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, insc

Predicting The Past (Aeneas / Ithaca) Aeneas (Latin) and Ithaca (Ancient Greek) perform four tasks on ancient texts: 1. Restoration — fill missing/damaged characters 2. Attribution — geographical + chronological origin 3. Contextualization — retrieve parallel inscriptions 4. Embedding — generate text embedding vectors Prerequisites 1. uv : Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH. 2. 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: ? : Meaning : Known length gap: predict this character . Example : donat in ??????????rtis : Meaning : Unknown length gap: predict a sequence of unknown length Example : donat in rtis : Meaning : Missing/damaged character that does not need restoring Example : prolixin s fecit : Meaning : Missing section of unknown length that does not need restoring Example : prolixin s fecit After presenting this list, ask the user to provide the text they want to submit for analysis. Preprocessing Clean input text before inference: Or from a file: What preprocessing does Latin : lowercases, converts Arabic digits and Roman numerals to 0 , strips editorial brackets [] and () , removes punctuation, filters to valid chars ( abcdefghiklmnopqrstuvxyz plus 0 . ? <space ) Greek : lowercases, strips accents, converts numeral notation to 0 , applies PHI cleaning (bracket normalization, sigma conversion), filters to Greek alphabet ( αβγδεζηθικλμνξοπρςστυφχψωϛ plus 0 . ? <space ) Inference Restoration Constraints Minimum input length: 25 chars (pad with if shorter). No consecutive . No adjacent ? or ? . Spaces inside ? sequences count toward total. If the user's text contains , ask how many characters to restore and set restore max len accordingly. If the user tries to restore multiple parts of the text at once, suggest to restore texts section by section . Suggest to focus on one damaged region per query — this is faster, produces higher quality predictions. Pre Flight Checks Confirm with the user before proceeding if either applies: 1. Restoration complexity — if input contains more than 10 ? characters, or uses with restore max len 10 , warn: "This restoration involves N characters which will take approximately M minutes (restoration time scales roughly linearly ~10 s per additional ? on a high end CPU machine: 5 → ~1 min, 10 → ~2.5 min, 20 → ~5 min, 30 → ~8 min). Do you want to proceed, or simplify the query first (e.g. fewer ? marks, shorter restore max len , or restoring section by section)?" 2. Multi window splitting — if the input text exceeds 750 characters and will be split into multiple windows, warn: "This text is N characters long and will be split into W overlapping windows, each run independently. This will be significantly slower. Do you want to proceed, or shorten the input?" These factors compound: a complex restoration across multiple windows will be substantially slower than either factor alone. Task Selection Each task is controlled by its own flag. At least one must be provided: attribute — geographical + chronological attribution restore — text restoration (requires ? or in input) contextualize — parallel inscription retrieval Any combination is valid. All three can be used together. When embedding is provided, a text embedding vector is also generated alongside the other tasks. Running Inference Outputs are always returned to the console (JSON/Summary) and must always be written to an HTML dashboard for rich visualization. Serving HTML artifacts : After generating any HTML file (the dashboard or any other HTML artifact you create), start a background HTTP server so the user can view it in their browser. Run the server in the foreground of the background task process (without trailing & ) to prevent the process from being terminated. Use a random high port to avoid conflicts. In the example below, replace <HTML FILE with the absolute path of the HTML file you produced (e.g. /tmp/dashboard.html or C:\tmp\dashboard.html ). For Linux/macOS: For Windows (PowerShell): Then provide http://localhost:<port /<filename (or http://127.0.0.1:<port /<filename ) as the primary clickable link in your response. Additionally, provide http://<hostname :<port /<filename as a fallback link for remote setups. Do not give the user a raw file:// path. Flags input : Text string (mutually exclusive with input file ) input file : Path to UTF 8 text file language : latin or greek models dir : Model files directory (default: $HOME/.predictingthepast/models ) attribute : Run geographical and chronological attribution restore : Run text restoration (requires ? or in input) contextualize : Run parallel inscription retrieval contextualize top k : Number of top parallels to return (default: 10) contextualize exclude test valid : Exclude test/validation texts from contextualization results. Filters on the internal numeric id (where id % 10 is 3 or 4). embedding : Generate a text embedding vector (default: off). When set, the output includes an embedding field with the full precision float vector. output json : Save combined JSON to file (for visualize results.py ) restore beam width : Number of candidate restorations to explore in beam search. Higher = more thorough but slower (default: 100) restore max len : Maximum total characters to restore across all ? and gaps (default: 15) restore temperature : Sampling temperature. Lower = conservative, higher = creative (default: 1.0) window overlap : Overlap fraction for long text windowing (default: 0.33) Long Texts ( 750 characters) Each window must be between 25 and 750 characters . If the input text exceeds 750 characters, it is automatically split into overlapping windows (default 33% overlap). Each window is run through the model independently, and the geographical and chronological attribution results are averaged across all windows. Restoration and contextualization are run per window and concatenated. Output Format Use output json to save the combined JSON to a file. For the full output json format including all fields for attribution, restoration, and contextualization, see [output format.md](references/output format.md). Presenting Results Present results for every task requested by the user. Always generate the HTML dashboard, serve it via the background HTTP server described above, and provide the http:// URL as a clickable link. Format years as BCE/CE. Restoration Bold the restored characters in the top prediction. Show top 10 beam search candidates in a numbered table (rank, restored text, score). Summarise the most salient words (not characters) — do not dump the raw saliency array. Geographical Attribution Show the top 10 regions in a ranked table (rank, region, score). Highlight the top 1 prediction in the narrative. Summarise the most salient words (not characters) — do not dump the raw saliency array. Chronological Attribution Report top year and weighted average year. Describe the distribution shape (peak decade, probable range) — do not list all 160 bins. Summarise the most salient words (not characters) — do not dump the raw saliency array. Contextualization (Parallels) Use jp or write your own code to extract the contextualization data from output json . Show the top parallels in a table (rank, ID, Trismegistos ID, region, date range, score, partner link). Quote the full text of the top parallels from the JSON. If the user requests a specific number of parallels, use contextualize top k to control how many are returned. If the user asks to exclude test/validation texts, add contextualize exclude test valid . This filters on the internal numeric id (where id % 10 is 3 for test or 4 for validation). Embedding (when embedding is set) State the vector dimensionality (384 floats). References For more information, see: [predictingthepast.com](https://predictingthepast.com) IMPORTANT: The first time you return inference results to the user, remind them to cite the appropriate paper below (Ithaca for Greek, Aeneas for Latin), and the dataset acknowledgements below. Ithaca (Ancient Greek): Assael, Y., Sommerschield, T., Shillingford, B. et al. Restoring and attributing ancient texts using deep neural networks. Nature 603, 280–283 (2022). Aeneas (Latin): Assael, Y., Sommerschield, T., Cooley, A. et al. Contextualizing ancient texts with generative neural networks. Nature 645, 141–147 (2025). Dataset Acknowledgements IMPORTANT: The first time you present inference results to the user, include the appropriate citation and the full dataset acknowledgement for the language used (see below). The Ithaca and Aeneas projects rely on the availability of high quality datasets of ancient Greek and Latin inscriptions, respectively. These datasets were built through centuries of scholarly collection and decades of digital editorial work. Ithaca (Ancient Greek): In particular, Ithaca draws on a 2019 version of the Searchable Greek Inscriptions database made available under "Fair Use" license by the Packard Humanities Institute, generously supported by David Packard: [inscriptions.packhum.org](https://inscriptions.packhum.org). Aeneas (Latin): Aeneas was trained on data from: Epigraphic Database Roma (EDR) : Made available pursuant to a Creative Commons Attribution 4.0 International License (CC BY) on [Zenodo](https://zenodo.org/records/3575495). EDR is also available at [edr edr.it](http://www.edr edr.it/). Epigraphic Database Heidelberg (EDH) : Made available pursuant to a Creative Commons Attribution ShareAlike 4.0 International License (CC BY SA) on [Zenodo](https://zenodo.org/records/3575155). EDH is also available at [edh.ub.uni heidelberg.de](https://edh.ub.uni heidelberg.de/). ETL repository for Epigraphic Database Clauss Slaby (EDCS\ ETL) : Made available pursuant to a Creative Commons Attribution 4.0 International License (CC BY) on [Zenodo](https://zenodo.org/records/7072337). EDCS\ ETL is also available at [manfredclauss.de](http://www.manfredclauss.de/) and [github.com/sdam au/EDCS\ ETL](https://github.com/sdam au/EDCS ETL). These resources bring together a large proportion of published inscriptions in a searchable digital format.