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mike47512

Edge Computing

作者 mike47512 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install edge-computing
功能描述
Deep edge computing workflow—what runs at edge vs origin, caching, KV and data locality, security, limits, and latency validation. Use when deploying to CDN/...
使用说明 (SKILL.md)

Edge Computing

Edge runtimes move logic closer to users—with strict CPU/time limits, different APIs than full Node, and tenant isolation requirements.

When to Offer This Workflow

Trigger conditions:

  • Auth, redirects, or personalization at the CDN layer
  • HTML rewriting, A/B assignment, or bot mitigation at the edge
  • Global latency SLOs for read-heavy paths

Initial offer:

Use six stages: (1) workload fit, (2) edge vs origin split, (3) data & state, (4) security & tenancy, (5) limits & cost, (6) testing & rollout). Confirm platform (Workers, Lambda@Edge, Fastly Compute, etc.).

Stage 1: Workload Fit

Goal: Prefer short, CPU-light, request-scoped work—not long jobs or huge body buffering.

Exit condition: Explicit list of what remains on origin (heavy SSR, large uploads).

Stage 2: Edge vs Origin Split

Goal: Document what runs where: geo headers, redirects, cache key logic, A/B bucketing, partial HTML injection.

Practices

  • Cache Vary and cookie behavior documented to avoid wrong personalization leakage

Stage 3: Data & State

Goal: If using edge KV/Durable Objects/regional stores, state consistency (eventual vs strong) and rate of round-trips to origin.

Stage 4: Security & Tenancy

Goal: Validate JWT/session at edge; isolate tenants; never embed secrets in deploy bundles visible to clients.

Stage 5: Limits & Cost

Goal: Wall-clock CPU limits, request size caps, egress pricing; graceful degradation or fallback to origin.

Stage 6: Testing & Rollout

Goal: Canary per region/PoP; synthetics from multiple locations; compare p95 vs origin-only path.

Final Review Checklist

  • Workload fits edge constraints
  • Edge vs origin responsibilities documented
  • State/consistency strategy clear
  • Multi-tenant security reviewed
  • Limits, cost, fallback documented
  • Multi-region validation performed

Tips for Effective Guidance

  • Edge runtimes differ from full Node—verify available APIs (fs, streams, crypto).
  • Read platform-specific cold-start and isolate model docs.

Handling Deviations

  • Hybrid: edge for headers/cache only; heavy compute stays on origin.
安全使用建议
This skill is a generic, read-only workflow checklist for edge deployments and appears coherent and low-risk. Before installing: verify you trust the (unknown) author if you plan to convert guidance into automated scripts; avoid adding secrets or platform credentials into any automation derived from these instructions; when implementing platform-specific changes, follow official vendor docs and review any code you or an agent generates for network calls or secret use. If you want the agent to act (not just advise), restrict its permissions and review generated code before deployment.
功能分析
Type: OpenClaw Skill Name: edge-computing Version: 1.0.0 The skill bundle contains only metadata and a markdown file (SKILL.md) providing a structured workflow for edge computing deployments. There is no executable code, no network activity, and no instructions that could be interpreted as prompt injection or malicious behavior. The content is purely informational and aligns with its stated purpose.
能力评估
✓ Purpose & Capability
The skill name and description match the SKILL.md content: it provides architecture and rollout guidance for CDN/edge deployments. It does not request unrelated binaries, env vars, or accesses that would be incoherent with an advisory workflow.
✓ Instruction Scope
The instructions are high-level procedural guidance (six stages, checklist, tips). They do not instruct the agent to read system files, access credentials, contact external endpoints, or perform other actions outside advisory scope.
✓ Install Mechanism
No install spec and no code files are present (instruction-only). That minimizes on-disk risk; nothing is downloaded or executed by the skill itself.
✓ Credentials
The skill declares no required environment variables, credentials, or config paths. There is no disproportionate request for secrets or unrelated service keys.
✓ Persistence & Privilege
The skill is not marked always:true and does not request persistent system presence or modify other skills. Agent autonomous invocation is allowed by platform default but not escalated by this skill.
如何使用
  1. 确保已安装 OpenClaw（本地或 Docker 部署）
  2. 在对话框中输入安装命令：/install edge-computing
  3. 安装完成后，直接呼叫该 Skill 的名称或使用 /edge-computing 触发
  4. 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Initial release of edge-computing skill. - Provides a six-stage workflow for deploying workloads to CDN/edge environments (Cloudflare Workers, Lambda@Edge, etc.). - Covers workload suitability, edge vs. origin split, data/state strategy, security and tenancy, limits/cost, and testing. - Includes checklists, best practices, and platform-specific tips. - Designed for teams deploying logic at the edge versus traditional origin infrastructure.
元数据
Slug edge-computing
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Edge Computing 是什么？

Deep edge computing workflow—what runs at edge vs origin, caching, KV and data locality, security, limits, and latency validation. Use when deploying to CDN/... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 166 次。

如何安装 Edge Computing？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install edge-computing」即可一键安装，无需额外配置。

Edge Computing 是免费的吗？

是的，Edge Computing 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

Edge Computing 支持哪些平台？

Edge Computing 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 Edge Computing？

由 mike47512（@mike47512）开发并维护，当前版本 v1.0.0。

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