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…
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,
$ npx -y skills add K-Dense-AI/scientific-agent-skills --skill markdown-mermaid-writing --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/markdown-mermaid-writingContext preview
The summary Claude sees to decide when to auto-load this skill.
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,
name: markdown-mermaid-writing description: 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. allowed-tools: Read Write Edit Bash license: Apache-2.0 metadata: version: "1.1" skill-author: Clayton Young / Superior Byte Works, LLC (@borealBytes) skill-source: https://github.com/SuperiorByteWorks-LLC/agent-project skill-version: 1.0.0 skill-contributors: Clayton Young (Superior Byte Works, LLC / @borealBytes; Author and originator); K-Dense Team (K-Dense Inc.; Integration target and community feedback)
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
Use this skill when:
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).
| What matters | Mermaid in Markdown | Python / 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 |
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.
Mermaid covers 24 diagram types. Almost every scientific relationship fits one:
| Use case | Diagram type | File | | -------------------------------------------- | ---------------- | ---------------------------------------------------- | | Experimental workflow / decision logic | Flowchart | `references/diagrams/flowchart.md` | | Service interactions / API calls / messaging | Sequence | `references/diagrams/sequence.md` | | Data model / schema | ER diagram | `references/diagrams/er.md` | | State machine / lifecycle | State | `references/diagrams/state.md` | | Project timeline / roadmap | Gantt | `references/diagrams/gantt.md` | | Proportions / composition | Pie | `references/diagrams/pie.md` | | System architecture (zoom levels) | C4 | `references/diagrams/c4.md` | | Concept hierarchy / brainstorm | Mindmap | `references/diagrams/mindmap.md` | | Chronological events / history | Timeline | `references/diagrams/timeline.md` | | Class hierarchy / type relationships | Class | `references/diagrams/class.md` | | User journey / satisfaction map | User Journey | `references/diagrams/user_journey.md` | | Two-axis comparison / prioritization | Quadrant | `references/diagrams/quadrant.md` | | Requirements traceability | Requirement | `references/diagrams/requirement.md` | | Flow magnitude / resource distribution | Sankey | `references/diagrams/sankey.md` | | Numeric trends / bar + line charts | XY Chart | `references/diagrams/xy_chart.md` | | Component layout / spatial arrangement | Bl
🔔 Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills standard, not just Claude.
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…
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection,…
Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP…
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…
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree…
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk…