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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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).
/validation-pipeline# 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
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.
Choose the appropriate pipeline based on the user's request:
| Pipeline | Steps | Use Case |
|---|---|---|
requirements | 13 | Requirements documents, PRDs, user stories |
Additional pipelines can be added to .claude/helpers/validation-pipeline.cjs.
The pipeline helper (.claude/helpers/validation-pipeline.cjs) handles execution:
--continue-on-failure)| # | Step ID | Category | Severity | What It Checks |
|---|---|---|---|---|
| 1 | format-check | format | blocking | Headings, required sections, document length |
| 2 | completeness-check | content | blocking | Required fields populated, acceptance criteria present |
| 3 | invest-criteria | quality | warning | Independent, Negotiable, Valuable, Estimable, Small, Testable |
| 4 | smart-acceptance | quality | warning | Specific, Measurable, Achievable, Relevant, Time-bound |
| 5 | testability-score | quality | warning | Can each requirement be tested? |
| 6 | vague-term-detection | content | info | Flags "should", "might", "various", "etc." |
| 7 | information-density | content | info | Every sentence carries weight, no filler |
| 8 | traceability-check | traceability | warning | Requirements-to-tests mapping exists |
| 9 | implementation-leakage | quality | warning | Requirements don't prescribe implementation |
| 10 | domain-compliance | compliance | info | Alignment with domain model |
| 11 | dependency-analysis | traceability | info | Cross-requirement dependencies identified |
| 12 | bdd-scenario-generation | quality | warning | Can generate Given/When/Then for each requirement |
| 13 | holistic-quality | compliance | blocking | Overall coherence, no contradictions |
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
...
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}"
| Parameter | Type | Default | Description |
|---|---|---|---|
pipeline | string | requirements | Pipeline type to run |
file | string | required | Path to document to validate |
--steps | string | all | Comma-separated step IDs to run (e.g., format-check,completeness-check) |
--continue-on-failure | boolean | false | Skip blocking gates |
--json | boolean | false | Output as JSON instead of markdown |
--metadata | object | {} | Additional context for steps |
pipeline_validate toolThe 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;
}
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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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。