nwave-ai/nw-source-verification
nWave/skills/nw-source-verification/SKILL.md
Source reputation tiers, cross-referencing methodology, bias detection, and citation format requirements
$ install --global
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Last scanned: Apr 24, 2026, 8:51 PM3.0s1 file scanned
SKILL.md
- name:
- nw-source-verification
- description:
- Source reputation tiers, cross-referencing methodology, bias detection, and citation format requirements
- user-invocable:
- false
- disable-model-invocation:
- true
Source Verification
Source Reputation Tiers
Validate every source against the trusted source domains provided via prompt context.
| Tier | Score | Examples | Verification | |------|-------|---------|-------------| | High | 1.0 | Academic (.edu, arxiv.org, ieee.org), Official (.gov, w3.org, ietf.org), Tech docs (developer.mozilla.org), OSS foundations (apache.org, cncf.io) | Standard citation | | Medium-High | 0.8 | Industry leaders (martinfowler.com, stackoverflow.com, infoq.com) | Cross-ref with 1+ high-tier | | Medium | 0.6 | Community (medium.com verified experts, dev.to, hashnode.com) | Author verification + 3-source cross-ref | | Excluded | 0.0 | Unverified blogs (*.blogspot.com, wordpress.com), quora.com, pastebin.com | Reject, log warning, find alternative |
Cross-Referencing Methodology
- Identify the specific assertion to verify
- Find 2+ independent sources not citing each other (avoid circular refs)
- Verify independence: different authors, publishers, organizations
- Compare: agree on substance (minor wording differences OK)
- Document: verified / partially verified / unverified per finding
Circular Reference Detection
- Source B cites Source A = one source, not two
- Multiple sources referencing single study = cite the original
- Prefer primary over secondary sources
Bias Detection Checklist
Evaluate before citing:
- Commercial interest: selling related product/service?
- Sponsorship: sponsored/funded content?
- Conflict of interest: author benefits from conclusion?
- Geographic/cultural bias: limited to single region?
- Temporal bias: publication dates skewed to specific era?
- Cherry-picking: contradictory evidence acknowledged?
- Logical fallacies: correlation as causation, authority without evidence
When bias detected: note in Source Analysis, reduce confidence.
Citation Format
[1] {Author/Organization}. "{Title}". {Publication/Website}. {Date}. {Full URL}. Accessed {YYYY-MM-DD}.
Required Metadata Per Source
Source URL | Domain | Access date | Reputation score (from tiers) | Verification status
Paywalled or Restricted Sources
Mark "[Paywalled]"/"[Restricted Access]" | Provide URL | Find open-access alternative | Note in Knowledge Gaps
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