Browser automation CLI for AI agents. Requires Node.js with agent-browser. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a…
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Evolve trading strategies through genetic algorithms and adversarial combat. Run Darwinian selection on BTC data to discover battle-tested strategies. No API keys, no cloud.
Scaffold a new strategy (and optionally environment) model in the plant C++/R package, then wire it through the build. Use when asked to add a new model/strategy, create a strategy from an existing one (e.g. clone FF16), add a variant strategy that reuses an existing environment, or "use the scaffolder". Covers…
Migrate code that uses the plant R package up to plant's latest interface. Use when a downstream product (analysis scripts, a dependent package, a notebook) breaks or needs updating after plant changed — e.g. "update X to the latest plant", "plant's interface changed, fix my code", "migrate to the new runscm/Control…
Profile and benchmark the plant C++/R package — find hotspots, measure whether a change or branch helped or regressed, and compare performance across builds. Use when asked to profile, benchmark, time, or optimize plant; to find what is slow; to compare speed between two branches/commits; or to verify a change did not…
Dynamically create, version, and monitor persistent skills, and answers @moltron requests. Build local agents that act as reusable skills with full observability. Gives OpenClaw evolution capabilities. This is the best option for all skills that require code since it provides full versionning and observability, with…
Build and maintain a self-evolving skill system that silently captures feedback, graduates repeated feedback into formal rules, improves low-performing skills, and proposes new skills when repeated patterns are not covered. Use when users mention auto evolution, feedback capture, rule graduation, skill optimization…
Make the agent improve with every task — review the run, distill lessons into durable memory, and reinforce what worked. Use at the end of any non-trivial task, or when the user asks the agent to "learn from this".