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-better-togethergit 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-better-together)<a href="https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-better-together"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-better-together/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-better-together"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-better-together.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 | $0.00032 | $0.00775 |
| Opus 5 | $0.00016 | $0.00387 |
| Sonnet 5 | $0.00006 | $0.00155 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
dspy-better-together 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 12d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSPy BetterTogether
Goal
Sequence prompt and weight optimizers, evaluate intermediate programs, and return the best candidate.
Prerequisites
- Use DSPy
3.2.1or later in the stable3.2.xseries. - Assign an LM directly to every predictor with
student.set_lm(lm). - Keep a validation set, or allow
BetterTogetherto hold out part of the trainset. - Confirm the LM provider supports fine-tuning before including
BootstrapFinetune.
Basic Pattern
import dspy
lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)
student = dspy.ChainOfThought("question -> answer")
student.set_lm(lm)
def metric(example, pred, trace=None):
return float(example.answer.lower() == pred.answer.lower())
optimizer = dspy.BetterTogether(
metric=metric,
p=dspy.GEPA(
metric=lambda gold, pred, trace=None, pred_name=None, pred_trace=None:
dspy.Prediction(score=metric(gold, pred), feedback="Check answer correctness."),
reflection_lm=dspy.LM("openai/gpt-4o"),
auto="light",
),
w=dspy.BootstrapFinetune(metric=metric),
)
compiled = optimizer.compile(
student,
trainset=trainset,
valset=valset,
strategy="p -> w -> p",
)
Strategy Choices
| Strategy | Use it when |
|---|---|
"p -> w" |
Start with a simple prompt-then-weight pass |
"p -> w -> p" |
Re-optimize prompts after fine-tuning |
"w -> p" |
Fine-tuning data is already strong |
| Custom chains | Comparing prompt optimizers or conducting controlled experiments |
Optimizer names come from constructor keyword arguments. For example, mipro=... and gepa=... make "mipro -> gepa" valid.
Per-Optimizer Compile Arguments
Pass optimizer-specific arguments through optimizer_compile_args:
compiled = optimizer.compile(
student,
trainset=trainset,
valset=valset,
strategy="p -> w",
optimizer_compile_args={
"p": {"max_metric_calls": 150},
},
)
Do not pass student inside optimizer_compile_args; BetterTogether manages the current program.
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.
- 12d ago First seen · 107 lines · 32 tokens per session scan A 317c84fa86d0
dspy-better-together is a skill published in the GitHub repository OmidZamani/dspy-skills (123 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 775 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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Use when a raw prompt needs rewriting or sharpening, when the user says "rewrite this prompt", "make this prompt better", or asks for a prompt to hand to another agent. Also use before launching any autonomous, long-running, parallel or expensive run (/loop, subagent fan-out, worktrees, overnight work), and whenever a…