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If MEGAHIT: run `megahit` with `--min-contig-len`, `--num-cpu-threads`, `-m` memory.\n2. If metaSPAdes: run `spades.py --meta` with `-k` kmer sizes, `-t` threads, `-m` memory.\n3. Filter contigs by minimum length.\n4. Generate assembly statistics: total contigs, total length, N50, L50, largest contig, GC content.\n5. Report assembly summary.\n\n## Output Contract\n\n- **Contigs FASTA** — Assembled contigs filtered by minimum length (`\u003Coutdir>\u002Fcontigs_min\u003CN>bp.fasta`).\n- **Assembly statistics** — N50, L50, total length, contig count, largest contig, GC percentage (`\u003Coutdir>\u002Fassembly_stats.txt`).\n\n## Limits\n\n- metaSPAdes requires significantly more memory than MEGAHIT (100-500 GB vs 10-50 GB for human gut metagenomes).\n- MEGAHIT is faster and more memory-efficient; recommended as the default for most samples.\n- Minimum contig length of 1000 bp is recommended for downstream binning.\n- Assembly quality depends heavily on sequencing depth and community complexity.\n- MEGAHIT and metaSPAdes (SPAdes) must be installed and available on `$PATH`.\n- This skill does not perform scaffolding; output is contigs only.\n- Common failure cases:\n  - metaSPAdes running out of memory on complex or deeply sequenced samples.\n  - MEGAHIT crashing due to insufficient disk space for intermediate k-mer graph files.\n  - Input reads still containing host contamination, inflating assembly size with host contigs.\n","# Skill: metagenome-assembly\n\n## Use When\n\n- The user wants to assemble metagenomic reads into contigs for downstream analysis.\n- The user needs contigs for downstream binning and gene prediction.\n- The user wants to compare assemblers (MEGAHIT vs metaSPAdes).\n- The user has sufficient compute resources for de novo metagenomic assembly.\n\n## Inputs\n\n- Required:\n  - Host-depleted FASTQ file(s) (`.fastq`, `.fq`, `.fastq.gz`, `.fq.gz`).\n- Optional:\n  - `--assembler STR` — Assembler to use: `megahit` or `metaspades` (default: `megahit`).\n  - `--threads N` — Number of threads (default: 4).\n  - `--memory N` — Memory limit in GB (default: 16).\n  - `--min-length N` — Minimum contig length in bp (default: 1000).\n  - `--kmer-sizes STR` — Comma-separated k-mer sizes for metaSPAdes (e.g., `21,33,55,77`).\n  - `--outdir DIR` — Output directory (default: `assembly_results`).\n\n## Workflow\n\n1. If MEGAHIT: run `megahit` with `--min-contig-len`, `--num-cpu-threads`, `-m` memory.\n2. If metaSPAdes: run `spades.py --meta` with `-k` kmer sizes, `-t` threads, `-m` memory.\n3. Filter contigs by minimum length.\n4. Generate assembly statistics: total contigs, total length, N50, L50, largest contig, GC content.\n5. Report assembly summary.\n\n## Output Contract\n\n- **Contigs FASTA** — Assembled contigs filtered by minimum length (`\u003Coutdir>\u002Fcontigs_min\u003CN>bp.fasta`).\n- **Assembly statistics** — N50, L50, total length, contig count, largest contig, GC percentage (`\u003Coutdir>\u002Fassembly_stats.txt`).\n\n## Limits\n\n- metaSPAdes requires significantly more memory than MEGAHIT (100-500 GB vs 10-50 GB for human gut metagenomes).\n- MEGAHIT is faster and more memory-efficient; recommended as the default for most samples.\n- Minimum contig length of 1000 bp is recommended for downstream binning.\n- Assembly quality depends heavily on sequencing depth and community complexity.\n- MEGAHIT and metaSPAdes (SPAdes) must be installed and available on `$PATH`.\n- This skill does not perform scaffolding; output is contigs only.\n- Common failure cases:\n  - metaSPAdes running out of memory on complex or deeply sequenced samples.\n  - MEGAHIT crashing due to insufficient disk space for intermediate k-mer graph files.\n  - Input reads still containing host contamination, inflating assembly size with host contigs.\n",{"name":5,"description":7},"main","5118e7fda890e9aa819cb71e003cd243d3a31970","skills\u002Fmetagenome-assembly",1,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence","https:\u002F\u002Fgithub.com\u002FScientific-Tooling","2026-11-03 00:01:40",{"source":8,"skillName":5,"commands":35,"prompt":45,"zipAvailable":46},[36,39,42],{"tool":37,"command":38},"npx","npx skills add Scientific-Tooling\u002Fstructured-intelligence --skill metagenome-assembly",{"tool":40,"command":41},"bunx","bunx skills add Scientific-Tooling\u002Fstructured-intelligence --skill metagenome-assembly",{"tool":43,"command":44},"pnpm","pnpm dlx skills add Scientific-Tooling\u002Fstructured-intelligence --skill 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