Predictingthepast
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Predicting The Past provides an interface for the Aeneas and Ithaca neural network models to analyze ancient epigraphic documents. It supports Latin and Ancient Greek texts, enabling users to restore damaged inscriptions, determine their geographical and chronological origins, retrieve parallel texts, and generate embedding vectors. The skill includes preprocessing utilities for text cleaning and visualization tools to generate interactive HTML dashboards for research results.
What it does
- Restores missing or damaged characters in ancient inscriptions
- Determines the geographical and chronological origin of texts
- Retrieves parallel inscriptions for contextual analysis
- Generates text embedding vectors for ancient language data
- Cleans and normalizes Latin and Ancient Greek input text
- Visualizes analysis results in interactive HTML dashboards
When to use it
- When you need to restore damaged or incomplete ancient inscriptions
- When you need to identify the likely time and place of an inscription's origin
- When you need to find similar or parallel ancient texts
- When you need to generate vector embeddings for epigraphic research
Advantages
- Automates complex epigraphic tasks like restoration and attribution
- Provides standardized preprocessing for Latin and Greek datasets
- Generates rich, visual HTML reports for research findings
- Supports multi-window processing for long inscriptions
Usage examples
- “Restore the missing characters in this Latin inscription: donat in ??????????rtis”
- The agent cleans the input, runs the Aeneas model to predict the missing characters, and presents the top beam-search candidates in a table.
- “Where and when was this Greek inscription written? [insert text]”
- The agent processes the text, runs geographical and chronological attribution, and provides a ranked table of regions and a distribution of the probable date range.
- “Find parallels for this inscription and generate a dashboard.”
- The agent performs contextualization, saves the results to a JSON file, generates an HTML dashboard, and provides a local URL to view the parallels.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/predictingthepast/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip predictingthepast.zip -d .agents/skills
Documentation by Google DeepMind
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:
?: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:
uv run <SKILL_DIR>/scripts/preprocess.py \
--language=latin \
--input="raw text here..."Or from a file:
uv run <SKILL_DIR>/scripts/preprocess.py \
--language=greek \
--input_file=/tmp/input.txt \
--output_file=/tmp/cleaned.txtWhat preprocessing does
Latin: lowercases, converts Arabic digits and Roman numerals to
0, strips editorial brackets[]and(), removes punctuation, filters to valid chars (abcdefghiklmnopqrstuvxyzplus0 . - _ ? # <space>)Greek: lowercases, strips accents, converts numeral notation to
0, applies PHI cleaning (bracket normalization, sigma conversion), filters to Greek alphabet (αβγδεζηθικλμνξοπρςστυφχψωϛplus0 . - _ ? # <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_lenaccordingly.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:
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)?"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
# Attribution + Restoration (text with gaps)
uv run <SKILL_DIR>/scripts/run_inference.py \
--language=latin \
--input="cleaned text with ???" \
--attribute --restore \
--output_json=/tmp/results.json
# Attribution + Contextualization (no gaps)
uv run <SKILL_DIR>/scripts/run_inference.py \
--language=latin \
--input="cleaned text" \
--attribute --contextualize \
--output_json=/tmp/results.json
# All tasks
uv run <SKILL_DIR>/scripts/run_inference.py \
--language=latin \
--input="cleaned text with ???" \
--attribute --restore --contextualize \
--output_json=/tmp/results.json
# Generate dashboard
uv run \
<SKILL_DIR>/scripts/visualize_results.py \
--input=/tmp/results.json \
--output=/tmp/dashboard.htmlOutputs 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:
# Pick an unused port and start the server as a persistent process
HTML_FILE=/tmp/dashboard.html # ← set to the actual HTML file path
PORT=$(export PATH="$HOME/.local/bin:$PATH" && uv run python -c "import socket; s=socket.socket(); s.bind(('',0)); print(s.getsockname()[1]); s.close()")
FILENAME=$(basename "$HTML_FILE")
echo "View URL: http://localhost:${PORT}/${FILENAME}"
export PATH="$HOME/.local/bin:$PATH" && uv run python -m http.server "$PORT" --bind 0.0.0.0 --directory "$(dirname "$HTML_FILE")"For Windows (PowerShell):
# Pick an unused port and start the server as a persistent process
$HTML_FILE = "C:\tmp\dashboard.html" # ← set to the actual HTML file path
$PORT = & "$HOME\.local\bin\uv" run python -c "import socket; s=socket.socket(); s.bind(('',0)); print(s.getsockname()[1]); s.close()"
$FILENAME = Split-Path $HTML_FILE -Leaf
$DIRNAME = Split-Path $HTML_FILE -Parent
Write-Host "View URL: http://localhost:${PORT}/${FILENAME}"
& "$HOME\.local\bin\uv" run python -m http.server $PORT --bind 0.0.0.0 --directory $DIRNAMEThen provide http://localhost:<port>/<filename> (or
http://127.0.0.1:<port>/<filename>) as the primary clickable link in your
response. Additionally, provide http://<hostname>:<port>/<filename> as a
fallback link for remote setups. Do not give the user a raw file:// path.
Flags
--input: Text string (mutually exclusive with--input_file)--input_file: Path to UTF-8 text file--language:latinorgreek--models_dir: Model files directory (default:$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 numericid(whereid % 10is 3 or 4).--embedding: Generate a text embedding vector (default: off). When set, the output includes anembeddingfield with the full-precision float vector.--output_json: Save combined JSON to file (forvisualize_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.
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
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.
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. EDR is also available at 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. EDH is also available at 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. EDCS_ETL is also available at manfredclauss.de and github.com/sdam-au/EDCS_ETL.
These resources bring together a large proportion of published inscriptions in a searchable digital format.
Files
- ATTRIBUTION.mdAdded by AutoGineer916 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md14.3 KiB
- references/iphi-region-sub-loc.json6.2 KiB
- references/iphi-region-sub.txt1.1 KiB
- references/led-proper-names.txt330.0 KiB
- references/led-region-sub-loc.json4.5 KiB
- references/led-region-sub.txt735 B
- references/output_format.md5.6 KiB
- scripts/preprocess.pyScript8.0 KiB
- scripts/run_inference.pyScript26.9 KiB
- scripts/visualize_results.pyScript23.3 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Attribution and licence
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- google-deepmind/science-skills · skills/predictingthepast · commit 4de6786
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- Apache License 2.0 (redistribution allowed)
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