prompt-optimize

name	prompt-optimize
description	Analyze and optimize a prompt for the DQIII8 ecosystem. Classifies prompt type (LLM routing, image gen, TTS, agent instruction, Telegram), scores 5 dimensions, produces optimized version.
command	/prompt-optimize
allowed-tools	["Read"]
user-invocable	true

/prompt-optimize — Optimize a Prompt for DQIII8/Pipeline

Analyze and optimize a prompt for maximum effectiveness in the DQIII8 ecosystem (LLM routing, scene director, TTS, Telegram bot, or agent instructions).

Usage

/prompt-optimize [the prompt text or file path]

Your Task

Given the prompt in $ARGUMENTS:

1. Classify the prompt type

• LLM routing prompt (sent to Ollama/Groq/Claude)
• Image generation prompt (sent to fal.ai flux-general)
• TTS prompt (narration text for ElevenLabs)
• Agent instruction (agent .md system prompt)
• Telegram bot message (user-facing output)

2. Evaluate on 5 dimensions (score 0-10 each)

Dimension	What to check
Clarity	Unambiguous intent, no vague instructions
Specificity	Concrete examples, exact formats, numbers
Conciseness	No filler words, no redundant instructions
Output contract	Explicit format (JSON/markdown/text) stated
Edge cases	Handles empty input, failure, ambiguity

3. Produce optimized version

Apply these DQIII8-specific improvements:

For LLM prompts:

• Add Output ONLY JSON (no markdown): if structured output needed
• Include exact field names in the output contract
• State the model tier this will run on (Haiku/Sonnet/Groq)

For image prompts (fal.ai):

• Lead with cinematographer style reference
• Include shot_type + camera_angle early in the prompt
• End with no text no watermarks no logos
• Keep under 200 tokens

For TTS narration:

• Present tense, max 8 words per sentence
• Include YOU/YOUR in at least 1 sentence
• Start with a specific name, date, or number
• Never start with "In [year]" / "During" / "Throughout"

For agent instructions:

• Add ## When NOT to use section if missing
• Verify trigger keywords match CLAUDE.md delegation table
• Confirm model assignment matches 3-tier routing

For Telegram messages:

• Keep under 280 chars for readability
• Use *bold* for key metrics, `code` for commands
• No raw paths longer than 40 chars

4. Output format

PROMPT OPTIMIZATION REPORT
===========================
Type: [classified type]
Original score: [X]/50

Scores:
  Clarity:       [X]/10  — [one-line finding]
  Specificity:   [X]/10  — [one-line finding]
  Conciseness:   [X]/10  — [one-line finding]
  Output contract:[X]/10 — [one-line finding]
  Edge cases:    [X]/10  — [one-line finding]

Key issues:
  1. [issue + fix]
  2. [issue + fix]

Optimized prompt:
---
[full optimized prompt text]
---
Optimized score: [X]/50

CRITICAL

• Do NOT change the semantic intent of the prompt
• Do NOT add hallucinated constraints (only what the system actually supports)
• Do NOT optimize for a different model tier than the one specified
• If the prompt is already >=40/50 → report PASS with minor suggestions only

User Input

$ARGUMENTS