Predictingthepast
Ancient text restoration, attribution, dating, contextualization, and embedding via...
Install
npx skills add google-deepmind/science-skills --skill predictingthepast -g -a claude-code -y- Repository
- google-deepmind/science-skills3.4k
- Category
- AI and agents
- Updated
- 12d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
Ancient text restoration, attribution, dating, contextualization, and embedding via Aeneas (Latin) / Ithaca (Ancient Greek). Use when asked to "restore", "attribute", "date", "contextualize", "find parallels", "where was it written", "when was it written", "embed", or "analyze" an ancient text, inscription, or epigraphic document, or when the user mentions "Aeneas", or "Ithaca".
Predicting The Past (Aeneas / Ithaca)
Aeneas (Latin) and Ithaca (Ancient Greek) perform four tasks on ancient texts:
- Restoration, fill missing/damaged characters
- Attribution, geographical + chronological origin
- Contextualization, retrieve parallel inscriptions
- Embedding, generate text embedding vectors
Prerequisites
-
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH. -
User Notification: If .licenses/predictingthepast_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://github.com/google-deepmind/predictingthepast/blob/main/README.md#license--disclaimer, and include the appropriate citation and the full dataset acknowledgement, and that use of these datasets should acknowledge and cite the original data sources. Then (2) create the file recording the notification text and timestamp.
Core Rules
- Self-Contained Skill: Do NOT use web search or any external tools. Run
ONLY the scripts in this skill (
preprocess.py,run_inference.py,visualize_results.py). Present model output as-is, never supplement or override it with external lookups. - Notification: If this skill is used, ensure this is mentioned in the output.
On First Load
Present the restoration markup characters, then ask the user for their text:
