Borrowing it
Nothing to install: this file belongs to yoanbernabeu/producthunt-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/yoanbernabeu/producthunt-skills/main/AGENTS.mdgit clone --depth 1 https://github.com/yoanbernabeu/producthunt-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/yoanbernabeu/producthunt-skills/agents-md)<a href="https://agentmods.dev/instructions/yoanbernabeu/producthunt-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/yoanbernabeu/producthunt-skills/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/yoanbernabeu/producthunt-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/yoanbernabeu/producthunt-skills/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00812 | $0.00812 |
| Opus 5 | $0.00406 | $0.00406 |
| Sonnet 5 | $0.00162 | $0.00162 |
| Haiku 4.5 | $0.00081 | $0.00081 |
Grade A, and why
producthunt-skills AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Hunt Launch Skills for AI Agents
Comprehensive guidance for launching products on Product Hunt - from strategy to post-launch optimization.
Overview
This skill collection provides expert guidance for every phase of a Product Hunt launch. AI agents can use these skills to help makers plan, execute, and optimize their launches.
Available Skills
Strategy & Planning
- ph-launch-strategy: Define objectives, create 4-6 week timeline, strategic decision-making
- ph-competitor-analysis: Research similar launches, benchmark top performers
- ph-timing-optimizer: Choose optimal launch day/time based on goals
Content Creation
- ph-tagline-writer: Craft compelling taglines under 60 characters
- ph-description-writer: Write converting product descriptions (AIDA, PAS, FAB formulas)
- ph-maker-comment: Create authentic first comments that build trust
- ph-thumbnail-creator: Design scroll-stopping thumbnails and GIFs
- ph-gallery-assets: Build compelling image galleries (4-6 images)
- ph-video-demo: Script and structure demo videos (30-60 seconds)
Marketing & Outreach
- ph-email-strategy: Plan email waves across timezones (5-6 waves)
- ph-social-media-launch: Coordinate Twitter/LinkedIn/Facebook campaigns
- ph-community-outreach: Engage Reddit, Indie Hackers, Hacker News
- ph-supporter-network: Build and activate supporter networks
Launch Day Execution
- ph-launch-day-checklist: Hour-by-hour execution plan
- ph-comment-responder: Respond effectively to comments (< 9 minutes)
- ph-real-time-monitor: Track metrics and make adjustments
Compliance & Risk Prevention
- ph-ban-prevention: Avoid shadow bans and penalties
- ph-algorithm-guide: Understand ranking factors
- ph-safe-messaging: Use compliant language patterns
Hunters & Profile
- ph-hunter-finder: Find and approach top hunters
- ph-profile-optimizer: Build maker reputation
Pricing & Offers
- ph-launch-offers: Create exclusive PH deals (discounts, LTDs)
- ph-pricing-psychology: Leverage urgency, scarcity, FOMO
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 88 lines · 812 tokens per session scan A 287c8e9d89db
producthunt-skills AGENTS.md is an instructions file published in the GitHub repository yoanbernabeu/producthunt-skills (18 stars, last pushed 7mo ago), licensed MIT. It adds 812 tokens to every session, about $0.0041 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
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AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
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vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.