Daily AI Zine
Friday, September 18, 2026
Issue No. 063 · Tokyo · full edition

✦ Today's Big Thing

GitHub starts measuring the agentic CLI layer

Today’s useful shift is measurement: agent skills, custom agents, MCP servers, slash commands, and plugins can move from terminal folklore into usage data.

4 min read · 7 sections

In Brief
  1. GitHub added agentic CLI customization fields to the Copilot usage metrics API.
  2. GitHub Actions Ubuntu 26.04 runners are now generally available for production workflows.
  3. Hot signal: GitHub says Copilot helped move an 800,000-line agent runtime into production Rust.
  4. Runway’s Gen-4.5 listing is worth a creative workflow watch, but not a production bet yet.

Today's Big Thing

The one thing that matters

Use it

GitHub exposes the hidden agentic CLI layer

GitHub added agentic CLI customization fields to the Copilot usage metrics API, covering skills, custom agents, MCP servers, slash commands, and plugins. This is a useful shift rather than a platform reset: the agent work that matters is no longer just who used Copilot, but which custom work surfaces are being used. For teams shipping with GitHub, MCP, and coding agents, this creates a practical control loop: keep the useful customizations, retire dead ones, and spot where agent work is trapped inside one person’s terminal setup. Use it as a measurement layer before adding more agent tooling.

My AI Ecosystem

Your actual stack

Use it

GitHub Actions Ubuntu 26.04 is production-ready

GitHub says the Ubuntu 26.04 runner image for GitHub Actions is now generally available and fully supported for production workflows on x64 and arm64. For repos that already depend on GitHub Actions, this is a normal but useful maintenance move: test one workflow, then migrate deliberately rather than waiting for runner drift to break builds.

Test it

Hot signal: GitHub used agents on an 800,000-line Rust migration

GitHub says porting the Copilot agent runtime to 800,000 lines of production Rust was not affordable before agents. The useful signal is not Rust itself. It is that agent-assisted rewrites are moving from toy demos into core infrastructure work. Test the pattern only on bounded internal modules with tests, reviews, and rollback.

Watch it

Runway’s Gen-4.5 is a video-model watch item

Runway lists Gen-4.5 as a video model focused on motion quality, prompt adherence, and visual fidelity. Treat the claim as a creative signal, not proof. For production workflow, the useful test is narrow: can it hold camera motion, object continuity, and brand style across a short concept cut without manual rescue.

CoWork Corner

Claude CoWork, day to day

CoWork has no T1 product change to act on today

Claude CoWork is the workspace Adrian uses for persistent projects, operational records, and AI-assisted workflows. I found no T1, CoWork-specific product change in today’s eligible sources. Revisit a safer workflow instead: keep executable steps out of open-ended project memory and route them through a named MCP lane with a narrow purpose. MCP means Model Context Protocol, the connector pattern that lets Claude reach approved tools. Use it for one bounded job, such as reading a folder or running a report, then review outputs before expanding access.

Tier 4 · quiet day, honest fallback

GPT Desk

OpenAI, ChatGPT, Codex

Test it

Turn OpenAI-style security triage into one reviewed patch lane

Cloudflare says Managed Defense combines production traffic and security signals with OpenAI Daybreak models to prioritize findings, prepare edge mitigations when safe, and propose code patches. Adrian should care because Grey Group OS, Netlify sites, and client microsites need a lightweight way to turn noisy security work into reviewed fixes. Today, create one GitHub template with four fields: finding, traffic evidence, proposed mitigation, and Codex patch branch for human approval.

Use it

Use OpenAI’s workflow case studies as an ops handoff prompt

OpenAI describes AI-native companies using agents to improve onboarding, account management, and developer integrations. Adrian should care because Goodsense, SET, and Grey Group OS all benefit when repeatable client work becomes an operating capability rather than a fresh scramble. Today, choose one proposal or onboarding handoff and ask ChatGPT to convert it into a checklist with owner, trigger, input, output, and review gate.

Small Money Systems

Small, repeatable, real

Test it

Small system: security triage mini-retainer

System: build a monthly AI-assisted security triage service for small GitHub and Netlify web properties. Customer: small production companies, local brands, and founders with public sites but no dedicated security staff. Offer: they receive a one-page priority report and a reviewed patch queue, not a vague scan dump. Price: ¥55,000 per month. Existing assets: Grey Group OS, GitHub workflows, Netlify deployment habits, and Adrian’s Codex review loop. AI workflow: Codex drafts patch branches from a triage template, while ChatGPT summarizes risk in client language. First action: make one sample report from an owned site today. Repeatability: reuse the same checklist monthly. Effort: one hour. Expected value: recurring retainer revenue and fewer emergency fixes.

Build Next

Deployable now

Test it

Build a CLI customization drift checker

What to build: a small scheduled report that pulls GitHub Copilot usage metrics for skills, custom agents, MCP servers, slash commands, and plugins, then flags no-use, sudden spikes, and orphaned MCP servers. Why now: GitHub added those agentic CLI customization fields to the usage metrics API. Effort: half day. Expected impact: cleaner agent setups, better spend control, and fewer invisible terminal-only workflows. Dependencies: GitHub Copilot usage metrics API access, consistent naming for custom agents, and at least one repo where agent work already happens.

Try This Today

One action, right now

Open one repo and list every custom agent, MCP server, slash command, plugin, and skill currently in use. Mark each one as keep, merge, retire, or unknown. This gives the new GitHub metrics a clean operating baseline.