Science Skills is a collection of add-ons that give AI agents structured instructions, scripts, and references for scientific research, including genomics, structural biology, cheminformatics, and literature search. Researchers use it to guide agents through specialized scientific tasks with information from databases and tools such as AlphaGenome, AFDB, and UniProt. The catalogue entries are individual skills from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add google-deepmind/science-skills --skill predictingthepastgit clone --depth 1 https://github.com/google-deepmind/science-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/google-deepmind/science-skills/predictingthepast)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/predictingthepast"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/predictingthepast.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 278 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.03741 |
| Opus 5 | $0.00052 | $0.01870 |
| Sonnet 5 | $0.00021 | $0.00748 |
| Haiku 4.5 | $0.00010 | $0.00374 |
Grade A, and why
predictingthepast scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/iphi-region-sub-loc.json 6.2 KB
- references/iphi-region-sub.txt 1.1 KB
- references/led-proper-names.txt 330 KB
- references/led-region-sub-loc.json 4.5 KB
- references/led-region-sub.txt 735 B
- references/output_format.md 5.6 KB
- scripts/preprocess.py 8.0 KB runs code
- scripts/run_inference.py 24 KB runs code
- scripts/visualize_results.py 23 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 344 lines · 104 tokens per session scan A 07520c497a85
predictingthepast is a skill published in the GitHub repository google-deepmind/science-skills (2,849 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 104 tokens to every session and 3,741 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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