Results for “frontmatter-validation”

Validates custom dotnet new templates for correctness before publishing, catching missing fields, parameter bugs, shortName conflicts, constraint issues, and common authoring mistakes.

Plan structure validation, phase completeness checks, reference integrity verification, and artifact existence confirmation. Provides the structured verification layer ensuring GSD artifacts are well-formed and complete.

Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated agents.

Blocks Edit/Write/Bash actions until the agent presents concrete facts about imports, data schemas, and the user's instruction, improving output quality.

Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated agents.

Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated agents.

VQAv2: Making the V in VQA Matter

Uses mutation testing to systematically identify gaps in test vector coverage for cryptographic algorithms, then generates new test vectors that close those gaps. Measures effectiveness by comparing mutation kill rates before and after.

Chameleon: Mixed-Modal Early-Fusion Foundation Models

This skill should be used when the user asks to "validate plugin structure", "review manifest files", "check frontmatter compliance", "verify tool invocation patterns", "explain plugin component types", or needs Claude Code plugin architectural guidance.

Validate financial data quality in market analysis documents before publication, checking price scales, instrument notation, date accuracy, allocation totals, and unit usage.

Pixtral 12B: A Frontier Multimodal Model

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

Enforces a verification gate before any completion claim, requiring fresh command output as evidence.

Verifies data structures, files, YAML blocks, or agent output against their defined schema, producing a pass/fail report with actionable fix instructions.

Enforces running verification commands and confirming output before claiming work is complete, fixed, or passing, ensuring evidence precedes any success claims.

Centralized JSON validation for AGENT_SUCCESS_CRITERIA with defensive parsing and injection attack prevention (CVSS 8.2)

Verification-before-completion discipline ensuring all success criteria are met, tests pass, and reviews complete before declaring work done.

Verify the article’s factual, product, citation, and link integrity before visual production.

Mathematical AI interpretability with sheaf cohomology, spectral analysis, causal inference, and hallucination prevention

Verify tasks marked [X] in tasks.md are implemented, not phantom completions (marked done but backed by missing or dead code).

Forces AI agents to investigate before editing or running destructive commands, improving code quality by requiring concrete facts like importers, data schemas, and user instructions.

Validate Pleasur.ai Stage 01 blog-keyword candidates with the Business potential, Intent, and Difficulty method using current Ahrefs evidence. Use before AIO vetting and prioritization to reject poor business fits, mismatched search intent, and SERPs the brand cannot realistically win.

Runs bottom-up type-chain validation across database, hooks, and UI layers, then fixes errors at the failing layer for project health checks before large changes.

Imported skill __init__ from anthropic

Multimodal Few-Shot Learning with Frozen Language Models

Execute HTTP Parameter Pollution attacks to bypass input validation, WAF rules, and security controls by injecting duplicate parameters that are processed differently by front-end and back-end systems.

Validates observable behavior of apps, CLIs, APIs, and generated artifacts against a prewritten contract without inspecting source code.

Use in TURBO mode before finalizing implementation, architecture, UI/UX, 3D web, mobile, SEO/PPC/growth, or programming work.

A validation *boundary* is any point where data crosses from a system you do not

Validate backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to ensure backups are reliable for disaster recovery and ransomware response scenarios.

Verifica la reproducibilidad y consistencia de un análisis de datos antes de presentarlo, validando contra la pregunta original y documentando limitaciones.

Measures distributional similarity between original GAN-generated images and their semantically manipulated counterparts using the Fréchet Inception Distance (FID) metric.

This skill should be used when the user asks to "run example" or "test example skill".