agent-booster
WASM-based instant code transforms for simple tasks, achieving 352x speedup over LLM inference with zero cost.
npx skills add a5c-ai/babysitter --skill agent-booster --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
- High-frequency repetitive modifications - When latency is critical (<1ms requirement) - Cost-sensitive batch operations ## Supported Transforms | Transform | Description | Example | |-----------|-------------|---------| | `var-to-const` | Modernize variable declarations | `var x = 1` -> `const x = 1` | | `add-types` | Insert TypeScript annotations | `function f(x)` -> `function f(x: string)` | | `add-error-handling` | Wrap in try/catch | Bare calls -> try/catch blocks | | `async-await` | Convert Promise chains | `.then().catch()` -> `async/await` | | `extract-function` | Extract code blocks | Inline code -> named function | | `inline-variable` | Inline single-use variables | Remove intermediate vars | | `add-jsdoc` | Generate documentation | Bare functions -> JSDoc comments | ## Performance - Execution: <1ms per transform - Cost: $0 (no LLM invocation) - Speedup: 352x compared to LLM inference - Confidence threshold: >90% pattern match required ## Agents Used - `agents/coder/` - Fallback for unmatched patterns ## Tool Use Invoke via babysitter process: `methodologies/ruflo/ruflo-task-routing`
- Supported Transforms
- Performance
- Agents Used
- Tool Use
What does the agent-booster skill do?
WASM-based instant code transforms for simple tasks, achieving 352x speedup over LLM inference with zero cost.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill agent-booster --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.