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-optimizer-selectiongit 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-optimizer-selection)<a href="https://agentmods.dev/skills/omidzamani/dspy-skills/dspy-optimizer-selection"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-optimizer-selection/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-optimizer-selection"><img src="https://agentmods.dev/badge/skills/omidzamani/dspy-skills/dspy-optimizer-selection.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 | $0.00048 | $0.00880 |
| Opus 5 | $0.00024 | $0.00440 |
| Sonnet 5 | $0.00010 | $0.00176 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
dspy-optimizer-selection 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSPy Optimizer Selection
Goal
Choose the smallest DSPy optimizer that matches the data, budget, and artifact being tuned. Establish a baseline before compiling anything.
Selection Matrix
| Need | Start with | Notes |
|---|---|---|
| Include a few labeled examples | dspy.LabeledFewShot |
Random labeled demos; useful as a baseline |
| About 10 examples | dspy.BootstrapFewShot |
Teacher-generated demos with metric filtering |
| 50+ examples and stronger demo search | dspy.BootstrapFewShotWithRandomSearch |
Searches multiple demo sets; alias: dspy.BootstrapRS |
| Per-input nearest demos | dspy.KNNFewShot |
Retrieves nearby examples before bootstrapping |
| Instruction-only hill climbing | dspy.COPRO |
Coordinate ascent over instructions |
| Instruction and demo search | dspy.MIPROv2 |
Bayesian search; install dspy[optuna] |
| Mini-batch introspective rules or demos | dspy.SIMBA |
Uses output variability and self-reflection |
| Rich textual feedback and trace reflection | dspy.GEPA |
Metric must accept five arguments |
| Distill prompts into model weights | dspy.BootstrapFinetune |
Requires a fine-tunable LM and set_lm() |
| Combine candidate programs | dspy.Ensemble |
Trades inference cost for robustness |
| Sequence prompt and weight optimization | dspy.BetterTogether |
Meta-optimizer for configurable optimizer chains |
Workflow
- Split data into train and validation sets.
- Evaluate the uncompiled program with dspy-evaluation-suite.
- Start with the least expensive optimizer that matches the need.
- Save the compiled program and compare it against the baseline.
- Escalate only when the measured gain justifies extra LM calls, fine-tuning, or inference cost.
Common Paths
Fast Demo Optimization
Use dspy-bootstrap-fewshot for the first optimization pass. Move to BootstrapFewShotWithRandomSearch when enough examples are available to search multiple demo sets.
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 · 80 lines · 48 tokens per session scan A f502f7ed4ed1
dspy-optimizer-selection is a skill published in the GitHub repository OmidZamani/dspy-skills (123 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 880 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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