prompt-optimization
Skill0 starsChanged 6 months agoNo licenceView on GitHub
Systematic prompt quality improvement via DSPy (trace-guided compilation), TextGrad (text-space gradient descent), OPRO (meta-prompting), ProTeGi (error-case textual gradients), and APE (candidate generation). Use when a multi-step LLM pipeline degrades, a specific output quality must compound across iterations, or you have labeled training data and need prompts that maximize a metric. Distinct from evaluator-optimizer (single critique loop on one generation) and autoresearch (arbitrary metric hill-climbing over code or configs).
- Installs packages
2 other projects use a version of this.
Adopted by
Most recently taken up by
- shinpr/rashomontheir own version, first seen Nov 1, 2026
- stagewise-io/stagewisetheir own version, first seen Oct 8, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- stagewise-io/stagewise★ 6.8ksame name
- shinpr/rashomon★ 20same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository219 other files it gives its agents.
- agents/AGENTS.mdAGENTS.md
- AGENTS.mdAGENTS.md
- codex-agents/AGENTS.mdAGENTS.md
- memory-bank/AGENTS.mdAGENTS.md
- copilot-instructions.mdCopilot instructions
- .github/copilot-instructions.mdCopilot instructions
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The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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