Alexgreensh Agent Skills
8 skills published by alexgreensh across 4 repositories. Together they weigh 21 423 tokens — that is what loading all of them at once would cost you in context.
8 skills 21 423 tokens total
Answer in the ADHD-friendly Attention-kind style for the rest of this chat.
Answer in the Rundown briefing style (TL;DR + checklist + numbered choices).
Answer in the terse, zero-warmth Spartan style for the rest of this chat.
Compress a document, thread, transcript, or pasted text into a scannable TL;DR.
Code-drawn illustrations, animations and scored films in nine hand-drawn styles, zero assets, identical every render. Use for hero art, explainers, stickers, loops, animated logos, storyboards or a short film.
| Cross-agent self-inspection of your AI-agent stack. Audits skills, MCP servers, hooks, plugins, commands, credentials, and memory files across Claude Code, Codex, OpenClaw, and NanoClaw. Produces a structured inventory and narrative briefing with cross-ecosystem risk analysis. Use when the user asks to audit their own setup, check what they have installed, review their agent stack security posture, or understand cross-tool interactions. Use when a user has accumulated skills/plugins/MCP servers over time and wants visibility into their attack surface. Use after installing new skills or plugins. Do NOT use for vetting external code before install (that is repo-forensics). Do NOT use for incident response during active attacks. Do NOT use for fixing or patching vulnerabilities (forensify is read-only).
Security forensics for git repos, AI skills, and MCP servers. Audits dependencies, detects prompt injection, credential theft, runtime dynamism, manifest drift, known CVEs, CISA KEV (actively exploited) vulns, and 2026 attack patterns. Not for fixing vulnerabilities or pentesting.
Interactive copilot that offloads grunt work to a cheaper engine while Claude orchestrates. Greets each session with your live token limits and a driving mode (auto-pilot / you-drive / hybrid), auto-conserves when your Claude window runs hot, and picks models by live benchmarks. Triggers: outsource, offload, delegate, conserve tokens, use GLM/Devin/Codex, or get a second opinion.