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Fetch and inspect JavaScript-heavy pages with Playwright in a Daytona browser-capable snapshot.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Delegate recursive work to child RLM sandboxes with budget management. Use when decomposing tasks into sub-queries, fanning out batched work, managing LLM call budgets, or building parent-child RLM hierarchies.
Diagnose fleet-rlm runtime failures, API contract drift, sandbox errors, and observability issues. Use when something is broken — Daytona connection failures, websocket mismatches, escalation not triggering, budget exhaustion, or missing traces.
Optimize fleet-rlm DSPy programs and RLM skill bundles using GEPA with MLflow tracking. Use when running optimization loops, designing feedback metrics, building training datasets, or comparing runs.
Hub skill for fleet-rlm: when to use dspy.RLM vs ReAct/CodeAct and which workflow skill to load next.
Design DSPy signatures and compose runtime modules for fleet-rlm tasks. Use when creating input/output field definitions, choosing between built-in signatures, selecting execution modes, or wiring custom modules.
Process documents and codebases exceeding a single context window using canonical dspy.RLM variable mode in the Daytona REPL.
| name | browser-interaction |
| description | Fetch and inspect JavaScript-heavy pages with Playwright in a Daytona browser-capable snapshot. |
Use when plain fetch / urllib returns empty or incomplete HTML (SPAs, dynamic docs).
| Approach | Use when |
|---|---|
Python urllib / requests | Static HTML, APIs, raw text downloads |
document_text REPL variable | fleet-rlm already fetched the URL into RLM |
| Playwright (browser snapshot) | Client-rendered pages, need visible text or screenshots |
fleet-rlm selects a browser-capable Daytona snapshot when this skill is active.
# After navigation, always print bounded excerpts for the RLM loop.
print(page.title())
print(page.inner_text("body")[:4000])
artifacts/ path for evidence.llm_query on excerpts, not full page dumps.Bundled instructions are seeded to {volume}/skills/system/browser-interaction.md.
Call load_skill("browser-interaction") from the REPL when the volume is mounted.