Blog Writing Guide

Sentry Blog Writing Skill

This skill enforces Sentry's blog writing standards across every post — whether you're helping an engineer write their first blog post or a marketer draft a product announcement.

The bar: Every Sentry blog post should be something a senior engineer would share in their team's Slack, or reference in a technical decision.

What follows are the core principles to internalize and apply to every piece of content.

The Sentry Voice

We sound like: A senior developer at a conference afterparty explaining something they're genuinely excited about — smart, specific, a little irreverent, deeply knowledgeable.

We don't sound like: A corporate blog, a press release, a sales deck, or an AI-generated summary.

Be technically precise, opinionated, and direct. Humor is welcome but should serve the content, not replace it. Sarcasm works. One good joke per post is plenty.

Use "we" (Sentry) and "you" (the reader). This is a conversation, not a paper.

Banned Language

Never use these. They are automatic red flags:

• "We're excited/thrilled to announce" — just announce it
• "Best-in-class" / "industry-leading" / "cutting-edge" — show, don't tell
• "Seamless" / "seamlessly" — nothing is seamless
• "Empower" / "leverage" / "unlock" — say what you actually mean
• "Robust" — describe what makes it robust instead
• "At [Company], we believe..." — just state the belief
• "Streamline" — everyone is streamlining, stop
• Filler transitions: "That being said," "It's worth noting that," "At the end of the day," "Without further ado," "As you might know"
• "In this blog post, we will explore..." — be direct, just start

The Opening (First 2-3 Sentences)

The opening must do one of two things: state the problem or state the conclusion. Never start with background, company history, or hype.

Good: "Two weeks before launch, we killed our entire metrics product. Here's why pre-aggregating time-series metrics breaks down for debugging, and how we rebuilt the system from scratch."

Bad: "At Sentry, we're always looking for ways to improve the developer experience. Today, we're thrilled to share some exciting updates to our metrics product that we think you'll love."

Structure: Follow the Reader's Questions

Structure every post around what the reader is actually wondering, not your internal narrative:

1. What problem does this solve? (1-2 paragraphs max)
2. How does it actually work? Not buttons-you-click, but underlying technology. (Bulk of the post — be specific)
3. What were the trade-offs or alternatives? (This separates good from great)
4. How do I use/try/implement this? (Concrete next steps)

For engineering deep-dives, also address: 5. What did we try that didn't work? (Builds trust) 6. What are the known limitations? (Shows intellectual honesty)

Formatting for Skimmability

People scroll. Shorter paragraphs are almost always better for keeping people reading.

Break paragraphs at contrast points. When a sentence introduces a "but," "however," or shifts perspective, start a new paragraph. Don't bury the turn inside a block of text.

Bad:

Traditional monitoring tracks requests and latency. That works for stateless HTTP services. AI agents are different. A single run might involve multiple LLM calls, tool executions, and handoffs.

Good:

Traditional monitoring tracks requests and latency. That works for stateless HTTP services.

AI agents are different. A single run might involve multiple LLM calls, tool executions, and handoffs.

The line break before the contrasting point creates visual emphasis. This is standard in online writing even though it breaks traditional paragraph rules.

One idea per paragraph. If a paragraph covers two distinct points, split it. Three-sentence paragraphs are fine. One-sentence paragraphs are fine for emphasis.

No em dashes. Use commas, periods, or line breaks instead. Em dashes are fine in print but create visual clutter in blog formatting.

SEO for Developer Content

When targeting a competitive search query:

Lead generic, close specific. The first 50-60% of the post should be tool-agnostic educational content (definitions, concepts, metrics, best practices). Introduce your product as an implementation example in the second half. Google ranks guides higher than product pages for informational queries.

Put keywords in H2s. Generic headings are invisible to search. "Key metrics for AI agent monitoring" beats "What to measure." (See Section Headings below for good/bad examples.)

Include a definitional section. For any head term ("agent observability", "error monitoring"), top-ranking pages almost always have a "What is X?" section. Include one even if it feels basic.

Add an FAQ. 3-4 questions targeting long-tail keywords at the bottom of the post. These can win featured snippets and People Also Ask boxes.

AI Writing Patterns to Avoid

LLM-generated prose has tells. Flag and rewrite these:

Staccato dramatic fragments.

• Bad: "No errors. No warnings. Everything green."
• Good: "There were no errors, no warnings, everything looked fine."

Bumper-sticker aphorisms.

• Bad: "You can't fix what you can't see."
• Good: "Without visibility into the full request lifecycle, you're guessing."

Three-beat reveals.

• Bad: "Not a config issue. Not a code bug. The deploy was stale."
• Good: "It wasn't a config issue or a code bug. The deploy was stale."

Smug simplicity.

• Bad: [code block] "That's it. That's all you need."
• Good: [code block] then explain what the code does, or just move on.

Parallel structure ad copy.

• Bad: "Metrics tell you what's broken. Traces tell you why."
• Good: "Metrics show what's broken, but traces are where you'll actually figure out why."

Personality only in the bookends. AI drafts open with a personal anecdote, go impersonal for 80% of the post, then close with a CTA. The author's voice should persist throughout.

• Bad: Personal intro → clinical middle → "Try Sentry for free."
• Good: First-person asides woven through the post: "this is the part that tripped me up" / "I would have blamed the wrong service."

Section Headings Must Convey Information

Weak: "Background," "Architecture," "Results," "Conclusion"

Strong: "Why time-series pre-aggregation destroys debugging context," "The scatter-gather approach to distributed GROUP BY," "Where this breaks down: the cardinality wall"