changelog

name	changelog
description	Write a new LLM Gateway changelog entry. Use when the user says "changelog", "changelog entry", "write a changelog", "add a changelog", "announce this feature", or asks to document a shipped feature for the public changelog. Produces the dated markdown file under apps/ui/src/content/changelog plus a gpt-image-2 prompt for the OpenGraph image.

Changelog

Write a public changelog entry for LLM Gateway, in the house style, and hand back a ready-to-run gpt-image-2 prompt for its OpenGraph image.

Paths below assume the LLM Gateway monorepo. Adapt them if your changelog lives elsewhere — the format, voice, and image guidance carry over.

What you need first

Before writing, make sure you understand the feature concretely. If the user only gave a feature name, find the facts — don't guess:

• Read the relevant docs page under apps/docs/content/ (e.g. features/<feature>.mdx).
• Inspect the shipping commit/PR if one is referenced: git show <sha> --stat, then read the changed UI/API/gateway files for the exact user-facing behavior.
• Confirm plan gating (free vs Pro vs Enterprise), exact field names, error codes, and any limits. The changelog must be accurate — never invent prices, limits, or capabilities.

Where entries live

• Entries: apps/ui/src/content/changelog/<YYYY-MM-DD>-<kebab-slug>.md
• Images: apps/ui/public/changelog/<kebab-slug>.png
• Schema is enforced by apps/ui/content-collections.ts — all frontmatter fields below are required (except draft).

Step 1 — Pick the date, id, and slug

• Date: today, YYYY-MM-DD. Entries sort by date descending, so this puts the entry at the top.
• id: the next integer after the current highest. Find it with:

  grep -h '^id:' apps/ui/src/content/changelog/*.md | sed 's/[^0-9]//g' | sort -n | tail -1
  

  Use that number + 1, as a string.
• slug: short kebab-case, feature-focused (e.g. custom-model-catalog). The slug must match the filename suffix and the image.src filename, and becomes the URL /changelog/<slug>.

Step 2 — Write the markdown file

Frontmatter (YAML), then the body. Entries are plain Markdown — no MDX/JSX components (no <Callout>); use bold inline notes instead.

---
id: "<next-id>"
slug: "<slug>"
date: "<YYYY-MM-DD>"
title: "<Title Case, ~3–7 words>"
summary: "<1–3 sentences: what shipped, the concrete benefit, and the plan if gated. This is the OG description and the listing blurb.>"
image:
  src: "/changelog/<slug>.png"
  alt: "<Descriptive alt text: the feature and what the image shows>"
  width: 1536
  height: 1024
---

<Opening paragraph: the problem this solves, then the one-line statement of what shipped. Bold the feature name once.>

## <Section header — verb-led or outcome-led>

<     /          >

---

 | 

House style (match existing entries)

Read the two or three most recent files in apps/ui/src/content/changelog/ before writing, and mirror their tone:

• Lead with the problem, then the fix. First paragraph names the pain; the next sentence states what shipped.
• Benefits over features, specific over vague. "about 50% cheaper", "rejected with 400 before any data leaves the gateway" — not "improved efficiency".
• Confident and plain. Active voice. No exclamation points. No "very/really/simply". No buzzwords ("streamline", "seamless", "revolutionary").
• Show the API. Include a realistic curl or JSON example when there's a request-level change. Use https://api.llmgateway.io/v1/... and $LLM_GATEWAY_API_KEY.
• Use a table for fields, options, strategies, or tiers.
• State plan gating explicitly (e.g. "Available on the Enterprise plan").
• Close with a footer link line: a docs link and one secondary CTA, separated by |, each as **[Label →](url)**.
• Keep section headers in ##. Keep it scannable — short paragraphs, bullets for lists of behaviors.

Step 3 — Produce the OpenGraph image prompt

Hand the user a single, ready-to-paste gpt-image-2 prompt that produces the OG image, plus where to save it.

Resolution. Target 1536×1024 (3:2 landscape). This is the recommended OpenGraph size for gpt-image-2: the dimensions are divisible by 16 (a gpt-image-2 requirement) and crop cleanly to the ~1.91:1 social card. Match the width/height in the frontmatter to whatever you generate. (Square 1024×1024 is acceptable for icon-style art but crops more on social cards — prefer landscape for OG.)

Prompt guidance. Write a small summary prompt — 2–4 sentences — that:

• Describes a clean, modern, abstract tech illustration that conveys the feature's concept (e.g. a catalog/ledger of model cards with price tags and guard rails for the model catalog). Concept over literalism.
• States the LLM Gateway brand feel: minimal, premium, soft gradients, subtle depth, plenty of negative space, suitable as a backdrop behind a title.
• Says "no text, no words, no letters, no UI chrome" — AI image text is unreliable; the title lives in the page, not the image.
• Specifies the aspect: "wide 3:2 landscape composition, 1536×1024".

Output the prompt in a fenced block, then the save path, e.g.:

Prompt for gpt-image-2 (1536×1024):

```
A clean, modern abstract illustration of <concept>, in the LLM Gateway brand
style: minimal and premium, soft gradients, subtle depth and glow, generous
negative space, balanced as a backdrop behind a headline. Wide 3:2 landscape
composition, 1536×1024. No text, no words, no letters, no UI chrome.
```

Save the result to: apps/ui/public/changelog/<slug>.png

Generate the image with gpt-image-2 (LLM Gateway playground, the Images API, or any gpt-image-2 client), then drop the PNG at that path. The entry references it via image.src; until the file exists the entry builds fine but the image 404s.

Step 4 — Validate

pnpm format
turbo run build --filter=ui

pnpm format normalizes the markdown; the ui build fails if the frontmatter doesn't match the content-collections schema. Then commit (conventional commit, ≤50-char title), e.g. docs(changelog): add custom model catalog entry.