Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OmidZamani/dspy-skills --skill dspy-reasoning-modulesgit clone --depth 1 https://github.com/OmidZamani/dspy-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-reasoning-modules)<a href="https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-reasoning-modules"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-reasoning-modules/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-reasoning-modules"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-reasoning-modules.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.00844 |
| Opus 5 | $0.00018 | $0.00422 |
| Sonnet 5 | $0.00007 | $0.00169 |
| Haiku 4.5 | $0.00004 | $0.00084 |
Grade A, and why
dspy-reasoning-modules scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSPy Reasoning Modules
Goal
Choose the appropriate DSPy reasoning module for long-context exploration, code-assisted reasoning, or parallel execution.
Module Selection
| 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 for Large Contexts
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.
Sandboxed Execution
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.
ProgramOfThought and CodeAct
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)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 116 lines · 36 tokens per session scan A 912dac9cb176
dspy-reasoning-modules is a skill published in the GitHub repository OmidZamani/dspy-skills (123 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 844 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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