98% of marketing AI skills activate automatically. 93% of coding skills wait for a direct command. One researcher classified 5,399 prompts across 34 open-source repositories and found that two of the most popular AI use cases arrived at completely opposite activation architectures for the exact same problem: when should an AI start?
The answer depends entirely on which domain you’re building for. And most builders never ask the question at all.
The Activation Split
Marketing skills are “Triggered.” Their activation language describes situations: “Use when the user mentions ‘cold email,’ ‘cold outreach,’ ‘prospecting emails’… Also use when they share an email draft that sounds too sales-y.” The skill fires when conditions match, not when someone types a command.
Coding skills are “Invoked.” Their activation language names commands: /gsd:set-profile, /gsd:execute-phase, /gsd:pause-work. You call it, it runs.
Constraint profiles across both groups are nearly identical. Entry-point design diverges completely. Marketing automation figured this out years ago. Cart abandonment emails don’t wait for someone to type /send-cart-email. They fire when conditions match. The prompt engineering community arrived at the same design independently.
How Production Prompts Handle Constraints
Across all 5,399 prompts, the breakdown looks like this:
- 71.8% Bounded (hard rules with room for judgment)
- 19.9% Guided (recommendations, not requirements)
- 7.1% Open (four fields, nothing more)
- 1.2% Scripted (one correct action, no judgment)
Practitioners overwhelmingly land in the middle. Not total freedom, not total rigidity. Bounded constraints give clear prohibitions and clear permissions, with room to reason between them. Both extremes are rare in production systems.
The Scripted extreme sounds like: “Never force-push. Merge is always --no-ff.” One correct action. No judgment. The Open extreme is four fields and nothing more. The agent fills every blank. Neither shows up much in real repos.
Two Authors Built the Same Architecture Without Talking
40 of 44 marketing skills in one collection check for a shared product-marketing-context.md file before doing anything. The copywriting skill reads it before asking questions. The humanizer calls it “your voice blueprint. Use it, don’t improvise a voice when the brief already defines one.” The psychology skill says: “Psychology works better when you know the audience.”
Corey Haines’ marketingskills (6,852 GitHub stars, 25 skills) independently converged on the exact same structure. Foundation-file check before acting. A dependency graph rooted in that context file. Skills that route to each other with conditions. Two authors with no apparent coordination. Same answer.
When two teams independently build the same architecture, that’s usually a signal you’re looking at a real pattern, not a personal preference.
3 Ways to Apply This to Your Own AI Builds
- Match activation to workflow reality. If your users naturally describe situations rather than remember commands, use Triggered activation. If they’re developers running structured workflows, Invoked is cleaner. Don’t default to one because it’s familiar. Ask how the user actually starts the task.
- Build a foundation file. Before your AI skill does anything, have it check a shared context file. Brand voice, product details, audience definition, constraints. Two independent teams built this pattern because separating “what do I know about this user’s world” from “what am I doing right now” solves a real problem at scale.
- Let skills route to each other. The best systems in this corpus aren’t collections of prompts. They’re routing matrices. The Page CRO skill references seven others by name with conditions. The Marketing Ops skill is a disambiguation matrix for 34 skills with explicit rules like: “‘Write a blog post’ goes to content-strategy, NOT copywriting (that’s for page copy).” Build skills that know their edges and hand off cleanly.
Tips and Pitfalls
Do: Use Bounded constraints as your default. Hard rules plus room to reason is what practitioners chose at 71.8%. It’s the production sweet spot for most real tasks.
Do: Ship a severity-rated failure checklist, not just style guidelines. The Content Humanizer in this corpus lists AI-detectable patterns with severity ratings and a threshold rule: “If the piece has 10+ AI tells per 500 words, a patch job won’t work. Flag for a full rewrite, not an edit.” That’s a decision system. Not a vibe.
Don’t: Assume all AI identity design works the same way. ChatGPT mirrors user tone. Claude opens in third person with no personality directives. Perplexity fits its entire identity into 340 characters. v0 by Vercel uses 60,037 characters where the identity is the capability surface. Four companies, four completely different answers to the same question.
Don’t: Skip the routing logic. If your skills can’t refer users to other skills with conditions, you’re building a pile of prompts, not a system. The difference shows up fast in real use.
Where to Go From Here
The full corpus is open and browsable at the link in the original post. Good starting points: Bounded constraints (3,875 prompts), Triggered skills (772 prompts), and the commercial system prompts if you want to see how the biggest AI products solve identity differently.
If you build AI workflows, this is the kind of data that changes what you build next. Not theory. Patterns pulled from what’s actually working in production repos right now.
Analysis of 5,399 prompts from 34 repos: marketing skills are 98% Triggered, coding skills are 93% Invoked. Same constraints, completely different activation architecture
by u/Obvious-Grape9012 in PromptEngineering