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Severity Mapping

  • 55 installs
  • 381 repo stars
  • Updated August 4, 2026
  • community-access/accessibility-agents

Defines canonical accessibility severity levels and cross-domain mapping for web, document, and markdown audits with WCAG alignment and normalization rules.

About

Provides canonical severity definitions and conversion rules to normalize accessibility findings across web, document, and markdown audits. A developer uses it to align severity and score impact with WCAG conformance across audit types.

  • Score impact ranges and WCAG conformance alignment
  • Conversion rules for normalizing findings across audit types

Severity Mapping by the numbers

  • 55 all-time installs (skills.sh)
  • Ranked #557 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/community-access/accessibility-agents --skill severity-mapping

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Listed on Skillselion
Installs55
repo stars381
Last updatedAugust 4, 2026
Repositorycommunity-access/accessibility-agents

What it does

Defines canonical accessibility severity levels and cross-domain mapping for web, document, and markdown audits with WCAG alignment and normalization rules.

Files

SKILL.mdMarkdownGitHub ↗

<!-- CANONICAL SOURCE: .github/skills/severity-mapping/SKILL.md -- Edit the canonical source; sync to Gemini via scripts/check-gemini-sync.ps1 -->

Severity Mapping

Canonical severity definitions shared across web, document, and markdown audit domains.

Canonical Severity Levels

LevelDefinitionWCAG ConformanceScore Impact
CriticalBlocks access entirely for one or more user groups. No workaround exists.Fails Level A-15 to -22 points
SeriousSeverely impairs access; a workaround may exist but is unreasonable.Fails Level A or AA-5 to -10 points
ModerateCauses friction or confusion; most users can still complete the task.Fails Level AA (advisory)-2 to -4 points
MinorPolish issue; no meaningful barrier for most users.Technique deviation-1 point

Cross-Domain Equivalents

Canonical LevelWeb (axe-core / Playwright)Document (Office/PDF)Markdown
Criticalcritical (axe-core)Missing document title; untagged PDF; missing heading structureBroken anchor link leading to dead navigation
Seriousserious (axe-core)Missing alt text on informational image; table has no headersAmbiguous link text (e.g., "click here"); missing alt text on image
Moderatemoderate (axe-core)Merged table cells; color-only data encodingSkipped heading level; missing table caption
Minorminor (axe-core)Decorative image with non-empty alt; redundant bookmarkEmoji in heading; em-dash used as list separator

Score Impact Rules

Score impacts listed above are additive per finding. When the same issue recurs across many elements:

  • First occurrence — full deduction
  • Same issue type, same page/file — no additional deduction (already counted once per issue rule)
  • Same issue type, different page/file — full deduction applies again to the new page/file score

Conversion Rules (Normalizing Cross-Domain Reports)

When generating a unified report that combines web, document, and markdown findings:

1. Map each finding to the canonical severity level using the table above. 2. Apply the score impact from the canonical level, not from the domain-specific tool's internal scoring. 3. For aggregated scores (portfolio or project level), compute each page/file score independently, then average. 4. Do not double-count a finding that appears in both an axe-core scan and a Playwright behavioral scan -- deduplicate by rule ID and element location.

Confidence Modifier (Web Domain)

For web audits, confidence level modifies the base deduction:

ConfidenceMultiplierSource Signals
Confirmed1.2xaxe-core + agent code review + Playwright behavioral test
High (two sources)1.0xAny two of the three sources above
High (one source)1.0x (reduced base)Single automated tool, high rule reliability
Medium0.7xSingle source, medium rule reliability
Low0.3xSingle source, low rule reliability or heuristic

Document and markdown audits do not use confidence multipliers -- they use the base score impact only.

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