dspy-reasoning-modules — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited dspy-reasoning-modules (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Choose the appropriate DSPy reasoning module for long-context exploration, code-assisted reasoning, or parallel execution.
| Module | Use it for | Important constraint |
|---|---|---|
dspy.RLM | Exploring very large contexts with iterative REPL code and recursive sub-LM calls | Experimental; requires Deno by default |
dspy.ProgramOfThought | Solving tasks by generating and executing Python | Requires Deno by default |
dspy.CodeAct | Combining generated Python with predefined tool functions | Functions only; requires Deno |
dspy.Parallel | Running (module, example) pairs concurrently | Tune threads and error handling |
RLM treats long inputs as external data in a sandbox rather than placing the full context in each LM prompt.
import dspy
dspy.configure(lm=dspy.LM("openai/gpt-4o"))
rlm = dspy.RLM(
"document, question -> answer",
max_iterations=12,
max_llm_calls=30,
sub_lm=dspy.LM("openai/gpt-4o-mini"),
)
result = rlm(
document=very_long_document,
question="What were the main revenue drivers?",
)
print(result.answer)Use max_iterations, max_llm_calls, and max_output_chars as explicit cost and output bounds.
The default dspy.PythonInterpreter uses Deno and Pyodide. It denies host filesystem, environment, and network access unless explicitly enabled.
from pathlib import Path
import dspy
with dspy.PythonInterpreter(
enable_read_paths=[Path("./inputs")],
enable_network_access=["api.example.com"],
) as interpreter:
print(interpreter.execute("print('ready')"))Grant only the minimum paths, environment variables, and network hosts needed by the task.
import dspy
dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
math = dspy.ProgramOfThought("question -> answer")
print(math(question="What is the sum of the first 100 integers?").answer)Use CodeAct when generated code also needs curated host-side tools:
def lookup_rate(currency: str) -> float:
"""Return a trusted exchange rate from the application service."""
return rates[currency]
agent = dspy.CodeAct("amount, currency -> converted", tools=[lookup_rate])parallel = dspy.Parallel(num_threads=8, return_failed_examples=True)
results, failed_examples, exceptions = parallel(
[(program, {"question": question}) for question in questions]
)Predict or ChainOfThought until code execution or long-context exploration is justified.RLM as experimental and load-test before production deployment.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.