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Agent skill

Qiime2 Amplicon

by K-Dense-AI in K-Dense-AI/scientific-agent-skills

Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance.

MITAuto-check passedResearch & Science

Install Qiime2 Amplicon

skills CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill qiime2-amplicon -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install K-Dense-AI/scientific-agent-skills qiime2-amplicon --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/qiime2-amplicon .claude/skills/qiime2-amplicon && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qiime2-amplicon
GitHub stars
48k
Used in
1 other repo
Token cost
~2.2k tokens
SKILL.md length
858 words
Files
3 (incl. scripts, references)
Skills in repo
153
Repo updated
First seen
Licence
MIT

At a glance

Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance.

  • Research & Science work in your project
  • SKILL.md covers Establish the assay before…, Input files, Execute and Inspect results before analysis, plus 1 more section
  • Runs Python scripts from its folder; calls python

What it does

Qiime2 Amplicon is an agent skill from K-Dense-AI/scientific-agent-skills. Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance. Checks paired FASTQ manifests, primer orientation diagnostics, predicted post-trimming overlap, sample IDs, runtime versions and read retention, and guides selection of compatible taxonomic classifiers.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts and reference files (for example `references/runtime-and-interpretation.md` and `scripts/amplicon_workflow.py`). Compatibility notes: Python 3.10+ for the standard-library validation helper; QIIME 2 2026.7 distribution with cutadapt, dada2, demux, feature-table, feature-classifier, taxa and…

It sits in Research & Science. It works with scikit-learn. The repository describes itself as: Turn any AI agent into an AI Scientist. The 1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific… The licence is MIT.

When your agent uses it

  • Research & Science work in your project

Example prompts

  • “Use the qiime2-amplicon skill to process paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance”
  • “/qiime2-amplicon”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Python 3.10+ for the standard-library validation helper; QIIME 2 2026.7 distribution with cutadapt, dada2, demux, feature-table, feature-classifier, taxa and types plugins for execution. Install through the official conda/container distribution, not PyPI. Requires a compatible trusted classifier and network for installation/reference downloads.

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • amplicon-docs.qiime2.org
    • library.qiime2.org
    • view.qiime2.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Python 3.10+ for the standard-library validation helper; QIIME 2 2026.7 distribution with cutadapt, dada2, demux, feature-table, feature-classifier, taxa and types plugins for execution. Install through the official conda/container distribution, not PyPI. Requires a compatible trusted classifier and network for installation/reference downloads.

    From compatibility in the SKILL.md frontmatter.

Context cost

Qiime2 Amplicon loads about 2.2k tokens when it runs, and up to ~5.6k if it reads all its reference files. Until then it costs about 81 tokens; SKILL.md has 858 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~81
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 858 words, ~2,160 tokens.

Download SKILL.mdSave it as .claude/skills/qiime2-amplicon/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
qiime2-amplicon
description
Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance. Checks paired FASTQ manifests, primer orientation diagnostics, predicted post-trimming overlap, sample IDs, runtime versions and read retention, and guides selection of compatible taxonomic classifiers.
compatibility
Python 3.10+ for the standard-library validation helper; QIIME 2 2026.7 distribution with cutadapt, dada2, demux, feature-table, feature-classifier, taxa and types plugins for execution. Install through the official conda/container distribution, not PyPI. Requires a compatible trusted classifier and network for installation/reference downloads.
license
MIT
metadata.version
1.1
metadata.skill-author
K-Dense Inc.
metadata.upstream-version
2026.7
metadata.last-reviewed
2026-10-01

QIIME 2 paired-end 16S amplicons

Use for a demultiplexed, paired-end 16S assay with known primers, quality encoding, expected insert length, and sample metadata. This bounded workflow imports reads, removes 5′ primers, denoises with DADA2, classifies ASVs using an explicitly supplied classifier, and retains .qza/.qzv provenance. Do not treat read counts as absolute cell counts or taxonomy assignments as strain identification.

Establish the assay before running

  • Confirm Phred+33, read orientation, primer sequences as sequenced in forward/reverse reads, and whether primers have already been removed. The bundled runner requires primers still present at the 5′ ends; it anchors Cutadapt matching and discards untrimmed pairs. Already trimmed reads need a direct import → DADA2 workflow with that omission recorded in provenance.
  • Choose truncation positions from actual per-base quality and error profiles. trunc-f/r are positions after primer removal. The expected maximum insert length also excludes primers. Require trunc_f + trunc_r - maximum_insert_length >= 12; use a margin for length variation. The check predicts geometrical overlap, not successful biological merging.
  • Choose a classifier whose reference database, taxonomic coverage, orientation and training approach fit the assay. Full-length classifiers are supported; primer-region-specific training is not mandatory. Match its scikit-learn version exactly to the installed environment (the official 2026.7 distribution pins 1.7.1). Record source URL, database version and checksum. QIIME's current data-resources page links externally hosted classifiers for 2026.4 and later; its older downloads are not automatically compatible. Do not automatically fetch an arbitrary “latest” classifier or reuse an incompatible serialized sklearn model. Load sklearn classifier artifacts only from trusted sources: QZA format validation does not make an untrusted serialized model safe.
  • Include extraction blanks, PCR negatives, and a mock community where available. The runner rejects empty FASTQs; preserve empty-control IDs separately and report them rather than silently deleting control evidence. Assess contamination before ecological interpretation.

Input files

Manifest is a tab-separated PairedEndFastqManifestPhred33V2 file with exactly these headers:

text
sample-id	forward-absolute-filepath	reverse-absolute-filepath
sample1	/data/sample1_R1.fastq.gz	/data/sample1_R2.fastq.gz

This is the helper's deliberately narrow manifest profile. Use actual tab characters, literal absolute paths visible to the runtime (expand environment variables before calling this helper), and one row per sample; no comment/directive rows or additional columns in this manifest. Do not reverse-complement R2 files. Sample metadata is a tab-separated file with first column sample-id, unique IDs matching the manifest, and optional #q2:types annotation. Include covariates and biological replicate IDs needed downstream. The helper checks metadata IDs and row structure; QIIME performs full metadata typing/directive validation during execution. Metadata used by actions persists in artifact provenance, so use de-identified biological replicate IDs.

Show full SKILL.md (450 more words)Show less

Execute

The helper lives at scripts/amplicon_workflow.py. Commands below assume the skill directory is the working directory. First validate without QIIME. These example primers and lengths are illustrative, not universal assay settings:

bash
python scripts/amplicon_workflow.py validate \
  --manifest manifest.tsv --metadata sample-metadata.tsv \
  --primer-f GTGYCAGCMGCCGCGGTAA --primer-r GGACTACNVGGGTWTCTAAT \
  --trunc-f 220 --trunc-r 200 --amplicon-max 300

Then run in the QIIME 2 2026.7 environment with a compatible classifier. This study-specific invocation is illustrative; choose lengths using a preceding quality inspection or pilot:

bash
python scripts/amplicon_workflow.py run \
  --manifest manifest.tsv --metadata sample-metadata.tsv \
  --primer-f GTGYCAGCMGCCGCGGTAA --primer-r GGACTACNVGGGTWTCTAAT \
  --trunc-f 220 --trunc-r 200 --amplicon-max 300 \
  --classifier compatible-classifier.qza --threads 4 --output run01

run executes immediately, writes only to a fresh output directory, and stops on a failing QIIME command. It streams through every paired FASTQ record to catch mismatched IDs/order/counts, checks sequence/quality consistency and sample alignment, and profiles the first 1,000 read pairs for exact IUPAC primer matches. Low exact-match rates are warnings: Cutadapt allows mismatches, but a low rate can also indicate incorrect orientation, adapters, or already-trimmed data. At least one complete pair per sample must meet nominal post-primer truncation lengths. These length estimates subtract the stated primer lengths; they do not simulate Cutadapt indels or quality filtering, and do not establish that any pair will actually merge.

The runner uses --output-dir for plugin methods with evolving output sets, preserving Cutadapt statistics and DADA2 base-transition artifacts when supplied by the release. The 2026.7 table summary also produces feature-frequencies.qza and sample-frequencies.qza beside table.qzv. It records qiime-info.txt, commands.json, workflow.log, input QC, classifier checksum and output artifact checksums. It runs maximum-level QIIME artifact validation before reporting completion. See references/runtime-and-interpretation.md for the release-pinned runtime, actual validation scope, restart handling and scientific interpretation.

Inspect results before analysis

Open trimmed.qzv, table.qzv, and taxa.qzv in a local QIIME visualization environment or QIIME 2 View as appropriate for the data. Examine quality/length profiles, per-sample depth and dominant taxa. Retain original artifacts rather than replacing them with CSV/BIOM exports: exports do not retain the original provenance graph.

retention-qc.json compares raw pairs with DADA2 input and non-chimeric reads, so trimming losses remain visible. A <50% retained fraction is a review heuristic, not a universal rejection rule. Inspect the individual stages in stats/stats.tsv: filtering loss suggests quality/expected-error settings; loss after forward denoising includes reverse-denoising and merging failures; chimera loss warrants reviewing library quality and parameters. Investigate missing/zero samples and control behavior before rarefaction, diversity, or differential abundance. Those downstream analyses need a separate design decision; this skill does not choose a rarefaction depth automatically. The standalone retention stats.tsv subcommand knows only DADA2 input counts: it reports raw_pairs: null and names that denominator explicitly. It supports merged-only paired DADA2 statistics, as produced by this runner; it rejects retained-unmerged/concatenated-read statistics.

Primary references

The rolling documentation may describe a development release. Inspect qiime info and action --help in the exact installed environment before adapting the pinned runner to a later release.

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts, references) in skills/qiime2-amplicon of K-Dense-AI/scientific-agent-skills.

  • SKILL.md
  • references/runtime-and-interpretation.md
  • scripts/amplicon_workflow.py

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Works with

Categories

Questions about Qiime2 Amplicon

What does Qiime2 Amplicon do?

Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance. Qiime2 Amplicon is an agent skill from K-Dense-AI/scientific-agent-skills. Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance.

When should I use Qiime2 Amplicon?

Qiime2 Amplicon fits situations like: research & Science work in your project.

How do I install Qiime2 Amplicon in Claude Code?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill qiime2-amplicon -a claude-code`. Or copy the skill folder (skills/qiime2-amplicon in K-Dense-AI/scientific-agent-skills) into .claude/skills/qiime2-amplicon in your project. Claude Code loads it when a task matches its description.

How do I install Qiime2 Amplicon in Codex?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill qiime2-amplicon -a codex`. Or copy the skill folder (skills/qiime2-amplicon in K-Dense-AI/scientific-agent-skills) into .agents/skills/qiime2-amplicon in your project. Codex loads it when a task matches its description.

Can I use Qiime2 Amplicon in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add K-Dense-AI/scientific-agent-skills --skill qiime2-amplicon -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qiime2-amplicon, .gemini/skills/qiime2-amplicon, .github/skills/qiime2-amplicon and .opencode/skills/qiime2-amplicon in your project.

What does Qiime2 Amplicon need to run?

Going by SKILL.md and its folder, Qiime2 Amplicon needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3. Compatibility (from SKILL.md): Python 3.10+ for the standard-library validation helper; QIIME 2 2026.7 distribution with cutadapt, dada2, demux, feature-table, feature-classifier, taxa and types plugins for execution. Install through the official conda/container distribution, not PyPI. Requires a compatible trusted classifier and network for installation/reference downloads..

Does Qiime2 Amplicon access the network?

SKILL.md names 3 domains. As links in the text: amplicon-docs.qiime2.org, library.qiime2.org and view.qiime2.org. This is read from the text; nothing was executed.

Is Qiime2 Amplicon safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Qiime2 Amplicon use?

Qiime2 Amplicon is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qiime2 Amplicon use?

About 2.2k tokens (SKILL.md is roughly 8.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.4k tokens, read only when the agent opens those files.

What are the alternatives to Qiime2 Amplicon?

Skills that share tags, products or a category with Qiime2 Amplicon: Academic Paper Reproduction Methodology (xjtulyc/MedgeClaw, 617 stars), Light Experiment Coding (Light0305/Light-skills, 640 stars), Molfeat Molecular Featurization (jaechang-hits/SciAgent-Skills, 374 stars) and Statistical Data Analysis (lingzhi227/agent-research-skills, 390 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qiime2 Amplicon?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/scientific-agent-skills, which has 48,215 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.

Source: K-Dense-AI/scientific-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.