Research MethodologySAFE
wanman is an open-source agent matrix runtime inspired by Japanese one-man trains. It lets human users step back into an observer role while local agent runtimes coordinate autonomous multi-agent workflows, task execution, and artifacts.
Overview
wanman is an open-source agent matrix runtime inspired by Japanese one-man trains. It lets human users step back into an observer role while local agent runtimes coordinate autonomous multi-agent workflows, task execution, and artifacts.
f0c3ec2eb827OBSERVED · 2026-10-13What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: research-methodology
description: Methodology for market research and data collection, ensuring data quality and source traceability
---
# Research Methodology
## Core Principles
**Every data point must have a source and confidence score.** Estimates are not facts — label them as estimates.
## Data Source Hierarchy
| 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| 5 | Training data memory | 0.10-0.30 | Data from model memory (may be outdated) |
## Search Strategy
1. **Search before estimating**: Do not guess from training data if the information can be searched
2. **Cross-validate**: Confirm key data from at least 2 independent sources
3. **Label timeliness**: Note the time range of data (e.g., "2024 data")
4. **Distinguish facts from opinions**: Clearly label when quoting
## Data Recording Format
Use `wanman artifact put` to record each research data point:
```bash
# 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}'
```
## Common Pitfalls
- **Survivorship bias**: Successful cases found via search do not represent the full industry picture
- **Outdated data**: Rent and foot traffic data from 2 years ago may have changed significantly
- **Regional differences**: Tier-1 city data cannot be directly applied to tier-2 or tier-3 cities
- **Currency/unit confusion**: Always label currency and measurement unitsTrust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
f0c3ec2eb827full audit observations/trust-audit/skill/chekusu__research-methodology.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-13 | f0c3ec2eb827 | SAFE | B | 89 | first audit |
Questions
What does the Research Methodology skill do?
wanman is an open-source agent matrix runtime inspired by Japanese one-man trains. It lets human users step back into an observer role while local agent runtimes coordinate autonomous multi-agent workflows, task execution, and artifacts.
Is Research Methodology safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Research Methodology access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (f0c3ec2eb827), read on 2026-10-13. The repository is watched, and a new audit runs when it changes — this is the first audit.