Skill UI

This tool verifies the consistency and integrity of the Architecture Decision Records (ADR) graph, which is populated by `adr-index`. It checks for common data corruption issues such as dangling references (broken links), supersedure cycles (circular dependencies), and status mismatches. It is ideal for post-import validation and use as a strict CI/CD gate.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This skill utilizes learned patterns and the current state to predict the most optimal next action in a complex, multi-step workflow. It guides the execution flow by checking prediction confidence: high confidence leads to immediate execution, while low confidence triggers a fallback to task list prioritization. Crucially, it continuously learns from completed tasks to update patterns, ensuring improved, self-correcting automation across sessions.

A rigorous, data-driven methodology for improving system performance. Use this skill when aiming to significantly increase speed, reduce latency, or lower operational cost. It establishes a measurable baseline, generates testable variants based on hypotheses, runs controlled benchmarks, and validates the best performing solution through iterative testing and code promotion gates.

This skill builds an active voice profile by extracting stylistic patterns, rhythms, and signatures from genuine source materials—such as posts, essays, and site copy. Instead of relying on generic AI tropes, it creates a reusable VOICE PROFILE block, ensuring consistent brand messaging across all content, social media, and outreach campaigns.

A comprehensive skill set for senior transportation managers overseeing a large, multi-modal carrier portfolio. It covers the full lifecycle of carrier engagement, including sourcing, compliance checks (FMCSA SAFER), executing complex RFPs, negotiating granular rate components (linehaul, FSC, accessorials), and establishing performance scorecards. Use this skill to optimize logistics costs, mitigate supply chain risks, and ensure consistent service quality across varied freight types.

This skill guides the definition of a defensible and decision-relevant competitor set. It is the foundational step before any benchmarking, helping users identify, categorize, and scope competitors (Direct, Adjacent, Aspirational) based on criteria like size, niche, market reach, and positioning. It ensures that strategic analysis focuses only on the most relevant rivals.

Implements a robust caching strategy by using the SHA-256 hash of file content as the primary key, instead of the file path. This pattern is essential for high-cost file processing pipelines (like OCR, PDF parsing) because it ensures cache hits survive file renaming or moving, while still automatically invalidating entries when the content changes. It adheres to the Single Responsibility Principle (SRP) by isolating caching logic into a service layer wrapper.

This pattern defines a robust, continuous loop structure for autonomous agents. It integrates crucial components like quality gates, evaluation harnesses, and explicit recovery controls. It guides complex workflows by defining decision paths (e.g., PR control, RFC decomposition) and includes strategies to mitigate common failure modes such as infinite loop churn or cost drift, ensuring measurable progress in autonomous systems.

This tool performs robust outlier detection on individual session spending. Utilizing Median and Median Absolute Deviation (MAD), it calculates a modified z-score, making it highly resistant to extreme values, unlike mean+sigma methods. It specifically identifies which singular sessions are anomalous, making it crucial for cost monitoring, auditing, and integrating into CI/CD pipelines to prevent unexpected resource overruns.

A comprehensive pattern for building robust and cost-efficient LLM application pipelines. This guide details combining model routing based on task complexity, immutable cost tracking, limited retry logic, and prompt caching to manage API expenditures while maintaining high quality. Essential for production systems handling variable-complexity, high-volume tasks.