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 agentmods add skills/google-deepmind/science-skills/interpro_databasenpx skills add google-deepmind/science-skills --skill interpro_databasegit clone --depth 1 https://github.com/google-deepmind/science-skillsWhat 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 | $0.00084 | $0.04198 |
| Opus 5 | $0.00042 | $0.02099 |
| Sonnet 5 | $0.00017 | $0.00840 |
| Haiku 4.5 | $0.00008 | $0.00420 |
Grade A, and why
interpro-database 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 3d 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 — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
InterPro Database Access
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/interpro_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.ebi.ac.uk/interpro/ and https://www.ebi.ac.uk/about/terms-of-use/, then (2) create the file recording the notification text and timestamp.
Overview
InterPro combines signatures from multiple, diverse databases into a single searchable resource, reducing redundancy and helping users interpret their sequence analysis results. By uniting these member databases (e.g., Pfam, CDD, SMART), InterPro capitalises on their individual strengths to produce a powerful diagnostic tool and integrated resource.
Use interpro-database to:
- Identify what domains, families, and sites are found in a particular protein.
- Identify all proteins that belong to a protein family or contain a particular domain, even when the names and activities of the proteins are highly variable.
- Examine the species in which a particular protein family or domain is found.
- Annotate genomes with protein family information and Gene Ontology (GO) terms.
This skill provides a robust utility, interpro_client.py, to interact with the
InterPro API seamlessly. It natively handles rate limiting (HTTP 429),
background query sleep tracking (HTTP 408), terminal errors (HTTP 404/410), and
lazy pagination.
Core Rules
- Use the Wrapper: ALWAYS execute the
scripts/interpro_client.pyhelper script to query the database rather than accessing the database directly. The scripts automatically enforce fair use and implement retry logic. - For exploratory queries: ALWAYS use the CLI with a strict
--limit. This allows you to rapidly understand the data schema without polluting your context window or fetching millions of results. - Output to file: Use the CLI with --output to output to a file rather than attempting to print it all to the console. Process the output using jq or code.
- For more complex pipelines import the module natively into your Python scripts to consume the generator directly, preventing the need to deserialize CLI strings in large workflows.
- Notification: If this skill is used, ensure this is mentioned in the output.
What ships with it
4 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.
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.
- 3d ago First seen · 428 lines · 84 tokens per session scan A ab24b2241057
interpro-database is a skill published in the GitHub repository google-deepmind/science-skills (2,814 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 84 tokens to every session and 4,198 once invoked, about $0.0004 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…
auditing-subgroup-fairness
Audit an OpenMed NER or de-identification model for performance disparities across demographic subgroups (sex, age band, race/ethnicity when available) using openmed.eval.fairnessreport. Use when the user wants per-subgroup recall and leakage, wants to check whether de-identification under-protects a group, wants to…
overleaf-sync
Two-way sync between a local paper directory and an Overleaf project, so ARIS audit/edit workflows stay on the local copy while collaborators edit in the Overleaf web UI. Use when user says "同步 overleaf", "overleaf sync", "推送到 overleaf", "connect overleaf", "Overleaf 桥接", "pull overleaf", "push overleaf", or wants to…
mixed-precision
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.