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Designs composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework
Passed 18 of 22 test cases with the skill, and 8 of 22 without it.
and 11 more in the full report
10 fixed8 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are building a personalized news article feed in Python using FastAPI. We currently fetch recent articles, but we need a clean, modular pipeline structure to pull candidate posts, hydrate user reading history, filter out muted publishers, score articles, select the top 10, and log impressions in the background. Could you guide us through designing this pipeline and provide a runnable code scaffold?
| Checked | Without skill | With skill |
|---|---|---|
| Mention pattern attribution as popularized by xAI's open-sourced For You algorithm. | Missed | Passed |
| Explicitly surface the multi-action prediction versus single-score model trade-off. | Missed | Passed |
| Implement impression logging as a non-blocking fire-and-forget background task. | Passed | Passed |
The instructions your model reads when the skill loads.
A spec-and-scaffold skill for building composable recommendation, ranking, and feed pipelines. It encodes the six-stage Source → Hydrator → Filter → Scorer → Selector → SideEffect framework popularized by xAI's open-sourced For You algorithm (Apache 2.0). This skill is an independent reimplementation of the pattern — no code is copied from the original — licensed MIT. Use it whenever you need "the top K items for a (user, context)": social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.
Ask the user three questions (only what is missing):
The full SKILL walks through: clarify use case → identify candidate sources → list required hydrations → list filters → design scorer chain → selector → side effects → generate scaffold. Each step surfaces the architectural trade-offs (multi-action vs single-score, candidate isolation vs joint scoring, online vs offline batch) so the user makes them explicitly rather than defaulting silently.
The upstream repository ships three runnable example scaffolds — every one green on its test suite:
GET /api/feed/for-you with multi-action scoring and author diversityGET /tasks/next?user_id=42&limit=10When the user's stack doesn't match, the skill generates from scratch following the interface definitions in references/interfaces.md (TypeScript, Go, Python, Rust).
test cases passed, vs 8 / 22 without
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