u/According-Stable4487

every win-back email template I've seen leans on "we miss you" or "don't miss out" language that feels a little desperate. made a ChatGPT prompt that leads with what actually changed in the product instead, and treats the incentive as a low-friction reason to look again rather than the whole pitch. also spits out a short SMS-length version and a follow-up nudge for free. works for apps, courses, subscriptions, anything with a lapsed-user problem.

Every testimonial request email I used to send sounded like it came from a corporate feedback survey — "we'd love to hear your thoughts!" — and barely anyone replied. Built a prompt that references the actual specific result the client got and gives them easy guiding questions instead of a blank box to write in.

Sent it to a client yesterday whose site redesign doubled her form submissions. She replied in 20 minutes with a full paragraph I basically just had to copy-paste.

The "guiding questions" part is doing most of the work honestly — most people don't know what to write, they just need three easy questions to answer.

Our team's process docs were basically tribal knowledge — someone would just explain it verbally and hope it stuck. Finally sat down and built a prompt that takes messy rough notes and turns them into a real SOP: purpose, steps, common mistakes, definition of done, all of it.

Ran it on our refund process first since that one caused the most confusion. Took the answer, pasted it into Notion, done in about 4 minutes what used to be a 30 minute "let me explain this to you" conversation.

Mostly using it for onboarding new hires now so I stop repeating myself in Slack every time someone joins.

Been sitting on old podcast/webinar recordings for months because cutting clips manually takes forever. Built a prompt that takes a transcript and spits out 5 clip ideas — hook line, why it works, caption, even a suggested length per platform.

Ran it on a 40-minute recording last night and had a week of TikTok posts picked out in like 3 minutes. The "why it works" part is honestly the most useful bit, didn't expect that.

Not linking it here, don't want this to look like an ad. Just wanted to share since I've seen this exact "how do I repurpose long content" question a few times this week.

If you're using Claude Code, there's a feature called Skills that lets you add custom behaviors that activate automatically based on what you say.

What it is

A Skill is a SKILL.md file you drop into ~/.claude/skills/<skill-name>/SKILL.md. Claude Code picks it up automatically — no restart, no config.

How it activates

The file has a description field in its frontmatter. Claude reads that and triggers the skill when your request matches. If the description says "use when writing a PR description", saying "write my PR" will auto-load it.

You can also trigger manually with /skill-name.

What goes inside

The body is markdown — steps, instructions, output format, rules. You can inject live shell context with !command syntax — runs the command and feeds the output to Claude before it responds.

Example:

---
description: Audits code for OWASP Top 10 security issues.
allowed-tools: Read, Grep, Glob
---
## Security Scanner
Read the files the user mentions. Check for SQL injection, hardcoded secrets, missing auth...

Why it's useful

Instead of pasting the same long prompt every time, you install it once and it's just there. Works great for recurring tasks: PR descriptions, commit messages, security audits, code review checklists.

The format follows the Agent Skills open standard so skills work across runtimes, not just Claude Code.

Anyone else building custom skills? Curious what workflows people are automating.

Spent the last few weeks testing different LinkedIn outreach approaches with GPT and finally landed on something that works consistently.

Most AI-generated DM sequences fail for the same reason: they cram too much into one message. The AI stuffs a pitch into message 1 and it immediately reads as spam.

The structure that actually worked for me:

• Message 1: connection request, zero pitch, reference something specific about them
• Message 2: industry insight, no ask
• Message 3: genuinely useful tip, build credibility
• Message 4: soft pitch + one discovery question
• Message 5: low-pressure close

I built a prompt around this. You fill in your target role, industry, what you sell, the main pain you solve, and the campaign goal. It spits out all 5 messages.

The thing that made the biggest difference: I forced myself to mention each variable only once across the entire prompt. When you repeat variables, GPT starts echoing them in weird places and it sounds off. One mention per variable = cleaner, more human-sounding output.

Tested it across B2B SaaS, fintech, consulting, eComm contexts. Consistently sounds like someone who actually did their homework on the prospect.

Happy to share the full structure here if anyone wants it.

been testing prompt structures for linkedin posts. the usual outputs are painful — very "I am delighted to announce" energy.

spent an evening on this. added specific tone constraints, banned the first line from starting with "I", forced a concrete number somewhere in the post. results got noticeably better.

tested it on a client in SaaS. post was about onboarding failures. got 40-something comments, mostly from people who actually worked in the space. one person said it was the most honest thing they'd read on the topic in months.

happy to share the structure here if anyone wants to pick it apart

Been using ChatGPT for short-form video hooks and kept getting the same 2-3 patterns regardless of how I worded the prompt. The fix was obvious once I noticed it: I was asking for "a hook" instead of separating by hook type.

Switched to a prompt that generates 10 types at once — shock stat, story, curiosity gap, mistake/warning, transformation, controversy, relatability, how-to, POV, trend-jacking — each with the exact 15-word opening line plus a note on why it works for that specific audience.

Also found that setting a specific video goal (drive saves vs drive comments) changes which hook types rank highest, which tracks with how platforms weight those signals differently.

Anyone else structure prompts by separating output into categories rather than asking for "the best one"? Seems to consistently produce more usable results across creative tasks.