moatmri
Skill by sickn33 · updated · ★ 48k
Analyze AI disruption pressure across a business, map competitive exposure, and produce a 90-day defensive action plan. moatmri is published by sickn33 on GitHub.
Install moatmri
Built from the published package, never by AIgit clone --depth 1 https://github.com/sickn33/agentic-awesome-skills /tmp/agentic-awesome-skills && mkdir -p ~/.claude/skills && cp -r /tmp/agentic-awesome-skills/skills/moatmri ~/.claude/skills/moatmriRun in your terminal. Claude Code loads skills from ~/.claude/skills automatically.
git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills /tmp/agentic-awesome-skills && mkdir -p ~/.gemini/antigravity-cli/skills && cp -r /tmp/agentic-awesome-skills/skills/moatmri ~/.gemini/antigravity-cli/skills/moatmriAntigravity CLI loads skills from ~/.gemini/antigravity-cli/skills, or .agents/skills for one project.
Skills can run scripts on your machine. Some need an API key: check the README.
Last commit in the past day
MIT: permissive, fine for commercial use
Clear description, installs from GitHub, 48,424 stars, original repository
No risky patterns in the files we checked
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Security check
ANo download-and-run commands, hidden instructions or signs of sending keys out were found.
Checked automatically: skills/moatmri/SKILL.md. This looks for known risky patterns and is not a full audit. How grades work
Inside the skill
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SKILL.md4 KB
Alternatives to moatmri
All alternatives →Use after a Codex or Claude Code feature has been implemented in Qwen Code to run the selected reference agent and Qwen Code under the same scenario, capture HTTP and terminal traces, compare request bodies, tool/function schemas, outputs, and iterate until the reproduced behavior is close enough.
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
Trigger: new skills, agent instructions, documenting AI usage patterns. Create LLM-first skills with valid frontmatter.
Capture deterministic Capacitor application screens with Vishot. Use when the selected Vishot target is a Capacitor app and Codex must capture its locally served WebView at mobile dimensions or distinguish WebView evidence from native-shell evidence.
Generates a structured Workable Spec JSON to guide a Developer Worker.
Guide agent-driven parameter optimization for configurable systems with measurable objectives. Use for HPO, inference tuning, simulations, or RL/control experiments.
moatmri: common questions
How do I install moatmri?
Run the command above to copy the skill into ~/.claude/skills/. Claude Code picks it up automatically.
Is moatmri free?
The code is published under the MIT licence. The service it connects to may have its own pricing.
Is moatmri safe to use?
It scores 91 of 100. Grades: maintenance A, licence A, quality A. Security check: grade A, no risky patterns found in the files checked. Review the code before you install. How grades work.
What are alternatives to moatmri?
Similar skills: agent-reproduce-align, app-builder, skill-creator, use-vishot-with-capacitor.
