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/chadixearth/graphyloop/deployment-patternsnpx skills add chadixearth/graphyloop --skill deployment-patternsgit clone --depth 1 https://github.com/chadixearth/graphyloopWhat 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 | $0.00031 | $0.03181 |
| Opus 5 | $0.00015 | $0.01590 |
| Sonnet 5 | $0.00006 | $0.00636 |
| Haiku 4.5 | $0.00003 | $0.00318 |
Grade A, and why
deployment-patterns scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
CMD wget --no-verbose --tries=1 --spider http://localhost:3000/health || exit 1 This is a copy
86% identical to deployment-patterns — 68 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 461 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deployment Patterns
Production deployment workflows and CI/CD best practices.
When to Activate
- Setting up CI/CD pipelines
- Dockerizing an application
- Planning deployment strategy (blue-green, canary, rolling)
- Implementing health checks and readiness probes
- Preparing for a production release
- Configuring environment-specific settings
Deployment Strategies
Rolling Deployment (Default)
Replace instances gradually — old and new versions run simultaneously during rollout.
Instance 1: v1 → v2 (update first)
Instance 2: v1 (still running v1)
Instance 3: v1 (still running v1)
Instance 1: v2
Instance 2: v1 → v2 (update second)
Instance 3: v1
Instance 1: v2
Instance 2: v2
Instance 3: v1 → v2 (update last)
Pros: Zero downtime, gradual rollout Cons: Two versions run simultaneously — requires backward-compatible changes Use when: Standard deployments, backward-compatible changes
Blue-Green Deployment
Run two identical environments. Switch traffic atomically.
Blue (v1) ↠traffic
Green (v2) idle, running new version
# After verification:
Blue (v1) idle (becomes standby)
Green (v2) ↠traffic
Pros: Instant rollback (switch back to blue), clean cutover Cons: Requires 2x infrastructure during deployment Use when: Critical services, zero-tolerance for issues
Canary Deployment
Route a small percentage of traffic to the new version first.
v1: 95% of traffic
v2: 5% of traffic (canary)
# If metrics look good:
v1: 50% of traffic
v2: 50% of traffic
# Final:
v2: 100% of traffic
Pros: Catches issues with real traffic before full rollout Cons: Requires traffic splitting infrastructure, monitoring Use when: High-traffic services, risky changes, feature flags
Docker
Multi-Stage Dockerfile (Node.js)
# Stage 1: Install dependencies
FROM node:22-alpine AS deps
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm ci --production=false
# Stage 2: Build
FROM node:22-alpine AS builder
WORKDIR /app
COPY --from=deps /app/node_modules ./node_modules
COPY . .
RUN npm run build
RUN npm prune --production
# Stage 3: Production image
FROM node:22-alpine AS runner
WORKDIR /app
RUN addgroup -g 1001 -S appgroup && adduser -S appuser -u 1001
USER appuser
COPY --from=builder --chown=appuser:appgroup /app/node_modules ./node_modules
COPY --from=builder --chown=appuser:appgroup /app/dist ./dist
COPY --from=builder --chown=appuser:appgroup /app/package.json ./
ENV NODE_ENV=production
EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
CMD wget --no-verbose --tries=1 --spider http://localhost:3000/health || exit 1
CMD ["node", "dist/server.js"]
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.
- 2d ago First seen · 461 lines · 31 tokens per session scan A 0574363afba8
deployment-patterns is a skill published in the GitHub repository chadixearth/graphyloop (2 stars, last pushed 16d ago), licensed MIT. It adds 31 tokens to every session and 3,181 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to deployment-patterns, differing in 68 lines, and is treated as a copy.
Other skills, from other repositories
agent-release-swarm
Agent skill for release-swarm - invoke with $agent-release-swarm.
agent-pagerank-analyzer
Agent skill for pagerank-analyzer - invoke with $agent-pagerank-analyzer.
agent-code-analyzer
Agent skill for code-analyzer - invoke with $agent-code-analyzer.
agent-neural-network
Agent skill for neural-network - invoke with $agent-neural-network.
agui-dotnet-streaming-chat
Get started with the AG-UI .NET SDK: bootstrap and run your first streaming-chat app (client + server) with the AG-UI .NET NuGet packages (AGUI.Client, AGUI.Server, AGUI.Formatting, AGUI.Abstractions). USE FOR: which packages to install and how to wire them; constructing an AGUIChatClient against an endpoint and…
agui-dotnet-sample-step
Add a GettingStarted sample Step (a Server/Client pair) to the AG-UI .NET SDK that demonstrates one protocol feature the way we want users to write it. USE FOR: adding a new samples/GettingStarted/StepNN Server+Client pair, wiring it into AGUI.slnx and the integration-test project, giving it a deterministic…