Prompt Optimizer

Turn whatever the user has — an existing prompt, a rough thought dump, or nothing at all — into a prompt tuned for their target model, effort level, and application. Never tune for a specific model from memory: the per-model behavioral deltas live in references/ and change every generation. Route first, then write.

Workflow

1. Classify the starting material. Existing prompt → diagnose before rewriting (see Common defects below); preserve the user's domain knowledge and hard-won constraints. Thought dump → extract goal, audience, constraints, success criteria; reorganize, don't summarize away. Nothing → interview first.
2. Establish the target. Four facts, inferred where possible, asked in ONE batch (AskUserQuestion in Claude Code) only for what's missing: model, application (Claude Code / Cowork / claude.ai chat / Deep Research / API or custom agent), effort or thinking setting, intent (what success looks like, who consumes the output, one-off vs reusable). If the user doesn't know model/effort, recommend from references/models-and-effort.md instead of asking them to decide blind.
3. Load references per the routing tables below. Read every file the target matches before writing.
4. Build or rewrite applying the universal guidelines, then layer the model-specific and application-specific patterns from the loaded references. Copy the references' verbatim prompt snippets where they fit — they are tested language.
5. Deliver: the prompt in a fenced block (with {{PLACEHOLDER}}s for reusable prompts), one-line recommended settings (model, effort/thinking, placement), and 3–6 "what changed and why" bullets for rewrites. Offer one iteration round: if the output misses, ask for the actual output and diagnose rather than guessing.

Universal guidelines (all current Claude models)

• Clear and direct. The golden rule: show the prompt to a colleague with minimal context — if they'd be confused, Claude will be too. Want above-and-beyond behavior? Ask for it explicitly.
• Give the reason, not only the request. State why the task matters, who it's for, what the output enables. Claude generalizes correctly from motivation ("output is read by TTS, so no ellipses" beats "NEVER use ellipses").
• Make success measurable. Concrete output format, length, audience, acceptance criteria.
• Structure with XML tags when mixing instructions, context, examples, or variable inputs. Long documents (20k+ tokens) go at the TOP, query at the bottom; wrap docs in <document> tags and ask for grounding quotes first.
• 3–5 examples when format or tone is hard to describe: relevant, diverse (edge cases, no unintended patterns), wrapped in <example> tags. Claude follows examples over contradicting instructions.
• Say what to do, not what to avoid. "Write flowing prose paragraphs" beats "don't use markdown." Match your prompt's own style to the desired output style.
• Prefer general instructions over prescriptive steps. "Think thoroughly about edge cases" beats a hand-written reasoning plan. Current models need intent and boundaries, not enumerated micro-instructions — over-prescription degrades output (only Haiku-class models want the opposite: explicit steps, tighter format specs, more examples).
• Effort is the primary capability/cost lever — recommend an effort setting alongside the prompt; per-model recommendations differ (see routing).
• Cut everything that doesn't earn its tokens. Obsolete scaffolding actively hurts: anti-laziness nudges, "CRITICAL: you MUST" emphasis, prescribed step-by-step reasoning, prefill hacks, budget_tokens thinking configs, forced interim status messages, and show-your-reasoning instructions (refusal risk on Fable 5).
• Measure any prompting change against real outputs or evals rather than assuming it helped.

Routing — model axis (always load the matching file)

Routing — application axis (load in addition to the model file)

Worked routing examples: "Fable 5 in Claude Code driving an ultracode fleet" → fable-5.md (all long-run sections) + platforms.md § Claude Code + best-practices.md § Agentic systems. "Opus 5 deep research in the Claude app" → opus-5.md (deliverable length, over-verification) + deep-research.md.

Common defects in existing prompts

• Missing motivation — rules with no why.
• Buried instructions — critical constraints mid-wall-of-text; move to top/bottom or a tagged section.
• Conflicting rules — accumulated patches; resolve, don't stack.
• Negative-only formatting rules — replace with positive instructions.
• Obsolete scaffolding — see the cut-list in Universal guidelines; also legacy verification steps (harmful on Opus 5) and forced-status-update scaffolding (current models update well unprompted).
• Example drift — examples contradicting instructions; Claude follows the examples.
• Wrong-generation tuning — prompt tuned for an older model's failure modes; check the target model file's migration notes before preserving any workaround.