Skill UI

This skill creates, revises, or audits AGENTS.md files based on repository evidence and verified commands. It ensures coding agents understand the workflow without rediscovering it, preserving maintainer intent. It builds evidence maps from manifests and CI, scopes instructions correctly, and validates changes before handoff to prevent mistakes.

Enables agents to perform authorized security assessments, blue team exercises, and penetration testing on AI products, agents, and infrastructure. Uses first-principles reasoning to identify vulnerabilities like prompt injection and tool abuse, generating evidence-based security reports without relying solely on payload libraries.

Analyzes AI-assisted coding sessions to identify root causes of scope drift and rework. Evaluates prompt sufficiency, classifies change types, and provides evidence-backed recommendations for improving future workflows and prompt engineering.

This skill assists in resisting manipulation, manufactured urgency, and authority appeals before responding. It separates evidence from persuasion tactics, identifies deception patterns, and ensures honest behavior under pressure. It provides a structured integrity scaffold for critical decision-making and validates claims without unsupported assertions.

This skill is designed to prevent AI coding assistants from exhibiting sycophantic agreement bias. When active, it forces a structured, critical assessment of user claims. The process involves extracting the core claim, assessing it independently with evidence (for/against), and concluding solely based on that assessment, ensuring objective and evidence-based responses.

This skill reviews repository architecture using Ontoly Software Graph and MCP capabilities. It helps explain architecture, module boundaries, package topology, service ownership, and architectural risks. It utilizes capabilities like ExplainArchitecture, GraphStatistics, FindCycles, and FindDependencies to provide evidence-based analysis for developers.

Capture and retrieve durable YYLO Ledger artifact Records with intentional profiles, payload modes, provenance, retention, and secret-safe immutable evidence.

This skill enables independent auditing of AI agent, gateway, or automation workflow runs. It reconstructs events from traces, checkpoints, and tool calls to verify success claims without trusting self-reports. Users establish contracts, build claim ledgers, normalize evidence, rank witnesses, and assign verdicts like proven or contradicted. It ensures integrity in multi-step automation and separates autonomous success from human-assisted outcomes.

Audit and rewrite prose to detect and fix AI-generated writing patterns such as vague wording, hedging, hollow intensifiers, and formulaic phrases while preserving meaning, evidence, and the writer's voice.

This skill reconstructs the detailed workflow behind a specific coding artifact (like a feature or PR). By analyzing local `ax` agent traces, it connects commits, sessions, skills, and tool calls into a concise, evidence-grounded narrative. It is used when needing to understand the step-by-step process and key decisions that led to a result.

Automates the babysitting of pull/merge requests through bot review rounds: verify findings, fix issues, reply to threads, and resolve discussions. Handles GitHub and GitLab, correlates inline threads and review bodies, and ensures every loop is closed with evidence before publishing.

An evidence-first procedure for system performance debugging, root-cause analysis and incident reports. Applies Brendan Gregg's USE and TSA methods to sweep resources and thread states, then drills down with off-CPU analysis and flame graphs, ending in triage notes, RCA reports or blameless postmortems.