prompt-optimizer
Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals.
What this skill does
# Prompt Optimizer Optimize prompts with evals. Keep every instruction, example, and external context reference causal. ## Load Only What You Need | Need | Read | |------|------| | New prompt | `references/core-patterns.md`, `references/model-family-notes.md`, `references/transformed-examples.md` | | Existing prompt | `references/meta-optimization-loop.md`, `references/core-patterns.md`, `references/model-family-notes.md` | | Model-family port | `references/model-family-notes.md`, `references/core-patterns.md` | | Repeated failures | `references/meta-optimization-loop.md`, `references/core-patterns.md` | | Weak or ambiguous draft | `references/transformed-examples.md` | | Provenance | `SOURCES.md` | ## Step 1: Capture Contract Record before editing: - task type: new, refine, port, or debug - target model family and snapshot, if known - prompt surface: `system`, `developer`, `user`, tool descriptions, examples, schemas - layer owners: platform, deployer/persona, retrieved context, user payload - objective and non-goals - inputs, tools, and external files available - required output shape - success criteria and failure cases - hard constraints: latency, verbosity, safety, budget, tool use, style If success criteria or examples are missing, create a small eval set first. If the bottleneck is model choice, retrieval, tool schema, or missing evals, say so before rewriting. ## Step 2: Inventory External Context For repo or agent prompts, list stable context by exact path: | Context type | Examples | |--------------|----------| | Agent rules | `AGENTS.md`, `CLAUDE.md` | | Specs | `specs/*.md`, `docs/api.md` | | Policies | `SECURITY.md`, `docs/releasing.md` | | Examples | `examples/`, `tests/fixtures/` | Rules: - Reference stable files by repo-relative path instead of copying them. - Paste only excerpts needed for the prompt or eval case. - Mark whether a file is `loaded`, `referenced`, or `out of scope`. - Avoid vague context pointers such as "read the docs". ## Step 3: Choose Model Strategy Read `references/model-family-notes.md`. - Known family: optimize for that family. - Unknown family: write a portable base plus short adapter notes. - Snapshot changes: rerun evals. - Cross-family divergence: specialize only the failing layer. ## Step 4: Shape Prompt Read `references/core-patterns.md`. - Put stable policy in `system` or `developer`. - Put task-local facts, retrieved context, and variables in user-facing sections. - Keep one owner per behavior rule. - Use headings or tags only to separate content types. - Put tool policy in prompt text; keep schemas in provider-native tools. - Keep persona light unless it changes behavior. - Use the shortest wording that preserves the constraint. - Cut filler, repeated reminders, dead examples, and rationale that does not affect evals. ## Step 5: Optimize Read `references/meta-optimization-loop.md` for refinements. 1. Baseline the current prompt on the same eval slice. 2. Cluster failures by root cause. 3. Write concrete edit criticisms. 4. Generate two to four candidates: - minimal-diff repair - structure-first rewrite - examples-first or tool-rule variant - provider adapter when needed 5. Compare candidates on the same cases. 6. Keep a short optimization log. 7. Validate the winner on holdout cases. 8. Stop on plateau, oscillation, overfit, excessive cost, or non-prompt bottleneck. ## Step 6: Return Package Return: 1. `Target` 2. `Success Criteria` 3. `External Context` 4. `Optimized Prompt` 5. `Adapter Notes` 6. `Eval Set` 7. `Optimization Log` 8. `Residual Risks` For existing prompts, include a concise diff-style note of the main behavioral changes. ## Failure Modes - editing before defining the eval target - mixing policy, examples, and raw context without boundaries - duplicating rules across layers - putting durable policy in user payloads - asking for chain-of-thought - keeping contradictory legacy instructions - overfitting to one or two examples - retaining examples that no longer improve evals - fixing tool-use failures only in prompt text when tool descriptions or schemas are weak - adding markup that does not reduce ambiguity - using persona as a substitute for behavior rules
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