nw-ab-critique-dimensi…
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Research output templates, distillation workflow, and quality standards for evidence-driven research
$ npx -y skills add nWave-ai/nWave --skill nw-research-methodology --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/nw-research-methodologyContext preview
The summary Claude sees to decide when to auto-load this skill.
Research output templates, distillation workflow, and quality standards for evidence-driven research
name: nw-research-methodology description: Research output templates, distillation workflow, and quality standards for evidence-driven research user-invocable: false disable-model-invocation: true
Use for all research documents in `docs/research/`.
# Research: {Topic}
**Date**: {ISO-8601} | **Researcher**: nw-researcher (Nova) | **Confidence**: {High/Medium/Low} | **Sources**: {count}
## Executive Summary
{2-3 paragraph overview of key findings, main insights, and overall conclusion}
## Research Methodology
**Search Strategy**: {how sources were found}
**Source Selection**: Types: {academic/official/industry/technical_docs} | Reputation: {high/medium-high min} | Verification: {cross-referencing approach}
**Quality Standards**: Target 3 sources/claim (min 1 authoritative) | All major claims cross-referenced | Avg reputation: {0.0-1.0}
## Findings
### Finding 1: {Descriptive Title}
**Evidence**: "{Direct quote or specific data point}"
**Source**: [{Source Name}]({URL}) - Accessed {YYYY-MM-DD}
**Confidence**: {High/Medium/Low}
**Verification**: [{Source 2}]({URL2}), [{Source 3}]({URL3})
**Analysis**: {Brief interpretation or context}
{Repeat Finding structure as needed}
## Source Analysis
| Source | Domain | Reputation | Type | Access Date | Cross-verified |
|--------|--------|------------|------|-------------|----------------|
| {name} | {domain} | {High/Medium-High/Medium} | {academic/official/industry/technical} | {YYYY-MM-DD} | {Y/N} |
Reputation: High: {count} ({%}) | Medium-high: {count} ({%}) | Avg: {0.0-1.0}
## Knowledge Gaps
### Gap 1: {Description}
**Issue**: {missing/unclear info} | **Attempted**: {sources searched} | **Recommendation**: {how to address}
## Conflicting Information (if applicable)
### Conflict 1: {Topic}
**Position A**: {Statement} — Source: [{Name}]({URL}), Reputation: {score}, Evidence: {quote}
**Position B**: {Contradictory statement} — Source: [{Name}]({URL}), Reputation: {score}, Evidence: {quote}
**Assessment**: {Which source more authoritative and why}
## Recommendations for Further Research
1. {Specific recommendation with rationale}
## Full Citations
[1] {Author}. "{Title}". {Publication}. {Date}. {URL}. Accessed {YYYY-MM-DD}.
## Research Metadata
Duration: {X min} | Examined: {count} | Cited: {count} | Cross-refs: {count} | Confidence: High {%}, Medium {%}, Low {%} | Output: docs/research/{filename}When creating a skill (via `*create-skill` or `skill_for` specified):
Execute comprehensive research, create full doc in `docs/research/{category}/{topic}-comprehensive-research.md`, complete quality gates.
1. Read comprehensive research 2. Transform: academic -> practitioner-focused 3. Preserve 100% essential concepts (no lossy compression) 4. Remove: verbose explanations, extensive examples, redundant cross-refs 5. Keep: core concepts, practical tools, methodologies, decision heuristics 6. Make self-contained (no external refs) | Target <1000 tokens/file 7. Write to `~/.claude/skills/nw-{skill-name}/SKILL.md{topic}-methodology.md`
Verify all essential concepts present | Confirm practitioner focus | Check self-containment
Source requirements adapt to available turn budget:
When budget runs low, prioritize BREADTH (cover all claims with minimum sources) over DEPTH (exhaust sources for one claim while ignoring others).
Additional requirements:
1. Every major claim has citations (3+ ideal, 2 acceptable, 1 authoritative minimum) | 2. All sources from trusted domains 3. All findings evidence-backed | 4. Knowledge gaps documented | 5. Output in allowed directories 6. Claims with fewer than 3 sources have confidence rating adjusted accordingly
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
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
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