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IcodeNetIcodeNet/agent-skills/skills/system-design/SKILL.md

system-design

Skill0 starsChanged 4 months agoMITView on GitHub

Designs services, APIs, and data models from requirements. Use when designing a new service, API, or data model, or when doing system design for a greenfield or major new capability. Do not use for a small local design choice inside an existing module (use architecture).

64 other projects use a version of this.

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Projects with it also use data-pipelines, data-engineering and 9 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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  • travisjneumanSkill

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  • wpankSkill

    caching

    Caching strategies, invalidation, eviction policies, HTTP caching, distributed caching, and anti-patterns. Use when designing cache layers, choosing eviction policies, debugging stale data, or optimizing read-heavy workloads.

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  • mmondoraSkill

    infrastructure-as-code

    Infrastructure as Code with Terraform and Pulumi. State management, modularity, drift detection, secrets handling. Use when provisioning cloud resources, writing Terraform/Pulumi modules, or managing infrastructure configuration.

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  • ayudaySkill

    autoplan

    Auto-review pipeline — reads the full CEO, design, and eng review skills from disk and runs them sequentially with auto-decisions using 6 decision principles. Surfaces taste decisions (close approaches, borderline scope, codex disagreements) at a final approval gate. One command, fully reviewed plan out. Use when asked to "auto review", "autoplan", "run all reviews", "review this plan automatically", or "make the decisions for me". Proactively suggest when the user has a plan file and wants to run the full review gauntlet without answering 15-30 intermediate questions.

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  • mjunaidcaSkill

    kubernetes

    Production-grade Kubernetes manifests and debugging for containerized applications. This skill should be used when users ask to deploy to Kubernetes, create K8s manifests, containerize for K8s, set up Deployments/Services/Jobs/StatefulSets/CronJobs, create namespaces with resource quotas, set up multi-team isolation, configure ResourceQuota/ LimitRange, secure with RBAC (ServiceAccount, Role, RoleBinding), configure init containers (model download, db wait, migrations), set up sidecars (logging, metrics), or debug pods (CrashLoopBackOff, logs, exec, describe, events). Auto-detects from Dockerfile/code, generates hardened manifests with educational comments. CKAD-aligned.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 50 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Prefer boring technology that fits the existing stack unless requirements force otherwise.

    line 27
  2. Distinctive:

    Do not over-design for hypothetical scale.

    line 28

“Common” is measured: kept by at least 6 in 10 of the 50 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

System Design

Turn requirements into a coherent design: boundaries, contracts, data, and failure modes.

Workflow

  1. Requirements — functional goals, non-functionals (latency, consistency, tenancy, offline), explicit non-goals.
  2. Context — actors, existing systems, trust boundaries. Sketch a simple topology.
  3. Contracts — public APIs/events first (schemas, errors, versioning). See references/design-checklist.md.
  4. Data — entities, ownership, consistency model, migrations story.
  5. Failure & scale — timeouts, retries/idempotency, backpressure, authn/z, observability.
  6. Options — 2 approaches with trade-offs; pick one; list open questions.
  7. Hand off — ADRs for major decisions; implementation slices that are testable.

Constraints

  • Design for operability (health, logs, metrics) not only happy path.
  • Prefer boring technology that fits the existing stack unless requirements force otherwise.
  • Do not over-design for hypothetical scale.

Verification

  • Requirements and non-goals written
  • Topology + trust boundaries clear
  • API/data contracts sketched
  • Failure modes and auth addressed
  • Decision + open questions recorded

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