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Methodology for market research and data collection, ensuring data quality and source traceability
Passed 16 of 20 test cases with the skill, and 7 of 20 without it.
and 13 more in the full report
10 fixed8 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
A researcher wants to log an analogical model calculation estimating daily foot traffic based on neighboring subway exit counts. What tier in the data hierarchy does this source belong to?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies the source as Tier 4 model inference or estimates. | Missed | Passed |
The instructions your model reads when the skill loads.
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| 5 | Training data memory | 0.10-0.30 | Data from model memory (may be outdated) |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}'
test cases passed, vs 7 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.