/netsi-prompt-optimize — Prompt Optimizer

Turn a weak prompt into a strong one. This skill applies only the levers that were measured to actually improve LLM answers (especially on small local models): specification, verification-with-a-real-signal, and information grounding. It deliberately avoids persona / "imagine you are" / role-relay framing, which added confident-sounding text and even new errors without improving correctness. The reasoning and evidence live in references/principles.md.

Trigger format

/netsi-prompt-optimize [the raw prompt to improve]

Examples:

• /netsi-prompt-optimize explain spirituality to me simply
• "Optimize this prompt: write a function that dedupes an array"
• "My local model gives rambling answers to this prompt — fix it"

If a prompt follows the command (or is pasted/attached), treat it as the input and optimize it directly — don't ask "what's your prompt?" again.

Language

Talk to the user in the language they write in. But note: for small local models, English instructions inside the prompt were the single biggest quality lever. So the optimized prompt itself may be written in English even when chatting in Danish — tell the user why, and honour any explicit "keep it in Danish" request (the output language is then stated as a constraint inside the prompt).

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TASK-000: Detect input and mode

1. Get the raw prompt from the command, pasted text, an attached file, or a task.md. If none is present, ask for it in one line.
2. Decide whether verification applies. Verification (a critic→revise pass) only helps when there is a real signal to check against — otherwise a self-judging critic invents "missing" requirements and makes the answer worse (this happened in testing). A real signal means one of:
   • Reference material the user supplied (facts, docs, a spec) → check the answer against it.
   • A verifiable task — code that can be run/tested, structured output that can be validated against a schema, a math result that can be checked. If neither is present, do not run a critic loop. Optimize the prompt, answer it, and stop.

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TASK-001: Optimize the prompt (always)

Rewrite the raw prompt into a precise, self-contained prompt that:

1. States the task unambiguously — one clear instruction, no vague verbs.
2. Preserves the user's own constraints exactly. If they said "3 short paragraphs", keep 3. Do not change their numbers and do not invent new constraints (length, format, sections) they never asked for. Adding a limit the user didn't request is only OK when the original had none and it clearly serves them — flag it if you do.
3. Makes the implied audience and reading level explicit and tells the model to match it (e.g. "for a total beginner; plain language, no jargon").
4. Adds a short "Success criteria" checklist derived only from what the task actually asks for.
5. Forbids invented specifics — no made-up names, citations, dates, or biography. If the model is unsure, it should say so plainly.
6. Mentions code hygiene only if the task involves code — then require valid, runnable code in a widely-known language (Python/JavaScript) unless another is specified, and make the task's language requirement override any default.
7. Sets the output language as an explicit constraint when it matters.
8. Contains no persona, no flattery, no grandiosity ("you are a world-class expert" was measured to backfire). Keep it concise.

Present the result as a fenced, copy-pasteable block titled Optimized prompt, followed by 2–4 bullets on what changed and why (anchored to the rules above).

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TASK-002: Answer only after the user confirms

The default deliverable is the optimized prompt — not the answer. After presenting it (TASK-004), stop and ask whether the user wants you to:

• answer the optimized prompt here, or
• run it against a small/local model via the script (TASK-005), or
• nothing — they'll take the prompt and use it themselves.

Do not produce the answer until they choose. Only when they ask you to answer:

1. Answer the optimized prompt. Follow the same quality contract: match the audience, obey the constraints, invent nothing, keep code valid.
2. If — and only if — TASK-000 found a real signal, run one verification pass:
   • Critic (hardened): list only concrete, fixable problems: violations of constraints explicitly stated in the prompt; claims that contradict or are unsupported by the reference; invalid code; made-up specifics. Never invent new requirements (citations, academic rigor, extra sections) the prompt didn't ask for. Output problems, or exactly NO ISSUES.
   • Revise: fix only what the critic flagged; keep the rest. If NO ISSUES, leave the answer unchanged.
3. No signal → no critic loop. Deliver the answer from step 1 as final. Say why ("no reference to verify against, so a self-check would risk inventing problems").

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TASK-003: Never do (measured anti-patterns)

• No persona / role framing. No "act as / imagine you are / world-class expert".
• No role-relay or multi-expert ensembles. Same-model personas share the same blind spots → correlated errors, no gain.
• No self-refine loop without a signal. Extra self-judged passes add confidence, not correctness, and can degrade a correct answer.
• No inventing constraints in the optimized prompt that the user didn't state.

See references/principles.md for the experiments behind each of these.

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TASK-004: Output format

By default, deliver only:

1. Optimized prompt — a fenced block the user can copy and reuse. This is the primary artifact.
2. What changed — a few anchored bullets.
3. One-line offer — ask whether to answer it here, run it on a small/local model (TASK-005), or stop. Then wait.

Only after the user asks you to answer, append:

4. Answer — the response to the optimized prompt.
5. Verification note — either the critic/revise result (if a signal existed) or one line stating it was skipped and why.

Keep it tight. Never jump straight to the answer — the optimized prompt is what this skill returns.

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TASK-005: Running it on a small / local model

For repeatable use against a local model (Ollama) or the Anthropic API, this skill bundles a production script: scripts/claude-optimize-prompt.ts. It does exactly TASK-001→TASK-002 as code (optimize always; critic→revise only when a reference.md/REFERENCE is present) and writes optimize-out.md.

# Local small model
MODEL=llama3.2 deno run -A scripts/claude-optimize-prompt.ts "the raw prompt"

# Factual task with grounding — auto-enables the critic loop
REFERENCE="$(cat facts.md)" deno run -A scripts/claude-optimize-prompt.ts "the raw prompt"

# Anthropic instead of Ollama
PROVIDER=anthropic ANTHROPIC_API_KEY=sk-... deno run -A scripts/claude-optimize-prompt.ts

Env knobs: PROVIDER (ollama|anthropic), MODEL, OLLAMA_URL, TEMPERATURE, VERIFY (auto|on|off), REFERENCE / reference.md.

Offer to run or adapt the script when the user's real target is a small model; otherwise just apply TASK-001→TASK-004 inline yourself.

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TASK-006: Knowledge base priority

1. The user's own prompt and stated constraints come first — never override them.
2. Otherwise apply the principles in references/principles.md.
3. When the target is a small local model, weight English instructions and information grounding highly — they moved quality most in testing.