews\u002Ffocus groups, disclose recruitment, consent, role coverage,\ndates\u002Fmode, guide, coding, divergent evidence, privacy controls, and limits to\ngeneralization.\n\nNever:\n\n- collect more personal data than necessary;\n- place direct identifiers or raw recordings in report artifacts;\n- use research as disguised selling or lead generation;\n- misrepresent identity\u002Fpurpose;\n- pressure participants to reveal employer\u002Fcustomer secrets;\n- report qualitative mention counts as market prevalence.\n\nFollow `references\u002Fmethods_and_ethics.md`.\n\n### 8. Analyze competitors and concentration\n\nDefine product and geographic scope from the customer perspective before\nselecting competitors or calculating shares. Consider non-price dimensions,\nchannels, imports, digital\u002Fmulti-sided features, innovation, and dynamic change\nwhere relevant.\n\nUse lawful public evidence and a common product edition, geography, and as-of\ndate. Validate a complete matrix:\n\n```bash\npython3 scripts\u002Fvalidate_competitor_matrix.py \\\n  assets\u002Fcompetitor_feature_matrix_template.csv \\\n  --source-ledger assets\u002Fsource_ledger_template.csv\n```\n\nFor shares, state revenue\u002Funits\u002Fcapacity\u002Fusers or other metric, denominator,\nperiod, residual share, and source coverage. HHI\u002FCRn are descriptive screens,\nnot legal conclusions. A TAM category is not automatically a relevant antitrust\nmarket.\n\n### 9. Normalize units and definitions\n\nBefore combining values:\n\n- align geography, period, stock\u002Fflow, gross\u002Fnet, unit, and denominator;\n- convert currencies with an identified source and rate convention;\n- align base year and nominal\u002Freal basis;\n- do not force chained-dollar additivity;\n- preserve taxonomy versions and document concordance uncertainty;\n- record every conversion as a calculation.\n\nCheck comparison groups:\n\n```bash\npython3 scripts\u002Fcheck_unit_consistency.py \\\n  assets\u002Fconsistency_check_template.csv\n```\n\n### 10. Draft and review\n\nLead with findings and uncertainty, not frameworks. Use optional frameworks\nonly to organize questions; do not force scores or a fixed number of factors.\nKeep recommendations separate from evidence and include dependencies,\ntrade-offs, decision thresholds, and disconfirming evidence.\n\nVisuals are optional. If used, build them from validated local data and include\nscope, units, source IDs, calculation ID, observed\u002Fforecast distinction, and\nlimitations. See `references\u002Fvisual_generation_guide.md`.\n\nGenerate a Markdown workspace:\n\n```bash\npython3 scripts\u002Fgenerate_report_scaffold.py \\\n  assets\u002Freport_manifest_template.json .\u002Fmarket-report-workspace\n```\n\nOr use the optional LaTeX assets:\n\n- `assets\u002Fmarket_report_template.tex`\n- `assets\u002Fmarket_research.sty`\n- `assets\u002FFORMATTING_GUIDE.md`\n\n## Release gate\n\n- Market boundary, taxonomy, denominator, geography, and period are explicit.\n- Every factual\u002Fquantitative claim maps to exact source IDs.\n- Publication\u002Fretrieval dates, revisions, method, and limitations are recorded.\n- Currency\u002Fbase year, nominal\u002Freal basis, stock\u002Fflow, and units are consistent.\n- Top-down and bottom-up methods use disjoint coverage and are reconciled.\n- TAM\u002FSAM\u002FSOM and forecasts are conditional scenarios with sensitivity.\n- Survey\u002Finterview evidence carries method, privacy, and inference limits.\n- Competitor evidence is lawful, dated, scoped, and uses `unknown` honestly.\n- Source conflicts and revisions remain visible.\n- No fabricated\u002Funsupported paid figures, PII, trade secrets, deceptive\n  collection, brand impersonation, or investment-advice framing appears.\n\n## Bundled resources\n\n### References\n\n- `references\u002Freport_structure_guide.md` — modular report architecture.\n- `references\u002Fevidence_model.md` — claim-source mapping and provenance.\n- `references\u002Fdata_analysis_patterns.md` — sizing, forecast, consistency,\n  survey, and concentration methods.\n- `references\u002Fofficial_data_sources.md` — current official source\u002FAPI routing.\n- `references\u002Fmethods_and_ethics.md` — survey, interview, privacy, competitor,\n  and antitrust safeguards.\n- `references\u002Fvisual_generation_guide.md` — optional evidence-led displays.\n- `references\u002Fsources.md` — dated authoritative source ledger.\n\n### Templates and CLIs\n\nUse the templates in `assets\u002F` as synthetic schemas, not real-world evidence.\nAll scripts in `scripts\u002F` are standard-library, bounded, local-only tools. They\nreject oversized or malformed input, do not follow symlink inputs, do not\noverwrite outputs without explicit permission, and make no network, LLM, image,\ndynamic-evaluation, or pickle calls.\n\n## Agent operating procedure\n\n1. **Check the environment.** Collect the user's draft, data, figures and target venue before writing anything.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Draft one section or paragraph and confirm tone, structure and terminology with the user.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Every claim traces to the user's data or a verified source; numbers match tables and figures; citations are real (`citation-verification`).\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| A claim has no supporting data or citation | Flag it in the text (e.g. `[citation needed]`) instead of inventing support. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- The user is the author: never add results, references or claims they did not provide or approve.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n","# Market Research Reports\n\n## Purpose\n\nCreate decision-focused market reports whose claims, calculations, assumptions,\nand uncertainties can be audited. Match depth and format to the question and\nevidence. There is no required length, chapter count, visual count, or output\nformat.\n\nDo not:\n\n- imitate or imply affiliation with a consulting, analyst, or research brand;\n- invent citations, quotes, market shares, or paid-market figures;\n- present TAM\u002FSAM\u002FSOM or a forecast as one certain truth;\n- treat a framework, chart, or fluent narrative as evidence;\n- provide investment, legal, antitrust, tax, accounting, or regulatory advice.\n\n## Operating principles\n\n1. **Define before sizing.** Fix product, customer, geography, channel, period,\n   measure, unit, denominator, currency\u002Fbase year, and taxonomy.\n2. **Map every claim.** Every factual or quantitative claim has a claim ID and\n   exact source IDs.\n3. **Separate statement types.** Distinguish facts, estimates, calculations,\n   forecasts, opinions, and recommendations.\n4. **Prefer primary evidence.** Use official statistics, regulator records,\n   filed company disclosures, and transparent original studies before\n   secondary synthesis.\n5. **Preserve uncertainty.** Retain source conflicts, revisions, scenario\n   ranges, sensitivity, and limitations.\n6. **Keep methods reproducible.** Use local structured inputs and deterministic\n   calculations when practical.\n7. **Collect lawfully and ethically.** No deception, PII disclosure, access\n   circumvention, confidential material, or trade-secret acquisition.\n\n## Workflow\n\n### 1. Establish the research contract\n\nClarify:\n\n- decision, audience, deadline, and materiality threshold;\n- formal market definition and adjacent exclusions;\n- buyer, payer, user, transaction, and value-chain level;\n- geography and treatment of imports, exports, and channels;\n- historical period, forecast period, and retrieval cutoff;\n- revenue\u002Fexpenditure, gross output\u002Fvalue added, units, capacity, users, or\n  another measure;\n- stock\u002Fflow, gross\u002Fnet, taxes, and denominator;\n- currency, base year, and nominal\u002Freal\u002Fcurrent\u002Fconstant basis;\n- industry and product classification with version;\n- permitted data sources, primary research, confidentiality, and output format.\n\nAsk a focused question when a missing choice would materially change the\ndenominator or result. Otherwise state a provisional scope and proceed.\n\nUse `references\u002Freport_structure_guide.md` for modular report design.\n\n### 2. Build the evidence plan\n\nRoute each question to the source closest to the underlying event:\n\n1. primary law, regulator decision, official filing, or official statistic;\n2. original company filing or attributable first-party disclosure;\n3. transparent survey\u002Fstudy with inspectable methods;\n4. institutional or peer-reviewed research using identifiable primary data;\n5. industry association data with disclosed coverage;\n6. reputable secondary synthesis;\n7. lawfully accessed paid estimate with inspectable scope and method;\n8. news\u002Fcommentary for leads or attributable events.\n\nFor company data, prefer the official filing system in the relevant\njurisdiction. For industry, labor, prices, population, trade, and national\naccounts, prefer the responsible national statistical agency or central bank.\nFor cross-country work, use harmonized World Bank, IMF, OECD, or Eurostat data\nonly after checking definitions and original-source lineage.\n\nRead `references\u002Fofficial_data_sources.md` before using public APIs. API rules\nand limits are a dated snapshot: verify current official terms before automated\nor high-volume retrieval. Never put an API key in a report or bundled script.\n\n### 3. Create the source ledger\n\nAssign stable IDs (`S-001`, `S-002`, ...). Record:\n\n- title, publisher, URL\u002Fpersistent ID, source type;\n- publication date and retrieval date;\n- original producer when accessed through an aggregator;\n- geography, covered population, period, and vintage;\n- currency, base year, price basis, measure type, unit, and denominator;\n- taxonomy and version;\n- preliminary\u002Frevised\u002Ffinal\u002Fcurrent status;\n- method, sample, imputation, suppression, and limitations;\n- license\u002Fterms and lawful local snapshot path.\n\nUse `assets\u002Fsource_ledger_template.csv` and validate it:\n\n```bash\npython3 scripts\u002Fvalidate_evidence_ledger.py data\u002Fsource_ledger.csv\n```\n\nIf publication date is unavailable, record `not-stated`; do not guess.\n\n### 4. Maintain a claims ledger\n\nAssign IDs (`C-001`, ...). Keep the exact claim text, statement type, source\nIDs, report location, as-of date, geography, currency\u002Fbase, measure\u002Funit,\ntaxonomy, revision status, confidence, calculation ID, and assumption IDs.\n\nRules:\n\n- one end-of-paragraph citation does not support unrelated sentences;\n- split compound claims that rely on different evidence;\n- a calculation cites its inputs, not a source that never published the result;\n- an aggregator and its original source are not independent corroboration;\n- an interview theme is not population prevalence;\n- absence of public feature evidence means `unknown`, not `no`.\n\nAudit mappings:\n\n```bash\npython3 scripts\u002Faudit_claim_citations.py \\\n  data\u002Fclaims.csv data\u002Fsource_ledger.csv\n```\n\nSee `references\u002Fevidence_model.md`.\n\n### 5. Size the market as scenarios\n\n#### Measurement guardrails\n\nGive every component a disjoint `coverage_key` and one shared\n`denominator_id`. Do not add:\n\n- manufacturer revenue to distributor or end-customer spend;\n- production, imports, and sales without trade\u002Finventory reconciliation;\n- parent and subsidiary revenue;\n- bundles and their included components;\n- gross output and value added;\n- installed-base stock and annual transaction flow;\n- overlapping customer or geographic segments.\n\nUse product classifications and supply-use logic when industry codes are too\nbroad. Preserve an unknown\u002Fresidual category instead of forcing totals.\n\n#### Top-down and bottom-up\n\nCompute independently:\n\n```text\nTAM_top = sum(disjoint in-scope component values)\n\nTAM_bottom =\n  sum(customer_count\n      * addressable_fraction\n      * annual_quantity_per_customer\n      * price_per_unit)\n```\n\nThen apply scenario-specific serviceability and capture assumptions:\n\n```text\nSAM_s = TAM * serviceable_fraction_s\nSOM_s = SAM_s * obtainable_share_s\n```\n\nUse at least two genuinely different scenarios; a downside\u002Fbase\u002Fupside set is\nusually useful. State horizon, constraints, evidence, and assumptions. SOM is\nnot a guaranteed revenue forecast.\n\nRun the deterministic calculator:\n\n```bash\npython3 scripts\u002Fcalculate_market_sizing.py \\\n  assets\u002Fmarket_sizing_scenarios_template.json\n```\n\nReport both methods, midpoint-relative gap, scope differences, sensitivity, and\nunresolved reconciliation. Do not average incompatible methods.\n\n### 6. Forecast with explicit uncertainty\n\nSeparate observed, estimated, and forecast periods. Record series ID,\nfrequency, units, seasonal adjustment, transformations, taxonomy breaks,\nretrieval date, and vintage\u002Frevisions.\n\nFor each scenario:\n\n- provide an annual rate path or driver equations;\n- state demand, price, supply, regulation, competition, capacity, and timing\n  assumptions;\n- list evidence and assumption IDs;\n- identify conditions that invalidate the scenario.\n\nDo not call scenario bounds confidence or prediction intervals. Do not assign\nprobabilities without a validated probabilistic model and diagnostics.\n\nRun:\n\n```bash\npython3 scripts\u002Fforecast_sensitivity.py \\\n  assets\u002Fforecast_sensitivity_template.json\n```\n\nShow the range by year, endpoint sensitivity, influential assumptions, and\nswitching values. See `references\u002Fdata_analysis_patterns.md`.\n\n### 7. Analyze customers and primary research\n\nFor survey evidence, disclose sponsor, target population, frame,\nprobability\u002Fnon-probability design, recruitment, mode\u002Flanguage, field dates,\nunweighted sample, subgroup bases, weighting, response\u002Fparticipation,\ninstrument wording, precision, processing, and limitations.\n\nFor interviews\u002Ffocus groups, disclose recruitment, consent, role coverage,\ndates\u002Fmode, guide, coding, divergent evidence, privacy controls, and limits to\ngeneralization.\n\nNever:\n\n- collect more personal data than necessary;\n- place direct identifiers or raw recordings in report artifacts;\n- use research as disguised selling or lead generation;\n- misrepresent identity\u002Fpurpose;\n- pressure participants to reveal employer\u002Fcustomer secrets;\n- report qualitative mention counts as market prevalence.\n\nFollow `references\u002Fmethods_and_ethics.md`.\n\n### 8. Analyze competitors and concentration\n\nDefine product and geographic scope from the customer perspective before\nselecting competitors or calculating shares. Consider non-price dimensions,\nchannels, imports, digital\u002Fmulti-sided features, innovation, and dynamic change\nwhere relevant.\n\nUse lawful public evidence and a common product edition, geography, and as-of\ndate. Validate a complete matrix:\n\n```bash\npython3 scripts\u002Fvalidate_competitor_matrix.py \\\n  assets\u002Fcompetitor_feature_matrix_template.csv \\\n  --source-ledger assets\u002Fsource_ledger_template.csv\n```\n\nFor shares, state revenue\u002Funits\u002Fcapacity\u002Fusers or other metric, denominator,\nperiod, residual share, and source coverage. HHI\u002FCRn are descriptive screens,\nnot legal conclusions. A TAM category is not automatically a relevant antitrust\nmarket.\n\n### 9. Normalize units and definitions\n\nBefore combining values:\n\n- align geography, period, stock\u002Fflow, gross\u002Fnet, unit, and denominator;\n- convert currencies with an identified source and rate convention;\n- align base year and nominal\u002Freal basis;\n- do not force chained-dollar additivity;\n- preserve taxonomy versions and document concordance uncertainty;\n- record every conversion as a calculation.\n\nCheck comparison groups:\n\n```bash\npython3 scripts\u002Fcheck_unit_consistency.py \\\n  assets\u002Fconsistency_check_template.csv\n```\n\n### 10. Draft and review\n\nLead with findings and uncertainty, not frameworks. Use optional frameworks\nonly to organize questions; do not force scores or a fixed number of factors.\nKeep recommendations separate from evidence and include dependencies,\ntrade-offs, decision thresholds, and disconfirming evidence.\n\nVisuals are optional. If used, build them from validated local data and include\nscope, units, source IDs, calculation ID, observed\u002Fforecast distinction, and\nlimitations. See `references\u002Fvisual_generation_guide.md`.\n\nGenerate a Markdown workspace:\n\n```bash\npython3 scripts\u002Fgenerate_report_scaffold.py \\\n  assets\u002Freport_manifest_template.json .\u002Fmarket-report-workspace\n```\n\nOr use the optional LaTeX assets:\n\n- `assets\u002Fmarket_report_template.tex`\n- `assets\u002Fmarket_research.sty`\n- `assets\u002FFORMATTING_GUIDE.md`\n\n## Release gate\n\n- Market boundary, taxonomy, denominator, geography, and period are explicit.\n- Every factual\u002Fquantitative claim maps to exact source IDs.\n- Publication\u002Fretrieval dates, revisions, method, and limitations are recorded.\n- Currency\u002Fbase year, nominal\u002Freal basis, stock\u002Fflow, and units are consistent.\n- Top-down and bottom-up methods use disjoint coverage and are reconciled.\n- TAM\u002FSAM\u002FSOM and forecasts are conditional scenarios with sensitivity.\n- Survey\u002Finterview evidence carries method, privacy, and inference limits.\n- Competitor evidence is lawful, dated, scoped, and uses `unknown` honestly.\n- Source conflicts and revisions remain visible.\n- No fabricated\u002Funsupported paid figures, PII, trade secrets, deceptive\n  collection, brand impersonation, or investment-advice framing appears.\n\n## Bundled resources\n\n### References\n\n- `references\u002Freport_structure_guide.md` — modular report architecture.\n- `references\u002Fevidence_model.md` — claim-source mapping and provenance.\n- `references\u002Fdata_analysis_patterns.md` — sizing, forecast, consistency,\n  survey, and concentration methods.\n- `references\u002Fofficial_data_sources.md` — current official source\u002FAPI routing.\n- `references\u002Fmethods_and_ethics.md` — survey, interview, privacy, competitor,\n  and antitrust safeguards.\n- `references\u002Fvisual_generation_guide.md` — optional evidence-led displays.\n- `references\u002Fsources.md` — dated authoritative source ledger.\n\n### Templates and CLIs\n\nUse the templates in `assets\u002F` as synthetic schemas, not real-world evidence.\nAll scripts in `scripts\u002F` are standard-library, bounded, local-only tools. They\nreject oversized or malformed input, do not follow symlink inputs, do not\noverwrite outputs without explicit permission, and make no network, LLM, image,\ndynamic-evaluation, or pickle calls.\n\n## Agent operating procedure\n\n1. **Check the environment.** Collect the user's draft, data, figures and target venue before writing anything.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Draft one section or paragraph and confirm tone, structure and terminology with the user.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Every claim traces to the user's data or a verified source; numbers match tables and figures; citations are real (`citation-verification`).\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| A claim has no supporting data or citation | Flag it in the text (e.g. `[citation needed]`) instead of inventing support. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- The user is the author: never add results, references or claims they did not provide or approve.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n",{"name":5,"license":15,"metadata":49,"description":7,"compatibility":54},{"tags":50,"version":51,"category":52,"maintainer":53},"market research, market sizing, TAM SAM SOM, competitive analysis, reports","1.3","research-writing","Kalaris Labs","Python 3.11+ standard library for optional offline CLIs. The optional LaTeX template uses XeLaTeX or LuaLaTeX. Online research requires user-approved network access and source-specific terms; bundled scripts make no network, LLM, or image calls.","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports",483,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-13 02:16:32",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill market-research-reports",{"tool":71,"command":72},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill market-research-reports",{"tool":74,"command":75},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill market-research-reports","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002FSKILL.md",true,[79,85,89,93,97,101,105,109,113,117,121,127,131,135,139,143,147,151,157,161,165,169,173,177,181],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"assets\u002Fclaims_ledger_template.csv","ASSET","资产文件",957,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002Fclaims_ledger_template.csv",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"assets\u002Fcompetitor_feature_matrix_template.csv",683,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002Fcompetitor_feature_matrix_template.csv",{"path":90,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":91,"sourceHtmlUrl":92},"assets\u002Fconsistency_check_template.csv",432,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002Fconsistency_check_template.csv",{"path":94,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":95,"sourceHtmlUrl":96},"assets\u002Fforecast_sensitivity_template.json",1742,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002Fforecast_sensitivity_template.json",{"path":98,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":99,"sourceHtmlUrl":100},"assets\u002FFORMATTING_GUIDE.md",4935,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002FFORMATTING_GUIDE.md",{"path":102,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":103,"sourceHtmlUrl":104},"assets\u002Fmarket_report_template.tex",10037,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002Fmarket_report_template.tex",{"path":106,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":107,"sourceHtmlUrl":108},"assets\u002Fmarket_research.sty",6319,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002Fmarket_research.sty",{"path":110,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":111,"sourceHtmlUrl":112},"assets\u002Fmarket_sizing_scenarios_template.json",3177,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002Fmarket_sizing_scenarios_template.json",{"path":114,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":115,"sourceHtmlUrl":116},"assets\u002Freport_manifest_template.json",882,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002Freport_manifest_template.json",{"path":118,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":119,"sourceHtmlUrl":120},"assets\u002Fsource_ledger_template.csv",1353,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Fassets\u002Fsource_ledger_template.csv",{"path":122,"resourceType":123,"resourceTypeDesc":124,"sizeBytes":125,"sourceHtmlUrl":126},"references\u002Fdata_analysis_patterns.md","REFERENCE","参考文档",9848,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Freferences\u002Fdata_analysis_patterns.md",{"path":128,"resourceType":123,"resourceTypeDesc":124,"sizeBytes":129,"sourceHtmlUrl":130},"references\u002Fevidence_model.md",6139,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmarket-research-reports\u002Freferences\u002Fevidence_model.md",{"path":132,"resourceType":123,"resourceTypeDesc":124,"sizeByt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rAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":190,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":193,"publishedAt":194},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":190,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":201,"publishedAt":194},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":190,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":208,"publishedAt":194},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":190,"licenseFileUrl":6,"sourceHtmlUrl":213,"categories":214,"keywords":215,"publishedAt":44},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":237,"name":238,"nameZh":6,"description":239,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":190,"licenseFileUrl":6,"sourceHtmlUrl":240,"categories":241,"keywords":242,"publishedAt":44},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[243,244,245,231,246,247,248,249,250,251,252,253,254,255,221,256,257,258,259,260],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":262,"name":263,"nameZh":6,"description":264,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":190,"licenseFileUrl":6,"sourceHtmlUrl":265,"categories":266,"keywords":276,"publishedAt":44},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[267,270,273],{"categoryNo":268,"categoryName":269,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":271,"categoryName":272,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":274,"categoryName":275,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[263,277,278,279,280,281,282,257,283,222,284,285,286,287,227,43,288,289,290,291],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","mcp server","schedule","api","publish"]