Review dimensions and bug patterns for journey artifact reviews
npx skills add https://github.com/nWave-ai/nWave --skill nw-por-review-criteria
Domain knowledge for product-owner-reviewer (Eclipse). Covers journey coherence, emotional arcs, shared artifacts, example data quality, CLI UX patterns.
Validate complete flow with no gaps.
Checks: all steps start-to-goal defined | no orphan steps | no dead ends | decision branches lead somewhere | error paths guide to recovery
Severity: critical = missing main flow steps / dead ends | high = orphan steps | medium = ambiguous decisions | low = minor clarity
Validate emotional design quality.
Checks: arc defined (start/middle/end) | all steps annotated | no jarring transitions | confidence builds progressively | error states guide not frustrate
Severity: critical = no arc / major jarring transitions | high = missing key annotations | medium = confidence doesn't build | low = minor polish
Validate ${variable} sources and consistency.
Checks: all ${variables} have documented source | single source of truth | all consumers listed | integration risks assessed | validation methods specified
Severity: critical = undocumented ${variables} / multiple sources | high = missing consumers / unassessed risks | medium = incomplete validation | low = minor consumer docs
Key review skill -- analyze data for integration gaps.
Checks: realistic not generic | reveals integration dependencies | catches version mismatches | catches path inconsistencies | consistent across steps
Severity: critical = generic placeholders hide issues | high = inconsistent across steps | medium = doesn't reveal deps | low = could be more realistic
Apply: 1) trace ${version} through all steps -- same? 2) compare ${install_path} step 2 vs 3 -- match? 3) does data show actual integration points?
Generic "v1.0.0" or "/path/to/install" hides bugs. Realistic "v1.2.86" from "pyproject.toml" reveals bugs.
Checks: command vocabulary consistent | help available | error messages guide to resolution | progressive disclosure respected
Severity: critical = inconsistent commands | high = no error recovery guidance | medium = missing progressive disclosure | low = minor vocabulary
Multiple version sources. Trace ${version} through all steps -- same source?
Step 1: v${version} from pyproject.toml
Step 2: v${version} from version.txt <-- MISMATCH
URLs without canonical source. For each URL: "where is this defined?"
Install: git+https://github.com/org/repo
<-- Where is this URL canonically defined?
Paths from different sources. Trace ${path} -- same source?
Install to: ${install_path} from config
Uninstall from: ~/.claude/agents/nw/ <-- HARDCODED
CLI commands without slash equivalents. Check both contexts exist.
Terminal: crafter run
Claude Code: /nw-execute <-- EXISTS?
review_id: "{timestamp}"
reviewer: "nw-product-owner-reviewer (Eclipse)"
artifact_reviewed: "{file path}"
strengths:
- strength: "{Positive aspect}"
example: "{Specific evidence}"
issues_identified:
journey_coherence:
- issue: "{Description}"
severity: "critical|high|medium|low"
location: "{Where}"
recommendation: "{Fix}"
emotional_arc:
- issue: "{Description}"
severity: "critical|high|medium|low"
location: "{Where}"
recommendation: "{Fix}"
shared_artifacts:
- issue: "{Description}"
severity: "critical|high|medium|low"
artifact: "{Which ${variable}}"
recommendation: "{Fix}"
example_data:
- issue: "{Description}"
severity: "critical|high|medium|low"
data_point: "{Which data}"
integration_risk: "{What bug it might hide}"
recommendation: "{Fix}"
bug_patterns_detected:
- pattern: "version_mismatch|hardcoded_url|path_inconsistency|missing_command"
severity: "critical|high"
evidence: "{Finding}"
recommendation: "{Fix}"
recommendations:
critical: ["{Must fix before approval}"]
high: ["{Should fix before approval}"]
medium: ["{Fix in next iteration}"]
low: ["{Consider for polish}"]
approval_status: "approved|rejected_pending_revisions|conditionally_approved"
approval_conditions: "{If conditional, what must be done}"
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take nwave-ai/nw-por-review-criteria from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.