| name | rlm |
| description | Hub skill for fleet-rlm: when to use dspy.RLM vs ReAct/CodeAct and which workflow skill to load next. |
fleet-rlm RLM Hub
Canonical DSPy module: https://dspy.ai/api/modules/RLM/
Module variants overview: https://dspy.ai/diving-deeper/built-in-module-variants/
When to use dspy.RLM
Use RLM when context is too large, unevenly relevant, or benefits from programmatic exploration — not a single full-context LM call.
fleet-rlm routes to dspy.RLM when:
execution_mode is rlm / rlm_only- Auto mode detects URL document analysis (
url_document_rlm)
- Auto mode detects large context (
large_context_rlm, threshold via FLEET_RLM_LARGE_CONTEXT_THRESHOLD)
Other turns are classified by the typed RouteTurnSignature router: direct (ChainOfThought), tools (dspy.ReAct loop), or rlm (sandboxed Python).
REPL contract (Daytona interpreter)
- Large fields are REPL variables; explore with
print() and Python.
- Built-ins:
llm_query, llm_query_batched, SUBMIT(...).
- Fleet extensions:
sub_rlm, sub_rlm_batched, volume load_skill(name).
- Durable storage:
/home/daytona/memory/ (skills/system/*.md seeded from scaffold).
Route to a workflow skill
- Sandbox / REPL / SUBMIT / volume lifecycle →
sandbox-execution
- Recursive child sandboxes / budgets →
delegation
- Signatures / modules / execution modes →
dspy-programs
- Large documents / variable mode →
long-context
- GEPA / offline optimization →
optimization
- Failures / contract drift →
diagnostics
- Volume layout / CRUD →
volume-bootstrap
- Playwright / rendered pages →
browser-interaction
