Atlas / Skills / alinaqi / Build In Public

Build In PublicSAFE

skills/alinaqi/build-in-public

What started as an opinionated Claude Code setup kit is now an autonomous AI engineering command center

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
707
01

Overview

What started as an opinionated Claude Code setup kit is now an autonomous AI engineering command center

Read from source at commit 3a4a7596e97eOBSERVED · 2026-10-13
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: build-in-public
description: Best practices for sharing engineering work publicly — what to post, what to withhold, and channel-specific guidance
when-to-use: When drafting build-in-public posts, changelogs, or launch content for social channels
user-invocable: false
effort: low
---

# Build in Public — Best Practices

## Philosophy

Build in public isn't marketing. It's letting people watch you work. The best posts feel like you're narrating your thought process to a friend who's also a senior engineer. No hype sludge. No "I'm excited to announce." Just: here's what I built, here's why it matters, here's what I learned.

## What to Share (and What Not To)

**Share:**
- Technical decisions and the reasoning behind them ("Chose SQLite over Postgres because...")
- Architecture insights ("Here's how the 9-tier routing pipeline works")
- Failures and what you learned ("Spent 3 hours debugging a race condition. Root cause: ...")
- Before/after metrics ("Compaction death spirals: 26 → 0 after Mnemos")
- Open source releases with context, not just links
- Counter-intuitive findings that surprised you

**Never share:**
- Revenue numbers, user counts, valuation
- Customer names or identifiable client details
- Internal URLs, API keys, credentials
- Anything covered by NDA
- Roadmap promises you might not keep
- "We're hiring" disguised as content

## Channel-Specific Best Practices

### LinkedIn

**Audience:** Engineers, CTOs, founders who build. They scroll between meetings looking for something that makes them think.

**Format:**
- 1-3 paragraphs, 800-2000 characters sweet spot
- Lead with the insight, not the context
- Use line breaks generously — wall of text kills engagement
- One clear takeaway per post
- No external links in first paragraph (LinkedIn penalizes off-platform clicks)

**Tone:** Confident, teaches something. You're the senior engineer explaining your approach to a peer. No jargon without explanation. If you used a technique others might not know, explain it briefly.

**When to post:** Tuesday-Thursday, 8-10 AM in target timezone. Avoid weekends and Monday mornings (everyone's catching up).

**What works:**
- Technical deep dives with concrete code examples
- "How I built X" narratives with architecture diagrams
- Lessons learned from failures (these outperform success stories 3:1)
- Opinionated takes on industry trends (but only if you have data)

**What flops:**
- "Excited to announce" press releases
- Pure product updates without technical insight
- Motivational content without substance
- Posts longer than 2500 chars without strong hook

### X (Twitter)

**Audience:** Developers who ship. They scroll fast and judge faster. You have one sentence to earn their attention.

**Format:**
- 280 characters max
- No threads unless the insight genuinely needs 3+ posts
- Lead with the counter-intuitive or surprising element
- One idea per post. If you have two ideas, make two posts.
- Screenshots need alt-text describing what's shown

**Tone:** Sharp, opinionated, zero filler. Imagine you're texting a builder friend. If it sounds like marketing, delete it.

**When to post:** Tuesday-Friday, 9-11 AM or 2-4 PM in target timezone. Weekends can work for developer audience (they're building side projects).

**What works:**
- One-sentence technical insights ("The difference between a good API and a great one is error messages.")
- Before/after comparisons with metrics
- "Just shipped X. Here's the one thing that surprised me."
- Asking genuine technical questions (engagement bait backfires)

**What flops:**
- Hashtag stuffing
- Threads that could be one post
- Generic "hot take" without personal experience
- Posting links without context

## Content Calendar Rhythm

**Daily (if you have something to say):**
- One X post about what you're working on or learned today

**Weekly:**
- One LinkedIn post: deeper technical insight or project milestone

**Per event (triggered by plugin):**
- PR merged → LinkedIn within 24h, X same day
- Feature shipped → both channels, LinkedIn first, X 90 min later
- Review passed → X only (architecture insights are punchy)
- Major release → LinkedIn deep dive + X announcement

## Anti-Patterns to Avoid

1. **The "we" trap** — Solo builders using "we" sounds insecure. Use "I" unless you're actually a team.
2. **Engagement bait** — "Agree?" or "Thoughts?" at the end of every post reads as desperate. Let the insight stand alone.
3. **Posting without building** — If you haven't shipped in 2 weeks, don't post. Your content should be a byproduct of your work, not a substitute for it.
4. **The LinkedIn bro voice** — "I'm humbled and honored to share..." Delete immediately. You're not accepting an award.
5. **Over-polishing** — A post that sounds like it went through 5 rounds of editing reads as corporate. Ship the draft.
6. **Ignoring replies** — If someone takes time to engage, reply within 24h. The conversation in comments often outperforms the original post.

## Measuring What Works

Track these signals (Buffer, LinkedIn analytics, X analytics):
- **Impressions** — how many people saw it
- **Engagement rate** — (likes + comments + reposts) / impressions
- **Profile visits** — did the post drive people to learn more?
- **Inbound** — DMs, connection requests, or emails referencing specific posts

A good LinkedIn post: 3-5% engagement rate. A great one: 8%+. On X, anything above 2% is solid for technical content.

## Plugin Integration

The build-in-public plugin follows these practices automatically:

```yaml
# What gets shared vs skipped:
on_pr_merged:
  - Share if: >3 files changed, meaningful commit message
  - Skip if: typo fix, dependency bump, config change only

on_feature_shipped:
  - Share: always, with screenshot
  - LinkedIn: deep dive on architecture decisions
  - X: punchy one-liner on impact

on_review_passed:
  - Share if: 3/3 unanimous approval
  - X only: architecture insights are punchy
  - Skip if: 1/3 or 0/3 (revisions aren't share-worthy)
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-13 · audit v0.4.1 · source sha 3a4a7596e97efull audit observations/trust-audit/skill/alinaqi__build-in-public.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-133a4a7596e97eSAFEB89first audit
05

Questions

What does the Build In Public skill do?

What started as an opinionated Claude Code setup kit is now an autonomous AI engineering command center

Is Build In Public safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Build In Public access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (3a4a7596e97e), read on 2026-10-13. The repository is watched, and a new audit runs when it changes — this is the first audit.

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