Best for
- Use this skill when the user wants to start a new project, refactor an existing one, or discusses high-level system design.
yun520-1/mark-heartflow-skill/skills/system-architect/SKILL.md
Acts as a Senior System Architect to design robust, scalable, and maintainable software architectures. Enforces industry standards (PEP 8 for Python, ESLint for JS/TS), modular design, and security best practices. Use this skill when the user wants to start a new project, refactor an existing one, or discusses high-level system design.
Decision brief
Acts as a Senior System Architect to design robust, scalable, and maintainable software architectures. Enforces industry standards (PEP 8 for Python, ESLint for JS/TS), modular design, and security best practices.
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/yun520-1/mark-heartflow-skill --skill "skills/system-architect"Inspect the Agent Skill "system-architect" from https://github.com/yun520-1/mark-heartflow-skill/blob/20bbbb4eacf56c941ddc3420dcbc81c04d55ec5c/skills/system-architect/SKILL.md at commit 20bbbb4eacf56c941ddc3420dcbc81c04d55ec5c. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Role: You are a strict but helpful Technical Lead.
1. Project Scaffolding: Create standard directory layouts. 2. Tech Stack Selection: Recommend tools based on requirements (e.g. Flask vs FastAPI, React vs Vue). 3. Code Standards: Provide pylintrc, .eslintrc, .editorconfig templates. 4. Documentation: Generate README.md and ARCH…
Always prioritize Security and Scalability.
Python Standards
Permission review
The documentation asks the agent to create, modify, or delete local files.
**Project Scaffolding**: Create standard directory layouts.Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 67/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 36 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
pylintrc, .eslintrc, .editorconfig templates.README.md and ARCHITECTURE.md templates.Alternatives
affaan-m/ECC
Read a plan document, decompose it into steps, design a per-step agent chain from the ECC catalogue, and emit ready-to-paste /orchestrate custom prompts. Generative only — never invokes /orchestrate itself. Use when the user has a multi-step plan and wants to drive it through orchestrate without composing chains by hand.
K-Dense-AI/scientific-agent-skills
Use when working directly with the `esm` Python SDK, ESM3 or ESMC model IDs, Forge/Biohub inference clients, or ESMFold2 folding workflows.
K-Dense-AI/scientific-agent-skills
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.
K-Dense-AI/scientific-agent-skills
Access a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecu