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 skills add v0idOS/performance-deity --skill networkgit clone --depth 1 https://github.com/v0idOS/performance-deityWrote 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/v0idos/performance-deity/network)<a href="https://agentmods.dev/skills/v0idos/performance-deity/network"><img src="https://agentmods.dev/badge/skills/v0idos/performance-deity/network/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/v0idos/performance-deity/network"><img src="https://agentmods.dev/badge/skills/v0idos/performance-deity/network.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00032 | $0.00287 |
| Opus 5.5 | $0.00013 | $0.00115 |
| Sonnet 5.5 | $0.00006 | $0.00057 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
network 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 1mo 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.
What it actually says
Execute all four phases in order.
Phase 1 — Payload Profiling
- Identify the API endpoint.
- Measure the raw JSON response size in bytes.
- Identify the nested or repeated structures contributing the most bytes.
Phase 2 — Compression & Caching
- Check response headers. Verify
Content-Encoding: gziporContent-Encoding: bris present. If not, enable it server-side. - Add HTTP caching headers:
Cache-Control: max-age=<N>, stale-while-revalidate=<N>ETagfor conditional GET support
Phase 3 — Serialization
If the payload exceeds 1MB:
- Migrate to GraphQL so clients request only the fields they need, or
- Migrate to Protocol Buffers (protobuf) for binary serialization.
- For real-time data, use Server-Sent Events or WebSockets to eliminate repeated full-payload polling.
Phase 4 — Report
| Metric | Before | After |
|---|---|---|
| Payload size | X KB | Y KB |
| Transfer time (3G sim) | Xms | Yms |
| Cache hit rate | 0% | Z% |
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.
- 1mo ago First seen · 34 lines · 32 tokens per session scan A aa1409777ec9
network is a skill published in the GitHub repository v0idOS/performance-deity (2 stars, last pushed 5mo ago), licensed MIT. It adds 32 tokens to every session and 287 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
ash-framework
Ash Framework — resources, actions, policies, aggregates, calculations, AshPhoenix.Form, LiveView, migrations. Use when generating resources via mix ash.codegen, editing changes, checks, types, validations, or domain code interfaces.
deploy
Elixir/Phoenix deployment patterns — Dockerfile, fly.toml, runtime.exs, mix release, rel/ overlays. Use when configuring Fly.io, Docker, CI/CD, health checks, or production migrations.
liveview-patterns
Build LiveView: async data (assignasync), PubSub (check connected?), phx-change events, form components/modals/uploads, streams for lists, livepatch. Use when handling interactions, debugging events, or tracking Presence.
tidewave-integration
Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.
oban
Use when writing, scheduling, testing or debugging Oban jobs, even for a how-to question: workers, cron, retries, unique jobs, queues, Pro Workflow/Batch. Load it before touching job code; it holds the job rules.
perf
Use when anything in a Phoenix app is slow, times out, or uses too much memory, even if the cause looks obvious. Load it before analyzing or fixing: it runs measured Ecto, LiveView and OTP checks and ranks fixes.