Agent skill · browserbase
optimize-agent-prompt
Builds and improves Browserbase Agent API demos through an Autobrowse-style outer loop: run a fixed task, collect Agent messages and session logs, score the result, revise one system-prompt heuristic, and confirm convergence. Use when creating a Browserbase Agents demo or POC, optimizing an Agent system prompt, diagnosing flaky Agent runs, or applying auto-research/autobrowse to the Browserbase Agents API.
What it needs
About 3k tokens when loaded.
What this skill does
Optimize Agent Prompt Optimize a Browserbase Agent's systemPrompt while holding its task, result schema, variables, and evaluation criteria fixed. Treat the outer agent as the teacher and each Browserbase Agent run as an inner-agent rollout. Use Node.js 18 or later and set BROWSERBASEAPIKEY. The harness uses only Node.js built-in modules. Set up the experiment Choose a short experiment name and create an isolated workspace inside the demo or POC repository: Edit the generated files: task.json: keep task, resultSchema, variables, browser settings, and evaluation oracle stable across iterations. prompts/iteration-001.md: write the minimal baseline system prompt. Include irreversible-action guardrails when applicable. Use concrete success criteria. Prefer a strict JSON Schema with required fields and null for unavailable facts. Add known-field regexes and factuality-warning regexes under evaluation when a truth oracle exists. Read references/evaluation.md when designing the task or score. Run the baseline The harness creates one reusable Browserbase Agent, updates its systemPrompt on later iterations, starts the run, polls messages and status, and writes: It stops a run after the configured message budget instead of paying for an unproductive spiral. Use --max-messages, --timeout-ms, --proxies, or --verified only when the task needs different values from task.json. Diagnose from observable evidence Start with the compact trajectory: Then read summary.json and drill into messages.json at the first wrong or wasted turn. Agent messages expose ordered tool calls, tool results, errors, and final output. A reasoning part may contain no readable text; never require hidden chain-of-thought for the teacher loop. Read session-logs.json only when browser-level evidence can distinguish the cause—for example, a redirect, 403, failed request, console error, or hidden endpoint. Empty session logs can mean the Agent completed with search/fetch tools and never drove its browser. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills browserbase/optimize-agent-prompt