/nw-source-verification
Source reputation tiers, cross-referencing methodology, bias detection, and citation format requirements
$ npx -y skills add nWave-ai/nWave --skill nw-source-verification --agent claude-codeHow it fires
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- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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/nw-source-verification
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Source reputation tiers, cross-referencing methodology, bias detection, and citation format requirements
SKILL.md
nw-source-verification.SKILL.mdname: 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
1. **Identify** the specific assertion to verify 2. **Find** 2+ independent sources not citing each other (avoid circular refs) 3. **Verify independence**: different authors, publishers, organizations 4. **Compare**: agree on substance (minor wording differences OK) 5. **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: 1. **Commercial interest**: selling related product/service? 2. **Sponsorship**: sponsored/funded content? 3. **Conflict of interest**: author benefits from conclusion? 4. **Geographic/cultural bias**: limited to single region? 5. **Temporal bias**: publication dates skewed to specific era? 6. **Cherry-picking**: contradictory evidence acknowledged? 7. **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
Read more
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
1. **Identify** the specific assertion to verify 2. **Find** 2+ independent sources not citing each other (avoid circular refs) 3. **Verify independence**: different authors, publishers, organizations 4. **Compare**: agree on substance (minor wording differences OK) 5. **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: 1. **Commercial interest**: selling related product/service? 2. **Sponsorship**: sponsored/funded content? 3. **Conflict of interest**: author benefits from conclusion? 4. **Geographic/cultural bias**: limited to single region? 5. **Temporal bias**: publication dates skewed to specific era? 6. **Cherry-picking**: contradictory evidence acknowledged? 7. **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
AI agents that guide you from idea to working code, with human judgment at every gate. nWave runs inside Claude Code. It breaks feature delivery into seven waves (discover, diverge, discuss, design, devops, distill, deliver).
Repo: nWave-ai/nWave
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