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mike47512

Edge Computing

by mike47512 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install edge-computing
Description
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/...
README (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.
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install edge-computing
  3. After installation, invoke the skill by name or use /edge-computing
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
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.
Metadata
Slug edge-computing
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Edge Computing?

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/... It is an AI Agent Skill for Claude Code / OpenClaw, with 166 downloads so far.

How do I install Edge Computing?

Run "/install edge-computing" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Edge Computing free?

Yes, Edge Computing is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Edge Computing support?

Edge Computing is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Edge Computing?

It is built and maintained by mike47512 (@mike47512); the current version is v1.0.0.

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