oc-prompt-ops

Topics in the source

• Prompt Ops
• /oc-prompt — Command Reference
• Executable evaluation commands
• How This Skill Fits the Build Pipeline
• Principle 1: Prompt-as-Code
• Principle 2: Eval Datasets (the goldset)
• Grading modes
• Avoiding train/eval leakage

On first invocation, read references/orchestrator.md and follow its welcome protocol. Treat prompts as code: source-controlled, diffable, semver'd, and gated on an eval suite the same way application code is gated on tests. A prompt is the single most behavior-defining string in an LLM app — and the one most teams edit live, in a console, with no version history and no way to tell whether "it got better" is real or vibes. This skill makes a prompt change a reviewable diff with a measured score delta attached. This is not a tri-agent harness. It's an operations layer the model-facing skills build on: oc-claude-api owns the request surface (model routing, caching, tool wiring), oc-agent-forge owns agent topology, oc-rag-forge owns retrieval. Prompt Ops owns the part underneath all three — the prompt text, the eval datasets that score it, and the regression gate that stops a "small wording tweak" from quietly tanking quality on a migration. opchain dogfoods this skill on itself. The worked example referenced throughout is prompts/opchain-eval/ — opchain's own eval set (inputs.jsonl, expected.jsonl, eval.yaml), published in Sprint 3 as the canonical /oc-prompt eval artifact. Wherever t

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