Source Verification
SkillDev toolsSource reputation tiers, cross-referencing methodology, bias detection, and citation format requirements
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Source Verification skill
What this skill tells your AI
The instructions your AI receives, as published by nwave-ai/nwave in nWave/skills/nw-source-verification/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 610
- Forks
- 64
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
nw-source-verification- Source
- github.com/nwave-ai/nwave