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-evaluation-suitegit 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-evaluation-suite)<a href="https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-evaluation-suite"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-evaluation-suite/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-evaluation-suite"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-evaluation-suite.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 | $0.00033 | $0.01880 |
| Opus 5.5 | $0.00013 | $0.00752 |
| Sonnet 5.5 | $0.00007 | $0.00376 |
| Haiku 4.5 | $0.00003 | $0.00188 |
Grade A, and why
dspy-evaluation-suite 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 1mo 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSPy Evaluation Suite
Goal
Systematically evaluate DSPy programs using built-in and custom metrics with parallel execution.
When to Use
- Measuring program performance before/after optimization
- Comparing different program variants
- Establishing baselines
- Validating production readiness
Related Skills
- Use with any optimizer: dspy-bootstrap-fewshot, dspy-miprov2-optimizer, dspy-gepa-reflective
- Evaluate RAG pipelines: dspy-rag-pipeline
Inputs
| Input | Type | Description |
|---|---|---|
program |
dspy.Module |
Program to evaluate |
devset |
list[dspy.Example] |
Evaluation examples |
metric |
callable |
Scoring function |
num_threads |
int |
Parallel threads |
Outputs
| Output | Type | Description |
|---|---|---|
score |
float |
Average metric score |
results |
list |
Per-example results |
Workflow
Phase 1: Setup Evaluator
from dspy.evaluate import Evaluate
evaluator = Evaluate(
devset=devset,
metric=my_metric,
num_threads=8,
display_progress=True
)
Phase 2: Run Evaluation
result = evaluator(my_program)
print(f"Score: {result.score:.2f}%")
# Access individual results: (example, prediction, score) tuples
for example, pred, score in result.results[:3]:
print(f"Example: {example.question[:50]}... Score: {score}")
Built-in Metrics
answer_exact_match
import dspy
# Normalized, case-insensitive comparison
metric = dspy.evaluate.answer_exact_match
SemanticF1
LLM-based semantic evaluation:
from dspy.evaluate import SemanticF1
semantic = SemanticF1()
score = semantic(example, prediction)
Custom Metrics
Basic Metric
def exact_match(example, pred, trace=None):
"""Returns bool, int, or float."""
return example.answer.lower().strip() == pred.answer.lower().strip()
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.
- 1mo ago First seen · 291 lines · 33 tokens per session scan A a5c9cef1fe2c
dspy-evaluation-suite is a skill published in the GitHub repository OmidZamani/dspy-skills (124 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 1,880 once invoked, about $0.0001 per session on Opus 5.5. 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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