// Methodology for market research and data collection, ensuring data quality and source traceability
| name | research-methodology |
| description | Methodology for market research and data collection, ensuring data quality and source traceability |
Every data point must have a source and confidence score. Estimates are not facts — label them as estimates.
| Tier | Source Type | Confidence Range | Examples |
|---|---|---|---|
| 1 | Official public data | 0.85-0.95 | Government statistics, public company filings, industry association reports |
| 2 | Authoritative media | 0.70-0.85 | Major financial media, industry analysis reports |
| 3 | Web search results | 0.50-0.70 | Blogs, forums, review sites |
| 4 | Model inference/estimates | 0.20-0.50 | Extrapolations from known data, analogical estimates |
| 5 | Training data memory | 0.10-0.30 | Data from model memory (may be outdated) |
Use wanman artifact put to record each research data point:
# Good recording practice
wanman artifact put --kind market_data --path "competitors/cafe_a/pricing" \
--meta '{"source":"web_search:dianping.com","confidence":0.65,"data_date":"2024-12","avg_price":35,"currency":"CNY","category":"specialty_coffee"}'
# Bad practice (missing source and confidence)
wanman artifact put --kind market_data --path "competitors/pricing" \
--meta '{"avg_price":35}'
Naming conventions for artifact kind and path, ensuring data can be aggregated and analyzed
Quality standards for structured deliverables, ensuring data is verifiable and traceable
CEO checks consistency of agent outputs during post-completion review
wanman CLI command reference — inter-agent communication, task management, artifact storage
File and output conventions for agent workspaces