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Process documents and codebases exceeding a single context window using canonical dspy.RLM variable mode in the Daytona REPL.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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.
Fetch and inspect JavaScript-heavy pages with Playwright in a Daytona browser-capable snapshot.
| name | long-context |
| description | Process documents and codebases exceeding a single context window using canonical dspy.RLM variable mode in the Daytona REPL. |
Official references:
dspy.RLM stores large inputs as REPL variables (document_text, context_paths, history, …). The model sees only metadata (name, type, length, preview) and explores with Python:
print(document_text[:2000]) or print(len(document_text)) to peek.re, or open(path) on context_paths to locate relevant sections.llm_query(snippet) or llm_query_batched([...]) on focused excerpts — never the full document.SUBMIT(answer=...).execution_mode=auto routes to large_context_rlm when estimated context ≥ FLEET_RLM_LARGE_CONTEXT_THRESHOLD (default 32_000 chars).context_paths REPL variables with context_manifest metadata.sub_rlm(text) delegates to an isolated child sandbox for heavy map-reduce (see delegation skill).When semantic boundaries matter before delegation:
scripts/semantic_chunk.py — split by structure (markdown, logs, Python, JSON).scripts/rank_chunks.py — rank chunks against the query.Chunking complements dspy.RLM; it does not replace REPL inspection.
llm_query on an entire large variable; slice first.max_llm_calls and max_output_chars; print summaries, not raw dumps.load_skill("long-context") when mounted at /home/daytona/memory/.When the user asks for a verbatim quote or speaker attribution:
SUBMIT — not a numbered list of quotes.document_text with Python search, then slice the typographic quote span verbatim.context_paths in the sandbox.