Kb Web Severity Scoring logo

Kb Web Severity Scoring

Organization
Community-Access
kb-web-severity-scoring

Reference data, not a reviewer. Compute web accessibility scores (0-100, A-F grades) with severity scoring, confidence levels, and remediation tracking across audits.

Overview

PublisherCommunity-Access
Repositoryaccessibility-agents
Skill namekb-web-severity-scoring
Stars
429
Forks
51
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Community-Access on GitHub. Read the source before you install it.

Installation

Install the Kb Web Severity Scoring AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Community-Access/accessibility-agents/tree/main/skills/kb-web-severity-scoring
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Community-Access/accessibility-agents.git /tmp/accessibility-agents
mkdir -p .claude/skills
cp -r /tmp/accessibility-agents/skills/kb-web-severity-scoring .claude/skills/kb-web-severity-scoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kb Web Severity Scoring in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kb Web Severity Scoring on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kb Web Severity Scoring is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Web Severity Scoring

Severity Scoring Formula

text
Page Score = 100 - (sum of weighted findings)

Weights:
  Critical (confirmed, all three sources):   -18 points
  Critical (high confidence, both sources):  -15 points
  Critical (high confidence, single source): -10 points
  Critical (medium confidence):               -7 points
  Critical (low confidence):                  -3 points
  Serious (high confidence):                  -7 points
  Serious (medium confidence):                -5 points
  Serious (low confidence):                   -2 points
  Moderate (high confidence):                 -3 points
  Moderate (medium confidence):               -2 points
  Moderate (low confidence):                  -1 point
  Minor:                                      -1 point

Floor: 0 (minimum score)

Scoring Profiles

Use a profile to tune strictness by context while keeping comparable grade bands:

ProfileIntended UseMultiplier
balanced (default)Standard product delivery1.0
strictRegulated/public-sector releases1.15
advisoryEarly design and prototyping0.8

Apply the profile multiplier to each final deduction after confidence handling.

Formula

pseudocode
page_score = 100
for each finding:
    base = lookup(severity, confidence_level, source_count)  // from table above
    multiplier = 1.2 if confidence_level == "confirmed" else 1.0
    deduction = base × multiplier
    page_score = max(0, page_score - deduction)

The values in the lookup table above are base deductions (pre-multiplier). "Confirmed" findings (validated by all three sources: axe-core + agent review + Playwright) apply an additional 1.2× multiplier.

Example: One Critical finding at confirmed confidence = 18 (base) × 1.2 = 21.6 points deducted → page score 78.

Calibration Layer (v2)

To reduce false-positive inflation and stabilize trends, apply a calibration coefficient by rule family:

text
calibrated_deduction = deduction × calibration_coefficient(rule_family)

Recommended initial coefficients:

Rule FamilyCoefficientRationale
Keyboard/focus1.1High functional impact at runtime
Forms/labels/errors1.05High completion risk for core tasks
Semantics/structure1.0Baseline scoring
Link text/context0.9Higher context variance
Content quality (alt/link clarity)0.85Needs human review more often

Update coefficients quarterly from confirmed outcomes. Avoid changing coefficients more than +/-0.1 per cycle.

Score Grades

Each score, with its grade and meaning.

ScoreGradeMeaning
90-100AExcellent - minor or no issues, meets WCAG AA
75-89BGood - some issues, mostly meets WCAG AA
50-74CNeeds Work - multiple issues, partial WCAG AA compliance
25-49DPoor - significant accessibility barriers
0-24FFailing - critical barriers, likely unusable with AT

Confidence Levels

Each level, with weight and when to use.

LevelWeightWhen to Use
Confirmed120%Validated by all three sources: axe-core + agent review + Playwright behavioral testing
High100%Confirmed by axe-core + agent, or definitively structural (missing alt, no labels, no lang)
Medium70%Found by one source, likely issue (heading edge cases, questionable ARIA, possible keyboard traps)
Low30%Possible issue, needs human review (alt text quality, reading order, context-dependent link text)

Source Correlation

Issues found by both axe-core AND agent review are automatically upgraded to high confidence regardless of individual confidence ratings.

Issues found by all three sources (axe-core + agent review + Playwright behavioral testing) are upgraded to confirmed confidence with a 1.2x weight multiplier. This applies when:

  • axe-core reports a violation
  • Agent code review identifies the same issue
  • Playwright behavioral scan confirms the issue at runtime (e.g., keyboard trap confirmed by actual Tab traversal, contrast failure confirmed by rendered CSS computation)

When Playwright is not available, the maximum achievable confidence remains High (100%). The confirmed tier is additive — it never downgrades findings.

Confidence Drift Guard

Track predicted confidence versus post-triage outcome and compute drift:

text
drift = abs(predicted_confidence_score - observed_confirmation_rate)

Operational guideline:

  • drift <= 0.10: stable
  • drift 0.11-0.20: tune coefficients and source mapping
  • drift > 0.20: freeze profile changes and run rule-level review

Scorecard Format

Single Page

markdown
## Accessibility Score

| Metric | Value |
|--------|-------|
| Page | [URL] |
| Score | [0-100] |
| Grade | [A-F] |
| Critical | [count] |
| Serious | [count] |
| Moderate | [count] |
| Minor | [count] |

Multi-Page

markdown
## Accessibility Scorecard

| Page | Score | Grade | Critical | Serious | Moderate | Minor |
|------|-------|-------|----------|---------|----------|-------|
| / | 82 | B | 0 | 2 | 3 | 1 |
| /login | 91 | A | 0 | 0 | 2 | 1 |
| /dashboard | 45 | D | 2 | 4 | 3 | 2 |
| **Average** | **72.7** | **C** | **2** | **6** | **8** | **4** |

Cross-Page Pattern Classification

Each pattern type, with its definition and remediation ROI.

Pattern TypeDefinitionRemediation ROI
SystemicSame issue on every audited pageHighest - usually layout/nav, fix once
TemplateSame issue on pages sharing a componentHigh - fix the shared component
Page-specificUnique to one pageNormal - fix individually

Remediation Tracking

Change Classification

Each status, with its definition.

StatusDefinition
FixedIssue was in previous report but no longer present
NewIssue not in previous report, appears now
PersistentIssue remains from previous report
RegressedIssue was previously fixed but has returned

Progress Metrics

  • Issue reduction: (fixed / previous_total) * 100
  • Score change: current_score - previous_score
  • Pages improved: count of pages with higher scores than previous audit
  • Trend: improving (score up 5+), stable (within 5), declining (score down 5+)

Normalized Trend Metric (Cross-Audit)

When audit scope changes between runs, use normalized change:

text
normalized_score = raw_score - (scope_variance_penalty)
scope_variance_penalty = min(10, abs(previous_pages - current_pages) * 0.8)

Use normalized score for trend charts and use raw score for release gates.

Output Metadata (Recommended)

Include these fields in generated score artifacts for reproducibility:

yaml
scoring:
  model: web-severity-scoring-v2
  profile: balanced
  calibrationVersion: 2026-q2
  confidenceSources:
    - axe-core
    - agent-review
    - playwright
  failThresholds:
    critical: 1
    score: 75

This metadata allows deterministic re-runs and audit-to-audit comparisons.

Issue Severity Categories

Critical

  • No keyboard access to essential functionality
  • Missing form labels on required fields
  • Images conveying critical information have no alt text
  • Color is the sole means of conveying information
  • Keyboard traps with no escape

Serious

  • Missing skip navigation
  • Poor heading hierarchy (skipped levels)
  • Focus not visible on interactive elements
  • Form errors not programmatically associated
  • Missing ARIA on custom widgets

Moderate

  • Redundant ARIA on semantic elements
  • Suboptimal heading structure (multiple H1s)
  • Missing autocomplete on identity fields
  • Links to new tabs without warning
  • Missing table captions

Minor

  • Redundant title attributes
  • Suboptimal button text
  • Missing landmark roles where semantic elements exist
  • Decorative images with non-empty alt text

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Kb Web Severity Scoring AI skill do?

Reference data, not a reviewer. Compute web accessibility scores (0-100, A-F grades) with severity scoring, confidence levels, and remediation tracking across audits.

Why use Kb Web Severity Scoring on TypingMind?

Because you install it once and use it with any model. Kb Web Severity Scoring is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kb Web Severity Scoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Community-Access/accessibility-agents/tree/main/skills/kb-web-severity-scoring. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Kb Web Severity Scoring?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kb Web Severity Scoring?

As many as you like. As long as a model supports skills, you can use Kb Web Severity Scoring with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kb Web Severity Scoring AI skill free?

Yes. It is published on GitHub by Community-Access under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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