r/sales_intelligence

Curated this list of 20 Claude Skills for devs to get help with marketing, sales, launch:

Content

• human-tone: scans your copy against 18 GTM slop patterns and rewrites it. basically a linter for marketing language
• cook-the-blog: researches a company, extracts SEO keywords, writes a case study in MDX, generates a cover image, pushes to GitHub. one command
• noise-to-linkedin-carousel: paste rough notes or a voice transcript, get a carousel with hook and CTA. good for people who think faster than they write
• tweet-thread-from-blog: turns any blog post into a 7-10 tweet thread. optionally posts to X via Composio
• linkedin-post-generator: reads a GitHub PR or article, produces a post with the right hook and story arc

Sales

• discovery: run a proper needs assessment before you pitch anything. most DevRels skip this and go straight to the demo. biggest mistake.
• objection-handling: "we already have something for this" and "our engineers will build it" are the two you'll hear constantly in developer sales. this is the one to internalize.
• storytelling: case studies and narratives move technical buyers more than feature lists. if you can make someone see themselves in a story, the sale is mostly done.
• qualifying-leads: not every inbound is worth chasing. knowing who to drop early saves more time than any outreach optimization.
• closing: DevRels are usually great at building trust and terrible at asking for the next step. this one bridges that gap.

Intelligence

• gh-issue-to-demand-signal: give it a competitor's public GitHub repo. clusters open issues into demand categories, scores by engagement, outputs a GTM messaging brief. surprisingly useful for competitive research
• where-your-customer-lives: give it your ICP, it searches Reddit/HN/DuckDuckGo to find the actual communities your customers are in. per-channel entry tactics
• hackernews-intel: monitors HN for your keywords, Slack alert on match, no duplicates. runs on cron or GitHub Actions
• map-your-market: searches Reddit, HN, GitHub Issues, G2 for pain signals. outputs ICP definition and messaging angles
• competitor-pr-finder: finds where your competitors got covered, which journalist wrote it, and the angle that got them in. gives you a ready-to-send cold pitch

Launch + Outreach

• show-hn-writer: drafts a Show HN post based on patterns from 250+ real HN submissions. generates 3 title variants, runs a review pass to catch anti-patterns before you post
• producthunt-launch-kit: taglines, listing copy, maker comment, tweet thread, LinkedIn post, 4-email sequence. all from one product description
• outreach-sequence-builder: buying signal in, 4-6 touchpoint sequence out across email, LinkedIn, phone
• cold-email-verifier: guesses, enriches, and verifies emails from a CSV autonomously
• npm-downloads-to-leads: give it npm package names, it pulls 12 weeks of download data, maps maintainers to GitHub/Twitter, outputs who to reach out to and what to say

Link in comments 👇

I pushed back on this for six months before running the test. The assumption was that first touch is where human presence matters most because trust is at zero and a bad impression ends the relationship before it starts. But my results didn't match that assumption.

An ai video call for sales first touch outperforms a live SDR in specific conditions and underperforms in others. The answer isn't binary and anyone telling you it is, is selling something.

Where the ai video call wins: inbound leads who submit a form outside of SDR working hours. Response time is the most important variable for inbound conversion and an ai that starts a video conversation within seconds of form submission captures intent that would otherwise go cold by morning. Also high-volume lower ACV segments where you can't economically justify a human rep on every lead.

Where a live SDR wins: named account outreach where specific research is the differentiator. An ai video call for sales can read the room during a conversation but it can't say "I saw your talk at the conference last month" in a way that lands. Also complex deals where the buyer wants to know there's a human champion on the account from the first interaction.

The Tavus video ai rep format for the inbound category produced meetings booked and engagement numbers consistent with what was needed to justify the investment, and the face-to-face format changed how prospects engaged compared to the text qualification bot that ran before it.