workers-ai-llm-pipelines
Skill1 starsChanged 51 days agoNo licenceView on GitHub
Build LLM-powered backends on Cloudflare Workers AI (env.AI.run): response shapes per model family, model-role allocation and cost math, KV state scoping, async agent stages with waitUntil, and Analytics Engine metrics. Use this skill whenever the user is calling Workers AI / env.AI.run, choosing between @cf/ models (gpt-oss, llama, qwen), debugging an LLM call that "returns nothing" or empty results on Cloudflare, adding observability to a Worker, building agent pipelines or background research/processing stages in Workers, or pricing a Workers AI workload — even if they don't say "Workers AI" but mention Cloudflare + LLM/AI together.
In this repository8 other files it gives its agents.
- implementation-orchestrationSkill
- langsmith-prod-evalsSkill
- llm-pipeline-qualitySkill
- monorepo-navigationSkill
- monorepoSkill
- n8n-automation-builderSkill
- paywall-conversionSkill
- qwen-asr-realtimeSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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