Borrowing it
Nothing to install: this file belongs to RudyCity/superagent. 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/RudyCity/superagent/main/.agents/skills/recsys-pipeline-architect/SKILL.mdgit clone --depth 1 https://github.com/RudyCity/superagentWrote 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/skills/rudycity/superagent/recsys-pipeline-architect)<a href="https://agentmods.dev/skills/rudycity/superagent/recsys-pipeline-architect"><img src="https://agentmods.dev/badge/skills/rudycity/superagent/recsys-pipeline-architect/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/skills/rudycity/superagent/recsys-pipeline-architect"><img src="https://agentmods.dev/badge/skills/rudycity/superagent/recsys-pipeline-architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 125 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00095 | $0.01849 |
| Opus 5 | $0.00048 | $0.00924 |
| Sonnet 5 | $0.00019 | $0.00370 |
| Haiku 4.5 | $0.00010 | $0.00185 |
Grade A, and why
recsys-pipeline-architect 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 8d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recsys Pipeline Architect
A spec-and-scaffold skill for building composable recommendation, ranking, and feed pipelines. Encodes the six-stage pattern popularized by xAI's open-sourced For You algorithm (Apache 2.0) and applies it to any "top K for (user, context)" problem.
Overview
Most "recommendation systems" in production aren't exotic ML — they're pipelines: fetch candidates from one or more sources, enrich them with metadata, drop the ineligible, score the rest, sort and pick the top K, then fire async side effects. The pattern is universal. The scoring function and the items change; the pipeline shape doesn't.
This skill is an independent reimplementation of the pattern (MIT) — no code copied from the original.
When to Use This Skill
- Building any system that returns "the top K items for a user/context"
- Designing or refactoring a personalized feed (content, search results, notifications)
- Wrapping an LLM/ML scorer in proper pipeline plumbing (sources, hydration, filters, side effects)
- Adding multi-action prediction with tunable weights (instead of a single relevance score)
- Building a RAG retrieval reranker (cheap retrieval → expensive rerank)
- Designing a task prioritizer or alert triage system
The Six-Stage Framework
| # | Stage | Job | Parallel? |
|---|---|---|---|
| 1 | Source | Fetch candidates from one or more origins | Yes — multiple sources run in parallel |
| 2 | Hydrator | Enrich candidates with metadata needed for filtering and scoring | Yes — independent hydrators run in parallel |
| 3 | Filter | Drop ineligible candidates (blocked, expired, duplicate, ineligible) | Sequential — each filter sees fewer items |
| 4 | Scorer | Assign each surviving candidate one or more scores | Sequential — later scorers see earlier scores |
| 5 | Selector | Sort by final score, return top K | Single op |
| 6 | SideEffect | Cache, log, emit events, update served-history | Async — must never block the response |
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.
- 8d ago First seen · 126 lines · 95 tokens per session scan A 74c056a02a06
recsys-pipeline-architect is a skill published in the GitHub repository RudyCity/superagent (21 stars, last pushed today), licensed MIT. It adds 95 tokens to every session and 1,849 once invoked, about $0.0005 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 2026-09-03.
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