Skip to content
cccskills
FreePublished on GitHub

validation-pipeline

Runs multi-stage validation gates with per-step scoring, pass/fail verdicts, and aggregate quality reports. Use when validating requirements, code, or artifacts through structured gate enforcement before merge or release.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515 stars(GitHub 515 + cccskills 0)

Run score Not tested

See how to install

Files included (4)

  • SKILL.md6.3 KB
  • evals/validation-pipeline.yaml16.1 KB
  • schemas/output.json5.9 KB
  • scripts/validate-config.json730 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

Validation Pipeline

Purpose

Run structured validation pipelines that execute steps sequentially, enforce gates at blocking failures, and produce scored reports. Uses .claude/helpers/validation-pipeline.cjs with 13 requirements validation steps (BMAD-003).

Activation

  • When validating requirements documents
  • When running structured quality gates
  • When assessing document completeness, testability, or traceability
  • When invoked via /validation-pipeline

Quick Start

# Validate a requirements document (all 13 steps)
/validation-pipeline requirements docs/requirements.md

# Validate with specific steps only
/validation-pipeline requirements docs/requirements.md --steps format-check,completeness-check,invest-criteria

# Continue past blocking failures
/validation-pipeline requirements docs/requirements.md --continue-on-failure

# Output as JSON
/validation-pipeline requirements docs/requirements.md --json

Workflow

Step 1: Read the Target Document

Read the file specified by the user. If no file is provided, ask for one.

Read the target document using the Read tool.
Store the content for pipeline execution.

Step 2: Select Pipeline

Choose the appropriate pipeline based on the user's request:

PipelineStepsUse Case
requirements13Requirements documents, PRDs, user stories

Additional pipelines can be added to .claude/helpers/validation-pipeline.cjs.

Step 3: Execute Pipeline

The pipeline helper (.claude/helpers/validation-pipeline.cjs) handles execution:

  1. Sequential execution — steps run in order, each receiving results from prior steps
  2. Gate enforcement — blocking steps that fail halt the pipeline (unless --continue-on-failure)
  3. Per-step scoring — each step produces a 0-100 score with findings and evidence
  4. Weighted rollup — overall score uses category weights (format=10%, content=30%, quality=25%, traceability=20%, compliance=15%)

Requirements Pipeline Steps (13 total)

#Step IDCategorySeverityWhat It Checks
1format-checkformatblockingHeadings, required sections, document length
2completeness-checkcontentblockingRequired fields populated, acceptance criteria present
3invest-criteriaqualitywarningIndependent, Negotiable, Valuable, Estimable, Small, Testable
4smart-acceptancequalitywarningSpecific, Measurable, Achievable, Relevant, Time-bound
5testability-scorequalitywarningCan each requirement be tested?
6vague-term-detectioncontentinfoFlags "should", "might", "various", "etc."
7information-densitycontentinfoEvery sentence carries weight, no filler
8traceability-checktraceabilitywarningRequirements-to-tests mapping exists
9implementation-leakagequalitywarningRequirements don't prescribe implementation
10domain-compliancecomplianceinfoAlignment with domain model
11dependency-analysistraceabilityinfoCross-requirement dependencies identified
12bdd-scenario-generationqualitywarningCan generate Given/When/Then for each requirement
13holistic-qualitycomplianceblockingOverall coherence, no contradictions

Step 4: Report Results

Format the pipeline result as a structured report:

# Validation Report: Requirements Pipeline

**Overall**: PASS/FAIL/WARN | **Score**: 85/100 | **Duration**: 42ms

## Step Results
| # | Step | Status | Score | Findings | Duration |
|---|------|--------|-------|----------|----------|
| 1 | Format Check | PASS | 100 | 0 | 2ms |
| 2 | Completeness | WARN | 60 | 2 | 5ms |
...

## Blockers
- (blocking findings listed here)

## All Findings
- [HIGH] Missing acceptance criteria: Requirement US-104 has no AC
- [MEDIUM] Vague term: "should" used 5 times without specifics
...

Step 5: Record Learning

After pipeline execution, record the outcome for learning:

// Store validation pattern
memory store --namespace validation-pipeline --key "req-validation-{timestamp}" --value "{score, findings_count, halted}"

Parameters

ParameterTypeDefaultDescription
pipelinestringrequirementsPipeline type to run
filestringrequiredPath to document to validate
--stepsstringallComma-separated step IDs to run (e.g., format-check,completeness-check)
--continue-on-failurebooleanfalseSkip blocking gates
--jsonbooleanfalseOutput as JSON instead of markdown
--metadataobject{}Additional context for steps

Integration Points

  • qe-requirements-validator agent — delegates structured validation to this pipeline
  • qe-quality-gate agent — uses pipeline for gate evaluation
  • YAML Pipelines — can invoke validation steps as workflow actions
  • MCP — accessible via pipeline_validate tool

Output Schema

The pipeline produces a PipelineResult object (see schemas/output.json):

{
  pipelineId: string;
  pipelineName: string;
  overall: 'pass' | 'fail' | 'warn';
  score: number;           // 0-100 weighted average
  steps: StepResult[];     // per-step details
  blockers: Finding[];     // blocking findings
  halted: boolean;
  haltedAt?: string;       // step ID where halted
  totalDuration: number;
  timestamp: string;
}

Error Handling

  • Step throws exception — captured as a FAIL with critical finding, pipeline continues or halts per severity
  • File not found — report error, do not run pipeline
  • Empty document — format-check step will catch this as a blocking failure

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

See all skills by proffesor-for-testing

Report a problem with this skill