BC product-hunt-launch-playbook-win-1-daily
Win #1 Product of the Day on Product Hunt. Completely reverse-engineered from 30x daily winner and 3x weekly winner campaigns — ranking algorithm decoded, launch timing optimized, hunter selection guide, community engagement approach, and post-launch momentum playbook. Use this if you're: (1) already read PH launch guides but still can't break into Top 5 and need algorithm-level deep optimization, (2) targeting Daily #1 and 5,000+ sign-ups with a proven 30+ case validated approach, or (3) an investor/accelerator coaching portfolio companies on PH launches. What's inside: PH ranking algorithm decoded: weighted factors for first 3 hours, 24 hours, and weekly ranking · Pre-launch preparation: gallery optimization, product URL selection, tagline refinement (≤60 chars) · Launch timing optimization: exact PST launch windows + scheduling guide · Community engagement KPIs: upvote targets, comment response time, maker comment strategy · Platform compliance guide: understanding PH's quality signals and organic growth · Post-launch 72-hour plan: maintaining momentum, conditions for Weekly #1 · Case studies: 3 different categories (AI Tool, App, SaaS) dissected Expected outcomes: Product Hunt Daily #1 ranking · strong community engagement on launch day · 3,000–10,000+ sign-ups (depending on product-market fit) 🇨🇳 Product Hunt 打榜完整攻略 | 🇯🇵 Product Hunt ローンチガイド | 🇰🇷 Product Hunt 런칭 가이드 Keywords: Product Hunt #1, Product Hunt launch strategy, PH ranking algorithm, PH #1 daily winner, Product Hunt tips, product launch, startup launch, ranking optimization, PH gallery optimization, Community engagement strategy, maker community engagement, product strategy, launch tactics, how to win Product Hunt, product hunt best practices
As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Shorten the description to 1024 characters.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1748 chars, limit 1024 - note
description-budgetdescription takes 1748 of the ~15000-char shared budget for all skills - note
frontmatter-keyunknown frontmatter key "source"
Process rating: all ten parameters 57/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 65Failures and branches. 3 branches
- 100Tools and files. No external tools needed
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1059 tokens
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1747: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 12 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 52.