From Ancient Epigraphy to 3D Proteins: Building Plugins, Skills & MCP Sidecars in Google Antigravity

What happens when you ask an AI coding agent to score a non-coding human genomic variant with AlphaGenome, map a missense mutation onto an AlphaFold 3D protein structure, screen ChEMBL for nanomolar small-molecule inhibitors, or restore a damaged 2,000-year-old Roman inscription using Google DeepMind's Aeneas model?

If you try to stuff 33 scientific APIs, rate limiters, clinical safety guardrails, and 3D WebGL visualizers into a single mega-prompt, the agent collapses under context bloat and hallucinated parameters.

Google Antigravity solves this at the runtime level with its Plugin & Customization Architecture—combining Progressive-Disclosure Skills (SKILL.md), Always-On Rules (AGENTS.md), Lifecycle Hooks (hooks.json), Zero-Leak Credential Protocols, and Dual-Process 3D MCP Sidecars (mcp_config.json + sidecar.json).

Instead of just reading the docs, I reverse-engineered the newly released Google Antigravity Science Plugin (v1.3.1) and built the open-source Antigravity Science Plugin Cookbook & Interactive 3D Studio—complete with a live browser-based 3D Biomolecular & Variant WebGL Sandbox, a zero-dependency PEP 723 3D protein + PAE generator, and a production bio-structural-qc custom plugin.

Try the Live 3D Biomolecular & MCP Sidecar Studio in Your Browser

• Launch the Interactive 3D Biomolecular & Variant WebGL Studio (index.html)
  • Explore 3D C-alpha backbones & non-covalent contact networks for **TP53 (AF-P04637), **Crambin (1CRN), and **EGFR Kinase + Gefitinib (CHEMBL203).
  • Click any 3D residue sphere via raycasting to trigger live *annotate_region** MCP tool calls, inspect AlphaFold pLDDT & AlphaGenome Variant Impact (AVI) scores, upload your own .pdb/.cif files, or run the interactive Aeneas Latin Epigraphy restoration demo!*
• Full Source Code & bio-structural-qc Plugin on GitHub (RobinaMirbahar/antigravity-science-plugin-cookbook)

1. The Antigravity Customization Architecture & Context Economics

Why can the Science plugin bundle 33 complex scientific skills and a 9-tool 3D WebGL MCP server without slowing down everyday coding turns? Because Antigravity strictly separates always-on behavioral guardrails from on-demand procedural execution:

flowchart TB subgraph Discovery["1. Hierarchical Discovery & Precedence (Highest -> Lowest)"] P1["1. Workspace (.agents/ walking up to repo root)"] P2["2. Workspace Declared (skills.json / plugins.json)"] P3["3. Global Config (~/.gemini/config/)"] P4["4. Built-in Bundles (/builtin/plugins/)"] P5["5. Global Declared JSON"] P1 --> P2 --> P3 --> P4 --> P5 end subgraph Bundle["2. Plugin Bundle (~/.gemini/config/plugins/science/)"] Manifest["plugin.json (Manifest & Metadata)"] Rules["rules/AGENTS.md (Always-On Guardrails)"] Skills["skills/*/SKILL.md (33 On-Demand Skills)"] Hooks["hooks.json (PreToolUse / PostToolUse)"] MCP["mcp_config.json (Stdio / Streamable HTTP)"] Sidecar["sidecars/*/sidecar.json (Aux Pane Web UI)"] end subgraph Runtime["3. Agent Context & Execution Loop"] SysPrompt["System Prompt\n• Rules inlined (24KB/file, 20K token cap)\n• Skill names + descriptions ONLY\n• Eager MCP tool schemas"] Activate["Progressive Disclosure\nAgent calls read_file(SKILL.md)\nONLY when task matches description"] Exec["Deterministic Execution\n• uv run scripts/*.py (PEP 723)\n• Call MCP Sidecar tools\n• Surface sidecar:// pill link"] SysPrompt --> Activate --> Exec end Discovery --> Bundle --> Runtime

The 5 Customization Primitives & Their Token Budgets

2. Dissecting the Official science Plugin (v1.3.1)

When installed from the Antigravity Marketplace (antigravity-plugins-official/plugin/science), the bundle unpacks into ~/.gemini/config/plugins/science/. Look at how cleanly the 33 skills, sidecar binaries, and rules are organized:

~/.gemini/config/plugins/science/ ├── plugin.json # Bundle manifest (v1.3.1) ├── mcp_config.json # Registers biomolecular_structure_viewer_mcp ├── README.md # User-facing documentation (never read by agent) ├── SKILL_LICENSES.md # Third-party database attribution & terms ├── rules/ │ └── AGENTS.md # Visualization Priority Rules (always active) ├── sidecars/ │ └── biomolecular_structure_viewer/ │ ├── sidecar.json # Auxiliary Pane Web UI definition │ ├── ui.html # PDBe Mol* v3.12.0 + Lit v3.1.2 3D entrypoint │ └── dist/ │ ├── main.js # Bundled Mol* UI overlay frontend (183 KB) │ ├── server.cjs # Local state & WebSocket/HTTP server (68 KB) │ └── mcp_entry.cjs # MCP stdio server entrypoint (30 KB) └── skills/ # 33 Progressive-Disclosure Scientific Skills ├── alphafold_database_fetch_and_analyze/ # pLDDT & sliding-window PAE domain heuristics ├── alphagenome_atlas_website_links/ # Deep-links to AlphaGenome Atlas ├── alphagenome_single_variant_analysis/ # RNA-seq, DNASE, ChIP & splicing effects ├── alphagenome_variant_impact_score/ # AVI scoring, VCF annotation, GENCODE v46 GTF ├── biomolecular_structure_viewer/ # Mol* MCP orchestration & highlight/annotate workflows ├── chembl_database/ # Bioactivity (IC50/Ki), targets, drug mechanisms ├── clinical_trials_database/ # ClinicalTrials.gov APIv2 queries ├── clinvar_database/ # Pathogenicity & clinical review status ├── credentials/ # Zero-leak .env verification protocol ├── dbsnp_database/ # rsID <-> GRCh38 VCF/HGVS resolution ├── embl_ebi_ols/ # 250+ biomedical ontologies (GO, HP, DOID) ├── encode_ccres_database/ # ENCODE SCREEN GraphQL & cis-regulatory elements ├── ensembl_database/ # Gene/transcript lookup, sequences & VEP ├── foldseek_structural_search/ # 3D structure similarity search (.cif/.pdb) ├── gnomad_database/ # Allele frequencies & gene constraint (pLI/LOEUF) ├── gtex_database/ # Tissue-specific RNA expression & eQTLs ├── human_protein_atlas_database/ # Spatial & tissue protein expression ├── interpro_database/ # Protein domains, families & InterPro-N ├── jaspar_database/ # TF binding profiles (PFMs/PWMs) ├── literature_search_arxiv/ # arXiv preprints & full-text extraction ├── literature_search_biorxiv/ # bioRxiv / medRxiv narrow-window search ├── literature_search_europepmc/ # Europe PMC XML/PDF & citation graphs ├── literature_search_openalex/ # Bibliometrics, citation counts & OA PDFs ├── ncbi_sequence_fetch/ # NCBI E-utilities nucleotide/protein fetch ├── openfda_database/ # 28 FDA endpoints (adverse events, 510k, NDC) ├── opentargets_database/ # Target-disease associations & tractability ├── pdb_database/ # Experimental 3D structures from RCSB PDB ├── predictingthepast/ # Aeneas (Latin) & Ithaca (Ancient Greek) epigraphy ├── protein_sequence_msa/ # EBI Clustal Omega multiple sequence alignment ├── protein_sequence_similarity_search/ # MMseqs2 (fast) & BLAST homology search ├── pubchem_database/ # SMILES/CID chemical properties & substructures ├── pubmed_database/ # NCBI PubMed + PMC BioC full-text linking ├── pymol/ # Headless PyMOL rendering & structural superposition ├── quickgo_database/ # Gene Ontology & ECO evidence mapping ├── reactome_database/ # Pathway enrichment & reaction participants ├── string_database/ # Protein-protein interaction (PPI) networks ├── ucsc_conservation_and_tfbs/ # phyloP, phastCons & ENCODE/JASPAR/ReMap TFBS ├── unibind_database/ # Experimentally validated direct TF-DNA binding ├── uniprot_database/ # UniProtKB/UniParc/UniRef metadata & sequences ├── uv/ # Prerequisite uv environment bootstrapper └── workflow_skill_creator/ # Interactive distiller: turns chat workflows into skills 

3. Five Production Engineering Patterns Inside the Science Plugin

While inspecting the source files across all 33 skills, five engineering patterns stood out that every agent builder should steal.

Pattern 1: Always-On Behavioral Routing via rules/AGENTS.md

When an agent has access to Matplotlib, headless PyMOL, and an interactive 3D Mol* sidecar, how do you guarantee it prioritizes the interactive 3D viewer and never forgets to surface the clickable UI pill?

You place a compact, always-on rule in rules/AGENTS.md:

# Science Visualization Priority Rules For any science visualization task, **prefer the interactive sidecar viewers** (e.g. Biomolecular Structure Viewer) over static image generation, Generative UI, or text-based representations. The sidecars provide an interactive experience that the user can explore directly. ## Key Rules  - Only fall back to other visualization skills (e.g. `pymol`) when no viewer sidecar can do what the user needs.  - **Always surface a sidecar link.** Whenever the agent loads or updates a visualization in a sidecar (e.g. by calling its MCP tools), it **MUST** include that viewer's `sidecar://` pill link in the chat response so the user can open the viewer panel with one click. Copy the exact link given in the viewer's skill; don't read sidecar files to build it. 

Pattern 2: Zero-Pollution Python Micro-Environments (uv + PEP 723 + Sliding-Window PAE Math)

Running global pip install inside a user's repository breaks virtual environments. Instead, every Science skill uses uv run with PEP 723 inline script metadata (# /// script) and enforces rate limits and mathematical heuristics inside deterministic Python scripts.

For example, in skills/alphafold_database_fetch_and_analyze/scripts/:

• fetch_structure.py uses polite-http with a hard qps=1.0 rate limit (CLIENT = http_client.HttpClient("https://alphafold.ebi.ac.uk", qps=1.0)) and automatically falls back to the longest isoform for massive proteins (> 2,700 amino acids like Dystrophin).
• analyze_pae.py forbids the LLM from guessing domain boundaries by running a 20-residue sliding-window Predicted Aligned Error (PAE) clustering algorithm (distance_cutoff=7.0 Å, min_domain_size=40, merge_cutoff=15.0 Å):

"""Analyzes Predicted Aligned Error (PAE) and detects domain boundaries.""" # /// script # requires-python = ">=3.10" # dependencies = [] # ///  def find_sub_domains(pae_matrix, distance_cutoff=7.0, min_domain_size=40): """Identifies structurally independent sub-domains based on the PAE matrix.""" n_res = len(pae_matrix) domains, current_domain = [], [] for i in range(n_res): if not current_domain: current_domain.append(i) continue window_size = min(20, len(current_domain)) recent_res = current_domain[-window_size:] pae_sum = sum(pae_matrix[r][i] + pae_matrix[i][r] for r in recent_res) avg_pae = pae_sum / (2.0 * window_size) if avg_pae < distance_cutoff: current_domain.append(i) else: if len(current_domain) >= min_domain_size: domains.append(current_domain) current_domain = [i] if len(current_domain) >= min_domain_size: domains.append(current_domain) return [[comp[0] + 1, comp[-1] + 1] for comp in domains] 

Pattern 3: The Zero-Leak Credential Protocol (skills/credentials/SKILL.md)

Genomic and literature APIs like AlphaGenome (ALPHAGENOME_API_KEY) require secret keys. If an agent asks a user to paste their API key into the chat—or runs cat ~/.env—that secret is permanently recorded in the conversation transcript and model context.

The credentials skill solves this with a strict Zero-Leak Protocol:

1. Silent Exit-Code Verification: The agent checks whether the variable exists in ~/.env using grep -sq (quiet + suppress errors), reading only the 0 vs. non-zero exit code:

 grep -sq "^ALPHAGENOME_API_KEY=" ~/.env 

1. Hidden Terminal Prompting (read -s): If missing, the agent stops immediately and outputs a terminal command for the user to run locally where keystrokes are hidden (read -s):

 printf "Enter ALPHAGENOME_API_KEY (typing hidden): " && read -s val && echo && echo "ALPHAGENOME_API_KEY=$val" >> "$HOME/.env" && echo "Saved." 

1. Internal dotenv Ingestion: Helper scripts load ~/.env internally via dotenv, so the key never appears in command-line flags or stdout.

Pattern 4: Dual-Process 3D WebGL Sidecars (mcp_config.json + sidecar.json + ui.html)

How does the Biomolecular Structure Viewer let the agent highlight 3D protein regions while the human rotates the molecule and pins 3D residue notes in real time?

It pairs an MCP Server (dist/mcp_entry.cjs, launched via agy-node) with an Auxiliary Pane Web UI Sidecar (dist/server.cjs + ui.html, powered by PDBe Mol* v3.12.0 and Lit v3.1.2):

sequenceDiagram participant User participant Agent as Antigravity Agent participant AF as Skill: alphafold / pdb participant MCP as MCP: mcp_entry.cjs participant Sidecar as Sidecar: server.cjs + PDBe Mol* UI User->>Agent: "Visualize TP53 (UniProt P04637) and highlight the R248Q DNA-contact hotspot" Agent->>AF: uv run scripts/fetch_structure.py P04637 -o <artifact_dir> AF-->>Agent: Saved AF-P04637-F1-model_v6.cif + PAE JSON Agent->>MCP: load_structure(id="AF-P04637-F1-model_v6.cif", source="artifact") MCP->>Sidecar: Atomic state patch (load local .cif) Agent->>MCP: highlight_region(chain="A", start_residue=245, end_residue=250, color="#FF1744") MCP->>Sidecar: Focus 3D camera & highlight residues Agent-->>User: Emits [Biomolecular Structure Viewer](sidecar://science/biomolecular_structure_viewer/ui.html) pill User->>Sidecar: Clicks pill -> Rotates 3D model & pins custom annotation on Zinc finger User->>Agent: "Check conservation for the residue I just annotated in 3D" Agent->>MCP: get_state() MCP-->>Agent: Returns activeSelection & user 3D annotations

Here are the exact two config files that wire this up:

• mcp_config.json (at the plugin root):

 { "mcpServers": { "biomolecular_structure_viewer_mcp": { "command": "agy-node", "args": ["sidecars/biomolecular_structure_viewer/dist/mcp_entry.cjs"] } } } 

• sidecars/biomolecular_structure_viewer/sidecar.json:

 { "args": ["dist/server.cjs"], "command": "node", "description": "Interactive 3D biomolecular structure viewer via Mol*", "display_name": "Biomolecular Structure Viewer", "has_web_ui": true, "restart_policy": "always", "ui_config": { "display_name": "Biomolecular Structure Viewer", "views": [ { "entrypoint": "SIDECAR_UI_ENTRYPOINT_AUX_PANE", "path": "/ui.html", "title": "Biomolecular Structure Viewer" } ] } } 

Pattern 5: Domain-Specific Guardrails — From Clinical Genetics (AlphaGenome) to Ancient Epigraphy (Aeneas & Ithaca)

Two of the most fascinating skills in the bundle demonstrate how SKILL.md enforces domain-specific scientific rigor:

1. alphagenome-variant-impact-score (Strict Molecular Framing + Context-Overflow Protection):
   • Clinical Safety Gate: Explicitly forbids extrapolating high AlphaGenome Variant Impact (AVI) scores into clinical diagnoses or patient prognosis, restricting outputs to molecular mechanisms (splicing disruption, chromatin accessibility, TF binding, or GENCODE v46 transcript coordinates).
   • Stdout Overflow Guard: Warns the agent that --include_track_info --format json emits ~3.9 KB per variant, requiring direct file redirection (-o query_results.json) whenever querying > 3 variants so stdout never floods the context window.

2. predictingthepast (Ancient Latin & Greek Epigraphy with DeepMind's Aeneas & Ithaca):

   • Restores damaged ancient inscriptions, attributes geographical province + chronological date ranges, and finds parallel inscriptions using special gap syntax (? for a single missing character, # for an unknown-length damaged gap):

    uv run scripts/preprocess.py --language=latin --input="imp caesar divi f ???????? cos xi" 

• Enforces a Self-Contained Epigraphy Rule: forbids the agent from using web search to "cheat" or override the neural restoration probabilities returned by Aeneas/Ithaca.

4. Five End-to-End Multi-Skill Use Cases Across the 33 Science Skills

Because all 33 skills share the conversation's artifact directory, Antigravity can chain multiple domain-specific CLI scripts and MCP sidecars in a single research session.

I included a runnable benchmark suite in examples/multi_domain_science_use_cases.py (uv run examples/multi_domain_science_use_cases.py) that executes and scores all five multi-skill pipelines below:

Use Case 1: Precision Oncology & 3D Pocket Triage (ClinVar → gnomAD → AlphaFold → ChEMBL → 3D Mol* Sidecar)

Scenario: A translational oncology team wants to evaluate an EGFR L858R (rs121434568, chr7:55191822:T>G) driver mutation, verify gene loss-of-function constraint, inspect 3D active-site confidence, rank nanomolar kinase inhibitors, and highlight the pocket in 3D.

# 1. Check allele rarity & gene constraint (pLI / LOEUF) in gnomAD uv run ~/.gemini/config/plugins/science/skills/gnomad_database/scripts/get_gene_constraint.py \ --gene EGFR --output ./artifacts/egfr_constraint.json # 2. Fetch AlphaFold 3D structure (P00533) & audit pLDDT + PAE domains uv run ~/.gemini/config/plugins/science/skills/alphafold_database_fetch_and_analyze/scripts/fetch_structure.py \ P00533 -o ./artifacts/P00533/ uv run ~/.gemini/config/plugins/science/skills/alphafold_database_fetch_and_analyze/scripts/analyze_pae.py \ ./artifacts/P00533/AF-P00533-F1-predicted_aligned_error_v6.json # 3. Screen ChEMBL for EGFR (CHEMBL203) bioactivities & download 3D SDF ligands uv run ~/.gemini/config/plugins/science/skills/chembl_database/scripts/chembl_api.py \ activity --search "EGFR" --limit 10 --output ./artifacts/egfr_chembl_ic50.json uv run ~/.gemini/config/plugins/science/skills/chembl_database/scripts/chembl_api.py \ molecule --id CHEMBL939 --dl_format sdf --output ./artifacts/gefitinib.sdf 

Finally, the agent invokes science_biomolecular_structure_viewer_mcp (load_structure + highlight_region(chain="A", start_residue=855, end_residue=862, color="#FF1744")) and surfaces [Biomolecular Structure Viewer](sidecar://science/biomolecular_structure_viewer/ui.html):

Use Case 2: Non-Coding GWAS Enhancer Variant Triage (AlphaGenome → UCSC phyloP → ENCODE cCRE → JASPAR → GTEx)

Scenario: Over 90% of GWAS risk loci fall in non-coding DNA. Suppose you are investigating chr1:109274968:G>T (rs12740374 at the SORT1 1p13 locus) and want to know how it alters gene regulation across human tissues without hallucinating clinical diagnoses:

# 1. Score variant impact across 18 biological modalities + GENCODE v46 coordinates uv run ~/.gemini/config/plugins/science/skills/alphagenome_variant_impact_score/scripts/alphagenome_atlas_avi.py \ query "chr1:109274968:G>T" --include_track_info --format json -o ./artifacts/sort1_avi.json # 2. Check 100-vertebrate evolutionary constraint (phyloP100way & phastCons100way) uv run ~/.gemini/config/plugins/science/skills/ucsc_conservation_and_tfbs/scripts/get_conservation.py \ --coordinates "chr1:109274968" --output ./artifacts/sort1_phylop.json # 3. Fetch Transcription Factor Position Weight Matrices (PWMs) from JASPAR (CEBPA) uv run ~/.gemini/config/plugins/science/skills/jaspar_database/scripts/jaspar_cli.py \ search --query "CEBPA" --output ./artifacts/cebpa_jaspar.json 

• Engineering Guardrail in Action: The alphagenome-variant-impact-score skill enforces that --include_track_info outputs (~3.9 KB/variant) are redirected via -o and framed strictly in terms of molecular mechanisms (CEBPA binding site creation + liver RNA-seq eQTL shift):

Use Case 3: Dark-Proteome Enzyme Discovery (AlphaFold → Foldseek 3D Search → InterPro → Clustal Omega MSA → Headless PyMOL)

Scenario: A protein engineering group wants to discover novel thermostable plastic-degrading PETase homologs hidden in environmental metagenomes where sequence identity is below 25% (the "twilight zone" where BLAST fails, but 3D fold geometry is conserved):

# 1. Search the 3D coordinate file against Foldseek's metagenomic & AlphaFold databases uv run ~/.gemini/config/plugins/science/skills/foldseek_structural_search/scripts/search.py \ ./artifacts/PETase_AF.cif \ --databases afdb50,mgnify_esm30,pdb100,cath50 \ -o ./artifacts/petase_foldseek.json > ./artifacts/petase_foldseek_summary.md # 2. Align the top structural hits with EBI Clustal Omega to verify the Ser-Asp-His catalytic triad uv run ~/.gemini/config/plugins/science/skills/protein_sequence_msa/scripts/clustalo.py \ --input ./artifacts/petase_homologs.fasta --output ./artifacts/petase_msa.aln 

• Engineering Guardrail in Action: Look at foldseek-structural-search/SKILL.md:
  1. Strict Input Gate: If the user only gives a gene name or sequence without a .pdb/.cif file, the skill halts immediately and instructs the agent to fetch the 3D structure first.
  2. Database Allowlist: Enforces validation against the exact 9 supported Foldseek databases (afdb50, afdb-swissprot, pdb100, BFVD, mgnify_esm30, cath50, gmgcl_id, bfmd, afdb-proteome).
  3. Headless OSMesa PyMOL Superposition (pymol skill): When generating static publication figures alongside .pse session files, the pymol skill runs headless via PYOPENGL_PLATFORM=osmesa and automatically checks cmd.count_atoms("name CA") == cmd.count_atoms("all") so C-alpha-only traces still render as smooth cartoons!

Use Case 4: Clinical Trial Intelligence & Pharmacovigilance Audit (OpenTargets → ClinicalTrials.gov → openFDA → OpenAlex)

Scenario: A clinical data science team wants to map all recruiting Phase 3 trials for a target indication, inspect inclusion/exclusion criteria without overflowing the LLM context window, and audit real-world post-market adverse events across 28 FDA endpoints:

# 1. Query Open Targets for Phase 3+ candidates and target tractability uv run ~/.gemini/config/plugins/science/skills/opentargets_database/scripts/query_opentargets.py \ --output ./artifacts/nsclc_drugs.json --limit 15 \ get-disease-drugs MONDO_0005090 --min-stage PHASE_3 # 2. Check total trial volume first, then fetch ONLY slimmed fields from ClinicalTrials.gov APIv2 uv run ~/.gemini/config/plugins/science/skills/clinical_trials_database/scripts/clinical_trials_api.py \ search --condition "Non-Small Cell Lung Cancer" --status RECRUITING --phase PHASE3 \ --count-total --fields NCTId,BriefTitle,OverallStatus,EligibilityCriteria \ --limit 10 --output ./artifacts/nsclc_phase3_trials.json # 3. Query openFDA Drug Adverse Events (FAERS) with automatic rate-limit protection uv run ~/.gemini/config/plugins/science/skills/openfda_database/scripts/openfda_query.py \ count --category drug --endpoint event \ --search "patient.drug.medicinalproduct:osimertinib" \ --count "patient.reaction.reactionmeddrapt.exact" \ --limit 10 --output ./artifacts/osimertinib_faers_counts.json 

• Engineering Guardrails in Action:
  • ClinicalTrials.gov Context Projection (--fields): Full protocolSection + resultsSection trial JSON payloads can exceed 100 KB per study. clinical-trials-database requires --count-total first and shorthand --fields projection (NCTId,BriefTitle,OverallStatus), reducing payload size by 98.7%.
  • openFDA Quota Escalation (1,000 → 120,000 req/day): openfda-database warns that an autonomous agent can burn through the 1,000 keyless daily quota in a single session, triggering the credentials skill (grep -sq "^FDA_API_KEY=" ~/.env) before running multi-query workflows:

Use Case 5: Systems Biology & Ancient Epigraphy (STRING → Reactome & DeepMind's Aeneas / Ithaca)

Two more specialized workflows show how the Science plugin handles ambiguous biological identifiers and digital humanities:

1. Systems Biology Pathway Enrichment (STRING + Reactome):
   • The STRING "Never Assume Human" Rule: Even if a user asks for TP53 or BRCA1 interaction partners, string-database/SKILL.md strictly forbids defaulting to Homo sapiens (9606) if the species wasn't stated—requiring the agent to ask for the NCBI Taxon ID first, map symbols via string_cli.py map, and then export pathway diagrams in reactome-database (preferring specific sub-pathways like Cell Cycle, Mitotic R-HSA-69278 over broad top-level nodes).

2. Restoring Damaged Roman & Greek Stone Inscriptions (predictingthepast):

   • Powered by Google DeepMind's Aeneas (Latin) and Ithaca (Ancient Greek) models:

    # Preprocess damaged Latin epigraphy (? = single missing character, # = unknown-length gap) uv run ~/.gemini/config/plugins/science/skills/predictingthepast/scripts/preprocess.py \ --language=latin \ --input="imp caesar divi f ???????? cos xi trib potest" 

• Returns ranked character restorations (augustus, 96.4% confidence), geographical province attribution (Latium et Campania), chronological dating (23 BCE), and corpus parallels—while strictly blocking external web search so model probabilities remain unadulterated:

5. Hands-On Cookbook: Build & Run the Custom bio-structural-qc Plugin

In the companion repository (RobinaMirbahar/antigravity-science-plugin-cookbook), I built a complete, installable custom plugin called bio-structural-qc plus a zero-dependency 3D Protein + PAE Generator & SVG Dashboard (examples/generate_3d_protein_pae_demo.py).

Directory Structure of bio-structural-qc

plugins/bio-structural-qc/ ├── plugin.json # Manifest & 3 starter prompts ├── hooks.json # PostToolUse command reproducibility logger ├── assets/ │ └── logo.svg # 128x128 high-contrast SVG icon ├── rules/ │ └── AGENTS.md # Mandatory QC gate before docking/Foldseek └── skills/ └── structure-qc-audit/ ├── SKILL.md # Progressive-disclosure instructions └── scripts/ └── audit_structure.py # PEP 723 C-alpha B-factor/pLDDT auditor 

1. The Always-On QC Gate (plugins/bio-structural-qc/rules/AGENTS.md)

# Biomolecular Structure QC Gate Whenever a workflow downloads or modifies a 3D coordinate file (`.pdb`, `.cif`, or `.mmcif`) for downstream structural analysis, homology search, or variant mapping:  1. **Mandatory QC Audit**: Run the `structure-qc-audit` skill on the coordinate file before passing it to downstream modeling tools. 2. **Disorder Warning**: If more than 30% of alpha-carbon (`CA`) atoms have a B-factor / pLDDT score below `50.0`, explicitly warn the user about disordered or low-confidence regions and highlight the high-confidence domain boundaries in the [Biomolecular Structure Viewer](sidecar://science/biomolecular_structure_viewer/ui.html). 

2. The Zero-Dependency PEP 723 Structure Auditor (audit_structure.py)

#!/usr/bin/env python3 """Audits PDB and mmCIF coordinate files for chain completeness and B-factor/pLDDT distribution.""" # /// script # requires-python = ">=3.10" # dependencies = [] # ///  import argparse, json, statistics from pathlib import Path def parse_ca_bfactors(filepath: PathSource：https://dev.to/robinamirbahar/from-ancient-epigraphy-to-3d-proteins-building-plugins-skills-mcp-sidecars-in-google-antigravity-4l1m
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