optimize-prompts SKILL.md skillA
optimize-prompts from contextjet-ai/awesome-llm-observability is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add contextjet-ai/awesome-llm-observability/optimize-prompts@git:20260705.6b497b1Pins git:20260705.6b497b1 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- contextjet-ai/awesome-llm-observability
- Path
skills/optimize-prompts/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.6b497b1, committed Jul 5, 2026
- Size
- 3,900 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this to improve a prompt systematically instead of hand-tweaking it by feel. Trigger on "optimize my prompt", "make this prompt better", "the prompt isn't working well", "auto-tune my prompt", "few-shot example selection", or when prompt quality has plateaued. Optimize against an eval set with a method, and let the numbers pick the winner.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Optimize prompts (with a method, not vibes) Hand-tuning a prompt and eyeballing one output tops out fast and quietly overfits to the last example you looked at. Systematic prompt optimization treats the prompt as something you search over, scored by an eval set. ## Prerequisite: you need an eval set You cannot optimize what you cannot measure. Build an eval set first (`build-eval-dataset`) and wire up scoring (`add-llm-evals`). The optimization loop is: propose a prompt variant, score it on the eval set, keep the winner. Everything below is a smarter way to propose variants. ## The levers, cheapest first 1. **Instructions.** Clarify the task, add constraints, specify the output format. The highest-leverage and cheapest change. 2. **Few-shot examples.** Adding 2-5 good examples often beats a longer instruction. *Which* examples matters a lot; select them from your eval/production data, and measure (more examples is not always better, and they cost input tokens). 3. **Output structure.** Ask for structured output (JSON/enum) when you need reliability; add a short reasoning step before the answer when quality needs it. …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/optimize-prompts@sha256:edc8d3e787829cd1The line at the top of this page pins the label git:20260705.6b497b1, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_bze43oysxkvjgr56)
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Versions
The registry has recorded 1 version of this file, committed on Jul 5, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (3900 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
contextjet-ai/awesome-llm-observability · 45 stars · license a license file GitHub does not identify · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_bze43oysxkvjgr56 GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/optimize-prompts GET https://markdownregistry.com/api/v1/blob/edc8d3e787829cd1420fa98c93c133c170d95ec303f104c903cd028a28a37958
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.