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markdown-mermaid-writing

Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.

O que é markdown-mermaid-writing?

markdown-mermaid-writing is a Claude Code agent skill that comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.

Funciona com~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/markdown-mermaid-writing

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Documentação

Markdown and Mermaid Writing

Overview

This skill teaches you — and enforces a standard for — creating scientific documentation using markdown with embedded Mermaid diagrams as the default and canonical format.

The core bet: a relationship expressed as a Mermaid diagram inside a .md file is more valuable than any image. It is text, so it diffs cleanly in git. It requires no build step. It renders natively on GitHub, GitLab, Notion, VS Code, and any markdown viewer. It uses fewer tokens than a prose description of the same relationship. And it can always be converted to a polished image later — but the text version remains the source of truth.

"The more you get your reports and files in .md in just regular text, which mermaid is as well as being a simple 'script language'. This just helps with any downstream rendering and especially AI generated images (using mermaid instead of just long form text to describe relationships < tokens). Additionally mermaid can render along with markdown for easy use almost anywhere by humans or AI."

— Clayton Young (@borealBytes), K-Dense Discord, 2026-02-19

When to Use This Skill

Use this skill when:

  • Creating any scientific document — reports, analyses, manuscripts, methods sections
  • Writing any documentation — READMEs, how-tos, decision records, project docs
  • Producing any diagram — workflows, data pipelines, architectures, timelines, relationships
  • Generating any output that will be version-controlled — if it's going into git, it should be markdown
  • Working with any other skill — this skill defines the documentation layer that wraps every other output
  • Someone asks you to "add a diagram" or "visualize the relationship" — Mermaid first, always

Do NOT start with Python matplotlib, seaborn, or AI image generation for structural or relational diagrams. Those are Phase 2 and Phase 3 — only used when Mermaid cannot express what's needed (e.g., scatter plots with real data, photorealistic images).

🎨 The Source Format Philosophy

Why text-based diagrams win

What mattersMermaid in MarkdownPython / AI Image
Git diff readable❌ binary blob
Editable without regenerating
Token efficient vs. prose✅ smaller❌ larger
Renders without a build step❌ needs hosting
Parseable by AI without vision
Works in GitHub / GitLab / Notion⚠️ if hosted
Accessible (screen readers)✅ accTitle/accDescr⚠️ needs alt text
Convertible to image later✅ anytime— already image

The three-phase workflow

flowchart LR
    accTitle: Three-Phase Documentation Workflow
    accDescr: Phase 1 Mermaid in markdown is always required and is the source of truth. Phases 2 and 3 are optional downstream conversions for polished output.

    p1["📄 Phase 1<br/>Mermaid in Markdown<br/>(ALWAYS — source of truth)"]
    p2["🐍 Phase 2<br/>Python Generated<br/>(optional — data charts)"]
    p3["🎨 Phase 3<br/>AI Generated Visuals<br/>(optional — polish)"]
    out["📊 Final Deliverable"]

    p1 --> out
    p1 -.->|"when needed"| p2
    p1 -.->|"when needed"| p3
    p2 --> out
    p3 --> out

    classDef required fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#1e3a5f
    classDef optional fill:#fef9c3,stroke:#ca8a04,stroke-width:2px,color:#713f12
    classDef output fill:#dcfce7,stroke:#16a34a,stroke-width:2px,color:#14532d

    class p1 required
    class p2,p3 optional
    class out output

Phase 1 is mandatory. Even if you proceed to Phase 2 or 3, the Mermaid source stays committed.

What Mermaid can express

Mermaid covers 24 diagram types. Almost every scientific relationship fits one:

Use caseDiagram typeFile
Experimental workflow / decision logicFlowchartreferences/diagrams/flowchart.md
Service interactions / API calls / messagingSequencereferences/diagrams/sequence.md
Data model / schemaER diagramreferences/diagrams/er.md
State machine / lifecycleStatereferences/diagrams/state.md
Project timeline / roadmapGanttreferences/diagrams/gantt.md
Proportions / compositionPiereferences/diagrams/pie.md
System architecture (zoom levels)C4references/diagrams/c4.md
Concept hierarchy / brainstormMindmapreferences/diagrams/mindmap.md
Chronological events / historyTimelinereferences/diagrams/timeline.md
Class hierarchy / type relationshipsClassreferences/diagrams/class.md
User journey / satisfaction mapUser Journeyreferences/diagrams/user_journey.md
Two-axis comparison / prioritizationQuadrantreferences/diagrams/quadrant.md
Requirements traceabilityRequirementreferences/diagrams/requirement.md
Flow magnitude / resource distributionSankeyreferences/diagrams/sankey.md
Numeric trends / bar + line chartsXY Chartreferences/diagrams/xy_chart.md
Component layout / spatial arrangementBlockreferences/diagrams/block.md
Work item status / task columnsKanbanreferences/diagrams/kanban.md
Cloud infrastructure / service topologyArchitecturereferences/diagrams/architecture.md
Multi-dimensional comparison / skills radarRadarreferences/diagrams/radar.md
Hierarchical proportions / budgetTreemapreferences/diagrams/treemap.md
Binary protocol / data formatPacketreferences/diagrams/packet.md
Git branching / merge strategyGit Graphreferences/diagrams/git_graph.md
Code-style sequence (programming syntax)ZenUMLreferences/diagrams/zenuml.md
Multi-diagram composition patternsComplex Examplesreferences/diagrams/complex_examples.md

💡 Pick the right type, not the easy one. Don't default to flowcharts for everything. A timeline beats a flowchart for chronological events. A sequence beats a flowchart for service interactions. Scan the table and match.


🔧 Core workflow

Step 1: Identify the document type

Check if a template exists before writing from scratch:

Document typeTemplate
Pull request recordtemplates/pull_request.md
Issue / bug / feature requesttemplates/issue.md
Sprint / project boardtemplates/kanban.md
Architecture decision (ADR)templates/decision_record.md
Presentation / briefingtemplates/presentation.md
Research paper / analysistemplates/research_paper.md
Project documentationtemplates/project_documentation.md
How-to / tutorialtemplates/how_to_guide.md
Status reporttemplates/status_report.md

Step 2: Read the style guide

Before writing any .md file: read references/markdown_style_guide.md.

Key rules to internalize:

  • One H1 per document — the title. Never more.
  • Emoji on H2 headings only — one emoji per H2, none in H3/H4
  • Cite everything — every external claim gets a footnote [^N] with full URL
  • Bold sparingly — max 2-3 bold terms per paragraph, never full sentences
  • Horizontal rule after every </details> — mandatory
  • Tables over prose for comparisons, configurations, structured data
  • Diagrams over walls of text — if it describes flow, structure, or relationships, add Mermaid

Step 3: Pick the diagram type and read its guide

Before creating any Mermaid diagram: read references/mermaid_style_guide.md.

Then open the specific type file (e.g., references/diagrams/flowchart.md) for the exemplar, tips, and copy-paste template.

Mandatory rules for every diagram:

accTitle: Short Name 3-8 Words
accDescr: One or two sentences explaining what this diagram shows.
  • No %%{init} directives — breaks GitHub dark mode
  • No inline style — use classDef only
  • One emoji per node max — at the start of the label
  • snake_case node IDs — match the label

Step 4: Write the document

Start from the template. Apply the markdown style guide. Place diagrams inline with related text — not in a separate "Figures" section.

Step 5: Commit as text

The .md file with embedded Mermaid is what gets committed. If you also generated a PNG or AI image, those are supplementary — the markdown is the source.


⚠️ Common pitfalls

Radar chart syntax (radar-beta)

WRONG:

radar
title Example
x-axis ["A", "B", "C"]
"Series" : [1, 2, 3]

CORRECT:

radar-beta
title Example
axis a["A"], b["B"], c["C"]
curve series["Series"]{1, 2, 3}
max 3
  • Use radar-beta not radar (the bare keyword doesn't exist)
  • Use axis to define dimensions, not x-axis
  • Use curve to define data series, not quoted labels with colon
  • No accTitle/accDescr — radar-beta doesn't support accessibility annotations; always add a descriptive italic paragraph above the diagram

XY Chart vs Radar confusion

DiagramKeywordAxis syntaxData syntax
XY Chart (bars/lines)xychart-betax-axis ["Label1", "Label2"]bar [10, 20] or line [10, 20]
Radar (spider/web)radar-betaaxis id["Label"]curve id["Label"]{10, 20}

Forgetting accTitle/accDescr on supported types

Only some diagram types support accTitle/accDescr. For those that don't, always place a descriptive italic paragraph directly above the code block:

Radar chart comparing three methods across five performance dimensions. Note: Radar charts do not support accTitle/accDescr.

radar-beta
...

🔗 Integration with other skills

With scientific-schematics

scientific-schematics generates AI-powered publication-quality images (PNG). Use the Mermaid diagram as the brief for the schematic:

Workflow:
1. Create the concept as Mermaid in .md (this skill — Phase 1)
2. Describe the same concept to scientific-schematics for a polished PNG (Phase 3)
3. Commit both — the .md as source, the PNG as a supplementary figure

With scientific-writing

When scientific-writing produces a manuscript, all diagrams and structural figures should use this skill's standards. The writing skill handles prose and citations; this skill handles visual structure.

Workflow:
1. Use scientific-writing to draft the manuscript
2. For every figure that shows a workflow, architecture, or relationship:
   - Replace placeholder with a Mermaid diagram following this skill's guide
3. Use scientific-schematics only for figures that truly need photorealistic/complex rendering

With literature-review

Literature review produces summaries with lots of relationship data. Use this skill to:

  • Create concept maps (Mindmap) of the literature landscape
  • Show publication timelines (Timeline or Gantt)
  • Compare methodologies (Quadrant or Radar)
  • Diagram data flows described in papers (Sequence or Flowchart)

With any skill that produces output documents

Before finalizing any document from any skill, apply this skill's checklist:

  • Does the document use a template? If so, did I start from the right one?
  • Are all diagrams in Mermaid with accTitle + accDescr?
  • No %%{init}, no inline style, only classDef?
  • Are all external claims cited with [^N]?
  • One H1, emoji on H2 only?
  • Horizontal rules after every </details>?

📚 Reference index

Style guides

GuidePathLinesWhat it covers
Markdown Style Guidereferences/markdown_style_guide.md~733Headings, formatting, citations, tables, Mermaid integration, templates, quality checklist
Mermaid Style Guidereferences/mermaid_style_guide.md~458Accessibility, emoji set, color classes, theme neutrality, type selection, complexity tiers

Diagram type guides (24 types)

Each file contains: production-quality exemplar, tips specific to that type, and a copy-paste template.

references/diagrams/ — architecture, block, c4, class, complex_examples, er, flowchart, gantt, git_graph, kanban, mindmap, packet, pie, quadrant, radar, requirement, sankey, sequence, state, timeline, treemap, user_journey, xy_chart, zenuml

Document templates (9 types)

templates/ — decision_record, how_to_guide, issue, kanban, presentation, project_documentation, pull_request, research_paper, status_report

Examples

assets/examples/example-research-report.md — a complete scientific research report demonstrating proper heading hierarchy, multiple diagram types (flowchart, sequence, gantt), tables, footnote citations, collapsible sections, and all style guide rules applied.


📝 Attribution

All style guides, diagram type guides, and document templates in this skill are ported from the SuperiorByteWorks-LLC/agent-project repository under the Apache-2.0 License.

This skill (as part of scientific-agent-skills) is distributed under the MIT License. The included Apache-2.0 content is compatible for downstream use with attribution retained, as preserved in the file headers throughout this skill.


Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

adaptyv

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

alphagenome

Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

analytical-method-validation

Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include

anndata

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

arbor

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g.

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

autoskill

Observe the user

benchling-integration

Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

bgpt-paper-search

Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

bids

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biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

bulk-rnaseq

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g.

cellxgene-census

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

cirq

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

citation-management

Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

clinical-decision-support

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

clinical-reports

Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.

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