skills/ getsentry/skills

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, o

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Prompt Optimizer

Optimize prompts with evals. Keep every instruction, example, and external context reference causal.

Load Only What You Need

NeedRead
New promptreferences/core-patterns.md, references/model-family-notes.md, references/transformed-examples.md
Existing promptreferences/meta-optimization-loop.md, references/core-patterns.md, references/model-family-notes.md
Model-family portreferences/model-family-notes.md, references/core-patterns.md
Repeated failuresreferences/meta-optimization-loop.md, references/core-patterns.md
Weak or ambiguous draftreferences/transformed-examples.md
ProvenanceSOURCES.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 typeExamples
Agent rulesAGENTS.md, CLAUDE.md
Specsspecs/*.md, docs/api.md
PoliciesSECURITY.md, docs/releasing.md
Examplesexamples/, 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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