name

devtool-promoter

description

Data-driven startup launch advisor backed by scraped data from 50k+ entries across BetaList, DevHunt, DailyPings, ListYourTool, and Hacker News. Recommends the right platforms, optimal posting times, title/tagline patterns, and realistic expectations based on what actually worked. Use this whenever someone asks about launching a product, gaining early traction, where to post their startup, how to write a title or tagline, which platforms to target, marketing a side project, getting first users, or whether to post on Product Hunt / HN / DevHunt. Also handles /recalibrate to refresh the underlying platform data.

Startup Platform Marketing Advisor

SKILL_DIR: ~/.claude/skills/devtool-promoter

Platforms covered

BetaList (betalist.com) — 31k startups, 2013–2025, pre-launch waitlists
DevHunt (devhunt.org) — 4.2k dev tool launches, 2024–2025
DailyPings (dailypings.com) — 3k maker shipping updates, 2024–2025
Hacker News (news.ycombinator.com) — 200-day rolling window of stories + comments
ListYourTool (listyourtool.com) — 8.2k AI tools, 2024–2025

Step 1 — Read reference data

Before generating any recommendations, read all five platform reference files. They contain the key stats, timing data, what works, and realistic expectations:

reference/betalist.md
reference/devhunt.md
reference/dailypings.md
reference/hn.md
reference/listyourtool.md

If resources/{platform}/charts/ exists (populated after /recalibrate), prioritize those over the static reference summaries. The most useful charts per platform:

DevHunt: 04_magic_words.png, 06_tagline_length_vs_impressions.png, 07_title_length_vs_impressions.png
HN: 02_best_hour.png, 04_magic_words.png, 10_best_day_of_week.png
DailyPings: 03_best_day.png, 09_best_hour.png, 12_title_length_vs_upvotes.png
ListYourTool: 03_saturated_categories.png, 06_upvotes_by_category.png, 16_ai_in_title.png
BetaList: 16_survival_by_category.png, 18_common_tagline_words.png

Step 2 — Project intake

Understanding the product before recommending platforms is what separates a useful answer from a generic one — product type, audience, and stage all change which platforms are worth the effort.

Ask these questions in a single conversational message before giving any recommendations:

What does your product do? (1–2 sentences)
Who is it for? (developers / founders / general consumers / enterprise)
What type? (dev tool / AI tool / SaaS / open source / consumer app / other)
What stage? (idea/pre-launch / beta / live)
Main goal right now? (traffic / waitlist signups / community feedback / upvotes or press / ongoing visibility)

Wait for their answers before proceeding.

Step 3 — Recommendations

Cross-reference intake answers against the reference data to produce platform-specific guidance.

Platform selection

Product type	Primary	Secondary
Dev tool / API / CLI	DevHunt, HN Show HN	DailyPings
AI tool	ListYourTool, BetaList	DevHunt (if dev-facing)
Consumer app / SaaS	BetaList, DailyPings	ListYourTool (if AI-powered)
Open source	HN Show HN, DevHunt	DailyPings
Pre-launch / waitlist	BetaList	DailyPings
Build-in-public	DailyPings	HN

Adjust for goal: if the goal is community feedback, prioritize HN and DailyPings over directories. If waitlist signups, BetaList first. If ongoing visibility, DailyPings consistency beats one-off launches.

For each recommended platform, cover:

Why it fits — product type, audience, and stage match
Timing — specific best day and hour from the reference/chart data
Title and tagline — length, keywords, and patterns that empirically perform well
Realistic expectations — median, top 10%, and exceptional outcomes
Saturation warning — flag overcrowded categories, especially on ListYourTool

Launch sequence

Always close with a suggested order and rough timing. A common pattern that works well:

Week 1: Biggest-ceiling platform first (usually HN Show HN or DevHunt) — maximum early momentum
Week 2–3: Directories (BetaList, ListYourTool) — capture long-tail discovery
Ongoing: DailyPings for build-in-public momentum between major launches

Tailor based on their goal. If they're pre-launch, BetaList should come before a live launch elsewhere.

/recalibrate

Triggered by: /recalibrate, "recalibrate", "refresh the data", "update platform data", or similar.

Check last update first

cat ~/.claude/skills/devtool-promoter/last-update.txt 2>/dev/null

Compare the stored date to today. If it was less than 30 days ago, tell the user:

Last calibration was {N} days ago (on {DATE}). Platform data doesn't shift dramatically week-to-week — a full scrape takes 15–30 minutes and mostly produces the same results. Run anyway?

Stop if they say no. Proceed if they say yes or if the file doesn't exist or is ≥ 30 days old.

Run the recalibration

SKILL=~/.claude/skills/devtool-promoter

# Create output dirs
mkdir -p $SKILL/resources/{betalist,devhunt,dailypings,hn,listyourtool}/charts

# Install dependencies
pip install --quiet duckdb pandas plotly kaleido requests beautifulsoup4 2>/dev/null || true

# Scrapers — each runs in its own subshell so a failure doesn't stop the rest
(cd $SKILL/resources/betalist     && python $SKILL/scripts/scrape_betalist.py)
(cd $SKILL/resources/devhunt      && python $SKILL/scripts/scrape_devhunt.py)
(cd $SKILL/resources/dailypings   && python $SKILL/scripts/scrape_dailypings.py)
(cd $SKILL/resources/hn           && python $SKILL/scripts/scrape_hn_200_days.py)
(cd $SKILL/resources/listyourtool && python $SKILL/scripts/scrape_listyourtool.py)

# Analyzers
(cd $SKILL/resources/betalist     && python $SKILL/scripts/analyze_betalist.py)
(cd $SKILL/resources/devhunt      && python $SKILL/scripts/analyze_devhunt.py)
(cd $SKILL/resources/dailypings   && python $SKILL/scripts/analyze_dailypings.py)
(cd $SKILL/resources/hn           && python $SKILL/scripts/analyze_and_make_charts.py)
(cd $SKILL/resources/listyourtool && python $SKILL/scripts/analyze_listyourtool.py)

# Record completion
date +%Y-%m-%d > $SKILL/last-update.txt

Confirm when done:

Recalibration complete. Data refreshed across 5 platforms. Next recommended recalibration: {DATE + 30 days}.