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Awesome Seedance connects prompt examples to their original posts and records how selected prompts perform when tried on other models. In a September 2026 article, Carl Lee reported 463 source-traced Seedance cases and 264 cross-model retest runs—but those are different counts, and the article explicitly says the cases were not all rerun.
What the project is designed to show
A viral AI-video prompt can be useful as inspiration, but an example is more informative when readers can see where it came from and how its structure might be adapted. Awesome Seedance brings together three kinds of material: links to creators’ original posts, reusable prompt templates, and records of attempts to reproduce selected prompts on other models.
The distinction is practical: provenance helps answer “Where did this prompt come from?” A template helps answer “How can I adapt its structure?” A retest record offers evidence about one attempt under particular conditions—not a guarantee that the prompt will work the same way elsewhere.
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| Figure | What it counts | Source and qualification |
|---|---|---|
| 463 | Seedance 2.5/2.0 cases traced to original posts | Reported in Carl Lee’s article published September 23, 2026; a project-reported snapshot, not an independently audited count. DEV Community article |
| 264 | Cross-model retest runs with public verdicts | Reported in the same article. A run is an attempt, not a unique case, and not all 463 cases were rerun. DEV Community article |
| 25 | Reusable templates | Reported in the same article’s project snapshot. DEV Community article |
| 60 | Installable AI-video Skills | Reported in the same article’s project snapshot. DEV Community article |
| 593 | Cases shown in a repository mirror sync note | AtomGit reported a September 30, 2026 sync. This is a later, differently scoped snapshot and should not be silently combined with the article’s 463. AtomGit mirror |
The project documentation provides a more specific retest accounting: 254 cases were rerun across 264 runs. Some cases therefore had more than one run. The article’s 463 cases and the documentation’s 254 rerun cases describe different populations, not conflicting versions of a single run count.
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How to use a case, template, and retest record
- Open a case and inspect its source. Follow the original-post link to see the creator’s post and the context available there. The documentation describes provenance fields including author, original post link, and publication date.
- Choose a template and adapt it. The article describes browsing reusable structures rather than treating a prompt as a universal recipe. For example, its UGC creator-review template keeps the person and product separately specified and ties spoken lines to visible actions.
- Generate with Seedance. Use the adapted prompt in the intended generation workflow. The source article describes this as part of the project’s suggested sequence, not as a guarantee of a particular output.
- Read the retest notes before changing the prompt. Check which model, platform, and batch produced the result. A degraded or failed attempt can help identify what did not transfer, but it does not by itself establish why it failed.
What the project says about provenance
The indexed project documentation says cases are human-verified against original posts and that prompts reverse-engineered solely from an output, without a source or submission, are rejected under the project’s collection standards. That describes the project’s stated method; it is not an independent audit of each case. A linked source makes a prompt’s attribution more traceable, but readers should still inspect the post and distinguish the creator’s original material from later adaptations.
What the retest results show—and what they do not
In statistics dated September 19, 2026, the project documentation reports 193 reproduced, 68 degraded, and 3 failed verdicts across 264 runs. These are project-reported outcomes. The mix itself matters: the corpus does not establish that every prompt transfers cleanly to another model.
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| Retest condition in project documentation | Runs | Reported reproduction rate | Qualification |
|---|---|---|---|
| MiniMax H3 Max 768p | 253 | 73% | September 2026 batch using fal.ai’s minimax/h3-max/text-to-video endpoint; project statistics dated September 19, 2026. |
| MiniMax H3 768p | 11 | 82% | August 2026 batch using Flova; project statistics dated September 19, 2026. |
These model tiers were tested on different platforms in different batches. The project documentation explicitly warns against merging them as though they were the same condition. The percentages describe those recorded runs, not the likelihood of success in a reader’s own setup; model versions, endpoints, prompt edits, and generation settings can all affect transfer.
The project does not provide independent validation of the experiment in the cited material. Treat the verdicts as useful project records rather than benchmark results for Seedance prompts as a whole or for the wider AI-video market.
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Templates and Skills serve different workflows
| Resource | Best suited to | How to interpret it |
|---|---|---|
| Reusable template | A person writing or adapting a prompt directly | A structure to copy and customize; it does not promise the original output will recur. |
| Installable AI-video Skill | An agent-based workflow | The article describes Skills for Claude Code, Codex, and other coding agents as a way to package the project’s prompt workflow. This is a description of the project’s intended use, not an endorsement of those tools. |
A template is the simpler starting point if you want to understand and edit the prompt yourself. A Skill is relevant when you want an agent workflow to apply the project’s structures. Neither replaces checking the original source or reading the conditions attached to a retest.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the counts can differ across snapshots
Counts are dated snapshots, not a fixed inventory. The project documentation gives September 19, 2026 as the last-updated date for its retest statistics, while an AtomGit mirror reports a September 30 sync with 593 cases. The sources do not establish that these figures use identical inclusion rules, so they should be attributed to their own source and date rather than combined into one total.
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