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-finetune-bootstrapgit 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-finetune-bootstrap)<a href="https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-finetune-bootstrap"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-finetune-bootstrap/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-finetune-bootstrap"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-finetune-bootstrap.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 | $0.00035 | $0.01825 |
| Opus 5 | $0.00017 | $0.00912 |
| Sonnet 5 | $0.00007 | $0.00365 |
| Haiku 4.5 | $0.00003 | $0.00183 |
Grade A, and why
dspy-finetune-bootstrap 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSPy BootstrapFinetune Optimizer
Goal
Distill a DSPy program into fine-tuned model weights for efficient production deployment.
When to Use
- You have a working DSPy program with a large model
- Need to reduce inference costs
- Want faster responses (smaller model)
- Deploying to resource-constrained environments
Inputs
| Input | Type | Description |
|---|---|---|
program |
dspy.Module |
Teacher program to distill |
trainset |
list[dspy.Example] |
Training examples |
metric |
callable |
Validation metric (optional) |
train_kwargs |
dict |
Training hyperparameters |
Outputs
| Output | Type | Description |
|---|---|---|
finetuned_program |
dspy.Module |
Program with fine-tuned weights |
model_path |
str |
Path to saved model |
Workflow
Phase 1: Prepare Teacher Program
import dspy
# Configure with strong teacher model
dspy.configure(lm=dspy.LM("openai/gpt-4o"))
class TeacherQA(dspy.Module):
def __init__(self):
self.cot = dspy.ChainOfThought("question -> answer")
def forward(self, question):
return self.cot(question=question)
Phase 2: Configure Fine-Tuning
Assign the LM directly to predictors before fine-tuning:
import dspy
from dspy.teleprompt import BootstrapFinetune
optimizer = BootstrapFinetune(
metric=lambda gold, pred, trace=None: gold.answer.lower() in pred.answer.lower(),
train_kwargs={
'learning_rate': 5e-5,
'num_train_epochs': 3,
'per_device_train_batch_size': 4,
'warmup_ratio': 0.1
}
)
Phase 3: Fine-tune Student Model
teacher = TeacherQA()
teacher.set_lm(dspy.settings.lm)
finetuned = optimizer.compile(teacher, trainset=trainset)
Phase 4: Deploy
# Save the fine-tuned model (saves state-only by default)
finetuned.save("finetuned_qa_model.json")
# Load and use (must recreate architecture first)
loaded = TeacherQA()
loaded.load("finetuned_qa_model.json")
result = loaded(question="What is machine learning?")
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 · 254 lines · 35 tokens per session scan A 4a6866192006
dspy-finetune-bootstrap is a skill published in the GitHub repository OmidZamani/dspy-skills (123 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 1,825 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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