ai-products
by MarsWang42ai-products helps agents curate daily AI product launches from Product Hunt, Hacker News, GitHub Trending, and Techmeme into a ranked digest for Trend Monitoring.
This skill scores 72/100, which means it is listable and likely useful for agents that need a repeatable AI product-launch digest, but directory users should expect some manual judgment because the repository provides a text workflow and template rather than executable helpers or detailed edge-case rules.
- Clear triggerability: the frontmatter explicitly says to use it for `/ai-products` and `/start-my-day` product launches.
- Operationally concrete core workflow: it specifies sources, cache path, filtering, deduplication, ranking factors, and digest sections.
- Good install-decision signal: TEMPLATE.md shows the expected output structure, metadata, and final deliverable format.
- No support files, scripts, or reference data are included, so execution depends on the agent correctly interpreting the written process and external sites.
- Filtering and ranking rules stay fairly heuristic (for example keyword-based AI relevance and normalized engagement), with limited guidance for ambiguous products or source failures.
Overview of ai-products skill
What the ai-products skill does
The ai-products skill helps an agent build a daily AI product-launch digest from Product Hunt, Hacker News, GitHub Trending, and Techmeme. Its real job is not just “find AI tools,” but fetch, filter, deduplicate, rank, and format launches into a usable trend brief for content planning or market scanning.
Who should install ai-products
Best fit: founders, analysts, newsletter writers, operators, and creators who need fast AI product discovery without manually checking four sources every day. The ai-products skill is especially useful for Trend Monitoring when you want a repeatable shortlist with source attribution, engagement signals, and content angles.
Why this is better than a generic prompt
A normal prompt can ask for “today’s AI launches,” but this skill already defines the source set, AI relevance filter, deduping logic, ranking factors, cache behavior, and output structure. That reduces agent guesswork and makes recurring digests more consistent than ad hoc browsing.
Key limits to know before installing
This skill is lightweight: it mainly provides workflow instructions in SKILL.md plus a digest format in TEMPLATE.md. It does not ship helper scripts, validation rules, or source-specific parsers, so output quality depends on your agent’s web access and how well you specify timeframe, audience, and desired categories.
How to Use ai-products skill
Install context and first files to read
If your environment supports skills, install from the OrbitOS repo and then read EN/.agents/skills/ai-products/SKILL.md first, followed by TEMPLATE.md. Those two files contain almost all of the practical value: source list, ranking logic, cache path convention, and final digest structure.
What input the ai-products skill needs
For strong ai-products usage, give the agent:
- a date or time window, such as “last 24 hours”
- your goal, such as trend monitoring, newsletter curation, or content ideation
- preferred categories, such as developer tools or automation
- output depth, such as “top 5 only” or “full digest”
- whether to favor open source, enterprise products, or consumer launches
A weak request is: Run ai-products.
A stronger request is: Use ai-products for Trend Monitoring. Curate AI launches from the last 24 hours, prioritize developer tools and open-source repos, deduplicate across sources, and give me 5 top picks with one content angle each.
Practical ai-products workflow
A good workflow is:
- Check whether a cached digest already exists for the target date using the path pattern in
SKILL.md. - Fetch Product Hunt, Hacker News Show HN, GitHub Trending RSS, and Techmeme.
- Extract product name, link, short description, and visible engagement metrics.
- Filter to clearly AI-related launches only.
- Merge duplicates across sources.
- Rank by relevance, engagement, novelty, and content potential.
- Format the result using
TEMPLATE.md.
This matters because the skill is opinionated about process. If you skip deduping or ranking normalization, the digest becomes a list of links instead of a decision-ready brief.
Prompting tips that improve output quality
Ask the agent to show its selection criteria in plain language, especially when borderline products appear. Also tell it how to treat noisy cases:
- exclude generic SaaS unless AI is core to the product
- separate models from apps
- surface “why this matters now,” not just descriptions
- keep all source names when duplicates are merged
If you want better downstream use, ask for Top Picks, Open Source Highlights, and Content Creation Opportunities, because those map directly to the provided template and make the ai-products skill more actionable.
ai-products skill FAQ
Is ai-products mainly for research or for publishing?
Both, but it is strongest when you need a publishable daily digest. The template pushes the agent toward clean sections, source attribution, metrics, and editorial angles, so it fits newsletters, internal briefings, and creator planning better than raw market research notes.
When should I not use ai-products?
Do not use ai-products if you need deep due diligence on a single company, historical trend analysis over months, or highly reliable coverage of every launch. The skill is optimized for fast daily discovery, not exhaustive databases or analyst-grade verification.
Is ai-products beginner-friendly?
Yes, if your agent can browse the web. The skill is simple to understand because it only has two key files and a clear workflow. The main beginner risk is under-specifying the request, which leads to mixed categories or shallow summaries.
How does ai-products compare with a normal web-search prompt?
The ai-products skill gives you a reusable operating procedure: fixed sources, AI filtering, duplicate merging, ranking logic, and a digest template. A normal prompt may find some of the same launches, but it usually misses consistency, cache awareness, and cross-source consolidation.
How to Improve ai-products skill
Give the ai-products skill sharper editorial constraints
The biggest output upgrade comes from telling the agent what “interesting” means for you. Examples:
Prioritize products with clear launch momentum, not funding news.Bias toward tools that a technical audience could trial this week.Only include items with a distinct product release, not vague AI feature updates.
This reduces filler and makes rankings more aligned with your use case.
Fix common failure modes early
Typical problems are false-positive “AI” matches, duplicate products listed twice, and over-weighting one source. Ask the agent to:
- justify AI relevance for each top pick
- merge products by canonical name and URL
- normalize engagement before ranking
- note uncertain or thinly described launches instead of overstating them
Provide stronger inputs for better ai-products usage
Better inputs produce better curation. Include:
- audience:
indie hackers,enterprise buyers,ML engineers - region or language preference if relevant
- category quotas:
2 dev tools, 2 workflow tools, 1 model - output intent:
newsletter,investment scan,social thread - exclusion rules:
skip image generators and generic wrappers
These constraints help the ai-products skill produce a digest you can actually use without major rewriting.
Iterate after the first draft
After the first run, ask for one focused revision instead of a total redo. Good follow-ups include:
Re-rank for novelty over popularity.Expand only the open-source section.Cut anything without a real launch signal.Add one-sentence comparisons for the top 3 products.
That revision pattern works well because the ai-products skill already has a stable structure; you usually need better prioritization, not a different format.