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-rag-pipelinegit 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-rag-pipeline)<a href="https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-rag-pipeline"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-rag-pipeline/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-rag-pipeline"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-rag-pipeline.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 | $0.00037 | $0.01819 |
| Opus 5 | $0.00018 | $0.00910 |
| Sonnet 5 | $0.00007 | $0.00364 |
| Haiku 4.5 | $0.00004 | $0.00182 |
Grade A, and why
dspy-rag-pipeline 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 13d 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 — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSPy RAG Pipeline
Goal
Build retrieval-augmented generation pipelines with ColBERTv2 that can be systematically optimized.
When to Use
- Questions require external knowledge
- You have a document corpus to search
- Need grounded, factual responses
- Want to optimize retrieval + generation jointly
Related Skills
- Optimize this pipeline: dspy-miprov2-optimizer, dspy-bootstrap-fewshot
- Build local semantic retrieval: dspy-embedding-retrieval
- Evaluate results: dspy-evaluation-suite
- Design signatures: dspy-signature-designer
Inputs
| Input | Type | Description |
|---|---|---|
question |
str |
User query |
k |
int |
Number of passages to retrieve |
rm |
dspy.Retrieve |
Retrieval model (ColBERTv2) |
Outputs
| Output | Type | Description |
|---|---|---|
context |
list[str] |
Retrieved passages |
answer |
str |
Generated response |
Workflow
Phase 1: Configure Retrieval
import dspy
# Configure LM and retriever
colbert = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
dspy.configure(
lm=dspy.LM("openai/gpt-4o-mini"),
rm=colbert
)
Phase 2: Define Signature
class GenerateAnswer(dspy.Signature):
"""Answer questions with short factoid answers."""
context: list[str] = dspy.InputField(desc="May contain relevant facts")
question: str = dspy.InputField()
answer: str = dspy.OutputField(desc="Often between 1 and 5 words")
Phase 3: Build RAG Module
class RAG(dspy.Module):
def __init__(self, num_passages=3):
super().__init__()
self.retrieve = dspy.Retrieve(k=num_passages)
self.generate = dspy.ChainOfThought(GenerateAnswer)
def forward(self, question):
context = self.retrieve(question).passages
pred = self.generate(context=context, question=question)
return dspy.Prediction(context=context, answer=pred.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.
- 13d ago First seen · 253 lines · 37 tokens per session scan A 56e3b3f7c056
dspy-rag-pipeline is a skill published in the GitHub repository OmidZamani/dspy-skills (123 stars, last pushed 2mo ago), licensed MIT. It adds 37 tokens to every session and 1,819 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.
Other skills, from other repositories
molecular-rag
Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).
rag-retrieval
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.
AI & LLM Security
LLM and AI application security testing — prompt injection, jailbreak resistance, OWASP LLM Top 10 (2025), RAG and agent/tool-use security, model supply chain, and AI red teaming for authorized assessments.
dspy
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming.
dspy
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming.
dspy
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming.