Metagenomic Krona Chart

---

name: metagenomic-krona-chart

description: Analyze data with `metagenomic-krona-chart` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

license: MIT

skill-author: AIPOCH

---

# Metagenomic Krona Chart

When to Use

Key Features

Dependencies

See `## Prerequisites` above for related details.

Example Usage

See `## Usage` above for related details.

cd "20260318/scientific-skills/Data Analytics/metagenomic-krona-chart"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.

2. Edit the in-file `CONFIG` block or documented parameters if the script uses fixed settings.

3. Run `python scripts/main.py` with the validated inputs.

4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See `## Workflow` above for related details.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py

# Example invocation: python scripts/main.py --help

# Example invocation: python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan."

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.

2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.

3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.

4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.

5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Function Description

Generate interactive sunburst charts (Krona Chart) to display taxonomic abundance hierarchies in metagenomic samples. Supports parsing data from common classification tool outputs such as Kraken2, Bracken, and Centrifuge, and generates interactive HTML visualization charts.

Output Example

skills/metagenomic-krona-chart/
├── SKILL.md
├── scripts/
│   └── main.py
├── example/
│   ├── input.tsv
│   └── output.html
└── README.md

Usage

Basic Usage

# Example invocation: python scripts/main.py -i input.tsv -o krona_chart.html

Parameter Description

| Parameter | Description | Default Value |

|------|------|--------|

| `-i, --input` | Input file path (TSV format) | Required |

| `-o, --output` | Output HTML file path | krona_chart.html |

| `-t, --type` | Input format type (kraken2/bracken/custom) | auto |

| `--max-depth` | Maximum display hierarchy depth | 7 |

| `--min-percent` | Minimum display percentage threshold | 0.01 |

| `--title` | Chart title | Metagenomic Krona Chart |

Input Format

#### Kraken2/Bracken Report Format

100.00  1000000 0   U   0   unclassified
 99.00  990000  0   R   1   root
 95.00  950000  0   D   2   Bacteria
 50.00  500000  0   P   1234    Proteobacteria
...

#### Custom Format (TSV)

taxon_id	name	rank	parent_id	reads	percent
2	Bacteria	domain	1	950000	95.0
1234	Proteobacteria	phylum	2	500000	50.0

Dependency Requirements

pip install plotly pandas

Output Features

Notes

1. Input files need to contain taxonomic hierarchy information

2. For large datasets, use `--min-percent` to filter low-abundance taxa

3. Output is a standalone HTML file that can be viewed offline

Risk Assessment

| Risk Indicator | Assessment | Level |

|----------------|------------|-------|

| Code Execution | Python/R scripts executed locally | Medium |

| Network Access | No external API calls | Low |

| File System Access | Read input files, write output files | Medium |

| Instruction Tampering | Standard prompt guidelines | Low |

| Data Exposure | Output files saved to workspace | Low |

Security Checklist

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

Test Cases

1. **Basic Functionality**: Standard input → Expected output

2. **Edge Case**: Invalid input → Graceful error handling

3. **Performance**: Large dataset → Acceptable processing time

Lifecycle Status

- Performance optimization

- Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

Error Handling

Input Validation

This skill accepts requests that match the documented purpose of `metagenomic-krona-chart` and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

> `metagenomic-krona-chart` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective

2. Inputs Received

3. Assumptions

4. Workflow

5. Deliverable

6. Risks and Limits

7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

Inputs to Collect

Output Contract

Validation and Safety Rules