/install edge-computing
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
Varyand 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.
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install edge-computing - After installation, invoke the skill by name or use
/edge-computing - Provide required inputs per the skill's parameter spec and get structured output
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.