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  3. Free Prompt Optimizer for Claude (Fable 5)

AI Toolbox

Free Prompt Optimizer for Claude (Fable 5)

Paste a prompt, choose where you will run it, and get deterministic suggestions grounded in official Claude prompting guidance. Your prompt text never leaves this browser.

Lint runs locally; we do not see your prompt text. These are deterministic heuristics, not a verdict. Use them as a checklist before shipping.

Lint runs locally; we do not see your prompt text.

These are deterministic heuristics, not a verdict. Use them as a checklist before shipping. Future rewrite modes will be opt-in and clearly separate from this local checker.

Where will you run this prompt?

Surface changes which Claude prompting rules apply.

Target model

Approximate token range: 0–0

Run the linter to see severity-tiered findings.

Official snippets library

Copy small, placement-labelled snippets into your system prompt, user message, or harness.

System prompt

Anti-overplanning

Discourage unnecessary planning loops.

Prefer the smallest plan that lets you act safely; update it only when new facts change the path.
  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

System prompt

Anti-overengineering

Keep implementations simple.

Choose the simplest implementation that satisfies the stated constraints and tests.
  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

User message

Brevity

Request concise output.

Answer in the fewest words that preserve the key decision, evidence, and next step.
  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

Harness

Checkpoint

Force progress checks in agent loops.

After each major step, summarize what changed, what was verified, and the next action.
  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

Harness

Grounded progress

Report only verified progress.

Do not claim success until a command, test, or observable artifact verifies it.
  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

System prompt

Autonomy

Permit safe independent action.

When the next step is reversible and within scope, take it without asking.
  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

System prompt

Boundaries

Clarify what not to modify.

Stay within the requested scope; if required information is missing, state the blocker instead of guessing.
  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

Rules this linter checks

Issues

multishot-examples

Add multishot examples

Examples are the most reliable way to steer exact output shape.

Format-sensitive task without examples. Add 3–5 diverse examples in <example> tags.

Citations

  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

long-context-ordering

Move long context before instructions

Long-context prompts are easier to ground when evidence is loaded before the ask.

Large pasted context appears after the task. Put documents first, then the query at the end.

Citations

  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

reasoning-echo

Avoid chain-of-thought echo requests

Echoing hidden reasoning can trigger reasoning-extraction safeguards.

Prompt asks to reveal/transcribe hidden reasoning. Ask for a concise rationale or summarized thinking instead.

Citations

  • Prompting Claude Fable 5 — Fable 5 responds better to concise intent and appropriate effort guidance than behavior over-prescription.

Suggestions

structure-sections

Add explicit sections

Structure helps the model separate source material, task, and expected output.

Long prompt with no section markers. Use <context>, <task>, and <output_format> or clear headings.

Citations

  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

negative-instructions

Rewrite negative instructions

Positive behavioral targets are easier to follow than dense prohibitions.

High density of do-not/never instructions. Say what to do instead of only what to avoid.

Citations

  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

constraint-without-rationale

Explain hard constraints

Rationale improves compliance with unusual constraints.

ALWAYS/NEVER/MUST without nearby rationale. Add because/so that language explaining why the rule matters.

Citations

  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

missing-output-format

Specify output format

Explicit output contracts reduce unusable responses.

Prompt does not state the desired output format. Name the format: JSON, table, markdown, bullets, or exact fields.

Citations

  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

vague-qualifiers

Replace vague quality words

Measurable targets make subjective editing requests actionable.

Vague words such as better/professional without criteria. Use measurable criteria, rubrics, examples, or constraints.

Citations

  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

missing-context-intent

Add context and intent

Audience and purpose help choose tone, depth, and trade-offs.

Task-only prompt lacks audience, goal, or purpose. Use “I am working on X for Y because Z.”

Citations

  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.

caps-emphasis

Normalize all-caps emphasis

Aggressive emphasis can overtrigger modern Claude models.

Excessive CRITICAL/MUST/NEVER style emphasis. Use calm wording and one explicit priority statement.

Citations

  • Prompting Claude Fable 5 — Fable 5 responds better to concise intent and appropriate effort guidance than behavior over-prescription.

Info

fable-overprescription

Simplify Fable behavior steering

Fable is more reliable with concise intent than enumerated behavior laws.

Repeated always/never/must behavior steering. Prefer a short instruction describing the desired behavior.

Citations

  • Prompting Claude Fable 5 — Fable 5 responds better to concise intent and appropriate effort guidance than behavior over-prescription.

fable-effort-guidance

Set effort on supported surfaces

Effort is model/surface-specific and irrelevant in claude.ai.

Complex work on API/Claude Code can benefit from effort guidance. Use high by default; consider max for very complex agentic work. Never set effort for Haiku.

Citations

  • Prompting Claude Fable 5 — Fable 5 responds better to concise intent and appropriate effort guidance than behavior over-prescription.

prompt-bloat

Trim prompt bloat

The concept asks for local token-range feedback; bloat detection turns that estimate into an actionable rule.

Very long prompt appears repetitive or boilerplate-heavy. Keep durable constraints, remove repeated boilerplate, and move reusable policy to system or harness context.

Citations

  • Anthropic prompt engineering guide — Claude performs best when prompts are explicit, structured, and grounded with examples where needed.