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Get Started Free →Microbiome and metagenomics analysis using MGnify, GTDB taxonomy, ENA sequencing data, and EuropePMC literature. Covers taxonomic classification, genome quality assessment, biome-clinical phenotype linkage, and pathway interpretation. Use for amplicon/shotgun metagenomics study analysis.
.claude/skills/mims-harvard-tooluniverse-metagenomics-analysis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 98% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 66% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-06 | ✓→✗ | ▼ Worse | 1% | 0% |
Integrated pipeline for exploring microbiome studies, classifying taxa, assessing genome quality, linking microbial composition to clinical phenotypes, and interpreting findings through pathway analysis and literature context.
Guiding principles:
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
| Database | Best For | |----------|---------| | MGnify | Processed metagenomics studies, taxonomic/functional results | | GTDB | Standardized bacterial/archaeal taxonomy, species-level resolution | | GMrepo | Gut species-to-human-health phenotype associations | | ENA | Raw sequencing datasets and study metadata | | KEGG | Pathway mapping for microbial functional annotations | | PubMed/EuropePMC | Published microbiome-disease studies | | CTD | Chemical-microbiome-disease relationships |
Phase 0: Parse query → organism, biome, phenotype, or accession
Phase 1: Study Discovery → MGnify_search_studies, ENAPortal_search_studies
Phase 2: Taxonomic Classification → GTDB_search_genomes, GTDB_get_species, GTDB_search_taxon
Phase 3: Genome Quality → MGnify_search_genomes, MGnify_get_genome (CheckM metrics)
Phase 4: Functional Annotation → MGnify GO terms + KEGG pathway mapping
Phase 5: Clinical Associations → GMrepo species-phenotype links
Phase 6: Literature → PubMed/EuropePMC + CTD gene-disease
Phase 7: Interpretation & Report SynthesisPhase 1: ENA requires structured queries (e.g., study_title="*IBD*"), not free text. If ENA fails, fall back to MGnify.
Phase 2: GTDB uses its own naming (e.g., s__Bacteroides_A fragilis vs NCBI Bacteroides fragilis). Always note discrepancies. Use GTDB_search_taxon(operation="search_taxon", query=name).
Phase 3 - Quality tiers (MIMAG):
Phase 4 - Functional interpretation: Don't just list GO terms. Connect to biology:
| Functional Category | Key KEGG Pathways | Significance | |---|---|---| | SCFA production | map00650, map00640 | Gut barrier, anti-inflammatory | | LPS biosynthesis | map00540 | Pro-inflammatory, endotoxemia | | Bile acid metabolism | map00120 | Fat absorption, FXR signaling | | Tryptophan metabolism | map00380 | Serotonin, AhR, immune | | Vitamin biosynthesis | map00730/740/760 | Host nutritional contribution |
Use kegg_search_pathway(keyword=...) (NOT query). Pathway IDs need organism prefix (hsa, ko, eco), NOT bare map.
Phase 5: GMrepo uses MeSH terms: "Crohn Disease" not "IBD", "Colitis, Ulcerative" not "UC", "Colorectal Neoplasms" not "colorectal cancer". Try NCBI taxon IDs if species name fails.
Phase 6 - Evidence grading:
Phase 7 - Report: Executive summary, study landscape, GTDB taxonomy, functional interpretation (not GO term lists), clinical relevance with evidence grades, mechanistic model, genome catalog with quality tiers, data gaps.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 31,959 | 9,669 | -70% | 1 | 1 | 0% | 6,231 | 2,144 | -66% | 0 | 0 | — |
case-02 | fail→fail | 32,691 | 15,757 | -52% | 1 | 1 | 0% | 6,220 | 2,238 | -64% | 0 | 0 | — |
case-03 | fail→fail | 34,680 | 9,368 | -73% | 1 | 1 | 0% | 6,216 | 1,751 | -72% | 0 | 0 | — |
case-04 | pass→pass | 7,509 | 7,983 | +6% | 1 | 1 | 0% | 1,409 | 2,511 | +78% | 0 | 0 | — |
case-05 | fail→fail | 10,319 | 11,025 | +7% | 1 | 1 | 0% | 1,761 | 3,163 | +80% | 0 | 0 | — |
case-06 | pass→fail | 8,781 | 6,708 | -24% | 1 | 1 | 0% | 1,674 | 1,697 | +1% | 0 | 0 | — |
case-07 | fail→pass | 13,702 | 3,565 | -74% | 1 | 1 | 0% | 932 | 1,848 | +98% | 0 | 0 | — |
case-08 | pass→pass | 7,715 | 3,555 | -54% | 1 | 1 | 0% | 1,300 | 1,777 | +37% | 0 | 0 | — |
case-09 | fail→fail | 12,551 | 6,720 | -46% | 1 | 1 | 0% | 2,175 | 1,555 | -29% | 0 | 0 | — |
case-10 | pass→fail | 8,149 | 4,269 | -48% | 1 | 1 | 0% | 1,618 | 1,439 | -11% | 0 | 0 | — |
case-11 | fail→pass | 8,882 | 5,371 | -40% | 1 | 1 | 0% | 1,655 | 2,189 | +32% | 0 | 0 | — |
case-12 | fail→pass | 6,528 | 3,834 | -41% | 1 | 1 | 0% | 1,125 | 1,863 | +66% | 0 | 0 | — |
case-13 | pass→pass | 12,453 | 20,497 | +65% | 1 | 1 | 0% | 2,407 | 4,982 | +107% | 0 | 0 | — |
case-14 | pass→fail | 12,634 | 7,611 | -40% | 1 | 1 | 0% | 2,266 | 1,876 | -17% | 0 | 0 | — |
case-15 | pass→fail | 8,501 | 8,956 | +5% | 1 | 1 | 0% | 1,673 | 2,059 | +23% | 0 | 0 | — |
case-16 | pass→fail | 18,817 | 9,156 | -51% | 1 | 1 | 0% | 3,455 | 1,603 | -54% | 0 | 0 | — |
case-17 | fail→fail | 14,119 | 5,567 | -61% | 1 | 1 | 0% | 2,943 | 1,579 | -46% | 0 | 0 | — |
case-18 | pass→pass | 11,138 | 11,223 | +1% | 1 | 1 | 0% | 2,430 | 3,497 | +44% | 0 | 0 | — |
case-19 | pass→pass | 18,471 | 15,355 | -17% | 1 | 1 | 0% | 3,915 | 4,972 | +27% | 0 | 0 | — |
case-20 | pass→pass | 5,509 | 9,922 | +80% | 1 | 1 | 0% | 1,064 | 3,271 | +207% | 0 | 0 | — |
case-21 | pass→fail | 8,971 | 22,506 | +151% | 1 | 1 | 0% | 1,703 | 2,283 | +34% | 0 | 0 | — |
case-22 | fail→pass | 9,861 | 6,564 | -33% | 1 | 1 | 0% | 1,611 | 2,531 | +57% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 11 counted toward the lift figure. The other 11 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -9 percentage points is the difference between those two pass rates over the 11 comparable cases. 7 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.