aiwg
Version:
Cognitive architecture for AI-augmented software development with structured memory, ensemble validation, and closed-loop correction. FAIR-aligned artifacts, 84% cost reduction via human-in-the-loop, standards adopted by 100+ organizations.
277 lines (252 loc) • 6.63 kB
YAML
# Research Document Frontmatter Schema
# Based on REF-056 FAIR Principles (I1 - machine-readable metadata)
# Issue: #105
$schema: "https://json-schema.org/draft/2020-12/schema"
$id: "https://aiwg.io/schemas/research-frontmatter/v1"
title: "Research Document Frontmatter Schema"
description: |
Structured YAML frontmatter for research documents to enable machine-readable
metadata extraction and FAIR compliance. All research documents (REF-XXX)
MUST include this frontmatter for interoperability.
type: object
required:
- ref_id
- title
- authors
- year
- source_type
properties:
ref_id:
type: string
pattern: "^REF-[0-9]{3}(-[a-z]+)?$"
description: "Unique reference identifier (e.g., REF-001, REF-056-fair)"
title:
type: string
minLength: 10
maxLength: 300
description: "Full paper/document title"
short_title:
type: string
maxLength: 50
description: "Abbreviated title for references"
authors:
type: array
minItems: 1
items:
type: object
properties:
name:
type: string
affiliation:
type: string
orcid:
type: string
pattern: "^[0-9]{4}-[0-9]{4}-[0-9]{4}-[0-9]{3}[0-9X]$"
description: "Author list with optional ORCID"
year:
type: integer
minimum: 1900
maximum: 2100
description: "Publication year"
month:
type: integer
minimum: 1
maximum: 12
description: "Publication month (optional)"
source_type:
type: string
enum:
- peer_reviewed_journal
- peer_reviewed_conference
- preprint
- technical_report
- book_chapter
- industry_whitepaper
- standard
- thesis
description: "Type of source for GRADE assessment"
venue:
type: object
properties:
name:
type: string
description: "Journal/conference name"
abbreviation:
type: string
description: "Venue abbreviation (e.g., NeurIPS, ICML)"
volume:
type: string
issue:
type: string
pages:
type: string
description: "Publication venue details"
identifiers:
type: object
properties:
doi:
type: string
pattern: "^10\\.[0-9]{4,}/.+$"
description: "Digital Object Identifier"
arxiv:
type: string
pattern: "^[0-9]{4}\\.[0-9]{4,5}(v[0-9]+)?$"
description: "arXiv identifier"
isbn:
type: string
description: "ISBN for books"
url:
type: string
format: uri
description: "Canonical URL"
description: "Document identifiers for retrieval"
keywords:
type: array
items:
type: string
description: "Keywords/topics"
categories:
type: array
items:
type: string
enum:
- multi_agent_systems
- code_generation
- reasoning
- planning
- tool_use
- memory
- retrieval
- evaluation
- human_ai_collaboration
- production_systems
- quality_assurance
- information_science
- standards
description: "AIWG-specific categories"
abstract:
type: string
minLength: 50
description: "Paper abstract or summary"
key_findings:
type: array
items:
type: object
properties:
finding:
type: string
metric:
type: string
impact:
type: string
enum: [high, medium, low]
description: "Primary findings with quantified metrics"
aiwg_relevance:
type: object
properties:
applicability:
type: string
enum: [direct, partial, reference, background]
components_affected:
type: array
items:
type: string
enum:
- agents
- flows
- schemas
- rules
- templates
- commands
- skills
implementation_priority:
type: string
enum: [top-10, round-2, round-3, future]
related_issues:
type: array
items:
type: string
pattern: "^#[0-9]+$"
description: "AIWG-specific relevance assessment"
quality_assessment:
type: object
properties:
grade_baseline:
type: string
enum: [high, moderate, low, very_low]
downgrade_factors:
type: array
items:
type: string
upgrade_factors:
type: array
items:
type: string
final_grade:
type: string
enum: [high, moderate, low, very_low]
description: "GRADE quality assessment"
pdf_hash:
type: string
pattern: "^[a-f0-9]{64}$"
description: "SHA-256 hash of source PDF for fixity"
analysis_date:
type: string
format: date
description: "When AIWG analysis was performed"
last_verified:
type: string
format: date
description: "Last verification of DOI/source"
# Validation rules
validation:
require_doi_for_published:
condition: "source_type in ['peer_reviewed_journal', 'peer_reviewed_conference']"
require: "identifiers.doi"
message: "Published papers must have DOI"
require_arxiv_for_preprints:
condition: "source_type == 'preprint'"
require: "identifiers.arxiv OR identifiers.url"
message: "Preprints must have arXiv ID or URL"
# Example frontmatter
examples:
- ref_id: "REF-001"
title: "Production-Grade Agentic AI Workflows"
short_title: "Production Agentic"
authors:
- name: "Anonymous Authors"
affiliation: "Industry Research"
year: 2024
source_type: preprint
identifiers:
arxiv: "2512.08769"
url: "https://arxiv.org/abs/2512.08769"
keywords:
- agentic AI
- production systems
- best practices
categories:
- production_systems
- multi_agent_systems
key_findings:
- finding: "Nine best practices for production agentic workflows"
metric: "Qualitative patterns"
impact: high
aiwg_relevance:
applicability: direct
components_affected: [agents, flows, rules]
implementation_priority: round-2
related_issues: ["#110"]
quality_assessment:
grade_baseline: moderate
downgrade_factors: ["not_peer_reviewed"]
final_grade: moderate
analysis_date: "2026-01-25"
# References
references:
research:
- "@.aiwg/research/findings/REF-056-fair-principles.md"
implementation:
- "#105"
related:
- "@agentic/code/frameworks/sdlc-complete/schemas/research/quality-assessment.yaml"