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AI/MLjmagly

grade-on-ingest

Trigger GRADE quality assessment automatically when new research sources or findings enter the corpus

Stars
141
Source
jmagly/aiwg
Updated
2026-05-31
Slug
jmagly--aiwg--grade-on-ingest
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/jmagly/aiwg/HEAD/agentic/code/frameworks/sdlc-complete/skills/grade-on-ingest/SKILL.md -o .claude/skills/grade-on-ingest.md

Drops the SKILL.md into .claude/skills/grade-on-ingest.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

GRADE-on-Ingest

Automatically triggers GRADE quality assessment when new research sources or findings are added to the corpus.

Triggers

Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):

  • "GRADE" → evidence quality rating framework
  • "quality of evidence" → GRADE assessment
  • "evidence level" → source quality grading

Purpose

Ensures every research source entering the corpus receives a GRADE quality assessment at ingestion time, preventing unassessed sources from being cited without quality context. Implements the "assess at entry" pattern to maintain corpus-wide quality visibility.

Activation Conditions

This skill activates when:

  1. New file created in .aiwg/research/sources/ or .aiwg/research/findings/
  2. File pattern matches: REF-*.md, *.pdf added to research directories
  3. Agent activity: Any agent writes to research corpus directories
  4. Manual trigger: User requests source assessment

Skip Conditions

  • File is in .aiwg/research/quality-assessments/ (already an assessment)
  • File is INDEX.md or README.md
  • File is a schema or template (*.yaml in schemas/)
  • Assessment already exists for this REF-ID

Behavior

When a new research source is detected:

  1. Extract metadata

    • Parse YAML frontmatter from source document
    • Extract ref_id, title, authors, year, source_type
    • If frontmatter missing, prompt agent to add it
  2. Determine baseline quality

    • Map source type to GRADE baseline:
      • peer_reviewed_journal -> HIGH
      • peer_reviewed_conference -> HIGH
      • preprint -> MODERATE
      • technical_report -> MODERATE
      • industry_whitepaper -> LOW
      • blog_post -> VERY LOW
      • forum_discussion -> VERY LOW
  3. Invoke Quality Assessor

    • Delegate to Quality Assessor agent for full GRADE assessment
    • Pass source metadata and content
    • Request assessment in YAML format
  4. Store assessment

    • Save to .aiwg/research/quality-assessments/{ref-id}-assessment.yaml
    • Update source frontmatter with grade_level field (if --update-frontmatter)
  5. Update corpus index

    • Add entry to quality assessment index
    • Update GRADE distribution statistics
    • Flag if corpus has > 30% unassessed sources
  6. Report

    • Display assessment summary to user
    • Include hedging language recommendations
    • Warn if source quality is LOW or VERY LOW

Agent Orchestration

  • Primary: Quality Assessor (performs the assessment)
  • Supporting: Citation Verifier (validates existing citations of this source after assessment)
  • Notification: Technical Writer, Documentation Synthesizer (if source is cited in existing docs, notify of GRADE level)

Integration

With Citation Guard

After assessment completes, Citation Guard uses the GRADE level to enforce hedging:

integration:
  citation_guard:
    action: update_grade_cache
    data: new_assessment

With Research Metadata

Assessment populates fields required by research metadata rules:

integration:
  research_metadata:
    fields_populated:
      - quality_assessment.grade_level
      - quality_assessment.baseline
      - quality_assessment.downgrade_factors
      - quality_assessment.upgrade_factors

With Provenance Tracking

Assessment activity recorded in provenance chain:

integration:
  provenance:
    activity_type: quality_assessment
    agent: quality-assessor

Configuration

skill:
  name: grade-on-ingest
  type: passive
  always_active_for:
    - quality-assessor
    - technical-researcher
    - citation-verifier
  file_triggers:
    - pattern: ".aiwg/research/sources/REF-*.md"
    - pattern: ".aiwg/research/findings/REF-*.md"
  auto_assess: true
  update_frontmatter: false  # Requires --update-frontmatter flag
  notify_on_low_quality: true
  block_on_missing_frontmatter: false

Output Locations

  • Assessment: .aiwg/research/quality-assessments/{ref-id}-assessment.yaml
  • Updated frontmatter: Source document (if --update-frontmatter)
  • Index update: .aiwg/research/quality-assessments/INDEX.md

References

  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/agents/quality-assessor.md - Assessment agent
  • @.aiwg/research/docs/grade-assessment-guide.md - GRADE methodology
  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/schemas/research/quality-dimensions.yaml - Quality schema
  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/rules/research-metadata.md - Metadata requirements
  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/rules/citation-policy.md - Citation policy
  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/skills/citation-guard/SKILL.md - Citation guard