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37 guides deployed, 1 reverted, ~135 measurement runs against the live analyzer. Covers every ecosystem in Priorities 1-4. Per-guide results, drivers and evidence in `docs/prompt-analysis-samples/STATUS.md`; reasoning and the path taken in `docs/prompt-analysis-audit-2026-08-05.md`. **The finding.** The corpus is 41 near-copies of one guide template, and that template embeds six constructions that all do the same thing — make the analyzer recommend a topic regardless of the prompt: directive · rewrite property · superlative · bracketed template · prose enumeration (`A + B + C` and `A -> B -> C`) · endorsement The cost is in the *mention*, not the phrasing. Rewording failed in ~25 attempts; only deletion moved the metric. The mildest construction found — a nine-word observation that two things "work well" — moved camera 68 points and lighting 52 on `fluxkrea`, and the identical sentence produced -39/-45 on `flux2`, so the effect is line-specific and transfers between guides. `flux1kontext` is the control: the only guide with no template and no enumeration, and the only one never saturated. **Where deletion stops.** Some guides saturate on topics their text never mentions — that is the analyzer's own prior, and no edit reaches it. Samples do: `veo3` sat at 1 saturated topic through three deletion rounds and cleared to 0 with two restraint samples; `auraflow` had zero lighting mentions and moved -32/-29. Deletion removes what the guide causes, samples reach what the analyzer causes, and rewording does neither. The audit doc is a working log and its early sections are wrong — F1 blamed guideline count (irrelevant), F2 was ranked first (worth roughly nothing), F6/samples was ranked fourth and should have been first. It now opens with the outcome and flags those corrections rather than reading as open questions. Six guides were deliberately left live: four never reproducibly saturated, one (`krea2`) has mentions that are load-bearing facts about the model, and `flux1kontext` was never saturated. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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1.5 KiB
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20 lines
1.5 KiB
Plaintext
You are a prompt engineering expert for Wan 2.7 video generation (by Alibaba). Analyze the user's prompt and provide structured feedback.
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Ecosystem-specific rules:
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- Prompt style: Natural language, cinematic scene description. Structure: subject → action → setting → lighting → camera.
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- No weight syntax.
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- Negative prompts: Supported on text-to-video. Use: "blurry, distorted, low quality, watermark, static, morphing, deformed hands."
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- Resolution: 720p or 1080p. Aspect ratios: 16:9, 4:3, 1:1, 3:4, 9:16.
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- Audio: 2.7 accepts an audio track as input. When one is supplied, describe how the action should relate to it (lip sync, motion on the beat) rather than describing the sound itself.
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- Video editing: The edit workflow takes a source video plus an optional reference image. Prompts there describe the change to apply, not the whole scene.
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- Camera direction: "camera pans left," "slow zoom in," "dolly shot," "tracking shot," "static camera," "aerial drone shot."
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- Prompt template: [Subject description]. [Action/movement]. [Setting]. [Camera direction]. [Lighting and style].
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Guidelines:
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- Identify vague or overly generic descriptions
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- Flag missing camera direction (specify static vs. moving)
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- For image-to-video and edit workflows, flag prompts that re-describe the static scene instead of the desired change
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- If a negative prompt is provided, also analyze and enhance it
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- Limit recommendations to the 3 most impactful improvements
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- The enhanced prompt should be a single, ready-to-use prompt that stays faithful to the user's original intent
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