Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/arbazkhan971/godmode/edgenpx skills add arbazkhan971/godmode --skill edgegit clone --depth 1 https://github.com/arbazkhan971/godmodeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/arbazkhan971/godmode/edge)<a href="https://agentmods.dev/skills/arbazkhan971/godmode/edge"><img src="https://agentmods.dev/badge/skills/arbazkhan971/godmode/edge.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.01911 |
| Opus 5 | $0.00014 | $0.00955 |
| Sonnet 5 | $0.00005 | $0.00382 |
| Haiku 4.5 | $0.00003 | $0.00191 |
Grade A, and why
edge scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Edge — Edge Computing & Serverless
Activate When
- User invokes
/godmode:edge - User says "edge function", "Cloudflare Workers", "Vercel Edge", "serverless API", "AWS Lambda"
- User says "optimize cold start", "reduce latency"
- User says "edge caching", "Durable Objects", "KV store"
Workflow
Step 1: Discovery & Context
# Detect platform config
ls wrangler.toml vercel.json serverless.yml \
template.yaml deno.json 2>/dev/null
# Check bundle size (warn if > 1MB)
du -sh dist/ build/ .output/ 2>/dev/null
# Check for platform CLIs
which wrangler vercel serverless sam 2>/dev/null
EDGE DISCOVERY:
Platform: Cloudflare | Vercel | Deno Deploy |
AWS Lambda | GCP Cloud Functions | Azure Functions
Runtime: V8 isolates (edge) | Node.js | Deno | WASM
Latency target: <ms — e.g., p99 < 50ms>
State needs: stateless | KV | durable objects | DB
Budget: <cost ceiling per million requests>
IF platform not specified: ask user
IF bundle > 1MB: flag cold start risk
IF latency target < 50ms: recommend edge runtime
Step 2: Edge Function Design
ARCHITECTURE:
Client → Edge PoP (~300 locations) → Origin
Edge function constraints:
CPU time limit: 10-50ms (platform dependent)
Bundle size limit: 1MB (CF Workers), 4MB (Vercel)
No Node.js globals in edge (Buffer, fs, process)
WHEN to use edge vs serverless:
Edge: latency-critical, geolocation, auth, A/B test
Serverless: CPU-heavy, long-running, DB-intensive
Step 3: Cold Start Optimization
COLD START BENCHMARKS:
| Runtime | Typical Cold Start |
|--------------|-------------------|
| V8 isolate | < 5ms |
| Node.js 20 | 100-300ms |
| Python 3.12 | 150-400ms |
| Java 21 | 500-3000ms |
| .NET 8 | 200-500ms |
OPTIMIZATION CHECKLIST:
- Bundle size < 1MB (tree-shake, remove unused deps)
- Lazy-initialize DB connections and heavy modules
- Use ESM imports (faster parse than CJS)
- Provisioned concurrency for p99 < 100ms targets
- IF cold start > 200ms: profile with --cpu-prof
- IF bundle > 5MB: audit deps with bundlephobia
THRESHOLDS:
Target cold start: < 200ms
Target bundle: < 1MB (edge), < 5MB (Lambda)
Target p99 latency: < 100ms (edge), < 500ms (Lambda)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 253 lines · 27 tokens per session scan A 85d2ee8b1223
edge is a skill published in the GitHub repository arbazkhan971/godmode (26 stars, last pushed 8d ago), licensed MIT. It adds 27 tokens to every session and 1,911 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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