Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jlifyio/wyx/pipelinenpx skills add jlifyio/wyx --skill pipelinegit clone --depth 1 https://github.com/jlifyio/wyxWrote 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/jlifyio/wyx/pipeline)<a href="https://agentmods.dev/skills/jlifyio/wyx/pipeline"><img src="https://agentmods.dev/badge/skills/jlifyio/wyx/pipeline.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00066 | $0.01394 |
| Opus 5 | $0.00033 | $0.00697 |
| Sonnet 5 | $0.00013 | $0.00279 |
| Haiku 4.5 | $0.00007 | $0.00139 |
Grade A, and why
pipeline 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 5d 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Workflow Specification
Generate a data pipeline specification — a structured description of how data flows through transformations, what quality invariants must hold, and which concepts own the source and output data.
How to interpret $ARGUMENTS
Determine the mode from the argument:
- Path to directory/file (e.g.
src/lib/server/syncs/): Retrofit mode — read the existing data transformation code, identify the pipeline stages, and propose a PIPELINE.md spec. Flag any quality invariants that are assumed but not checked. - Pipeline description (e.g.
Sentiment scoring with recency weighting): Greenfield mode — design a data pipeline spec from the description. Define sources, stages, outputs, and invariants. - No arguments: Discovery mode — analyze the project for data workflows (queries, aggregations, sync chains, batch operations) and list candidates for PIPELINE.md specs. Do NOT generate full specs; ask the user which to elaborate. If discovery surfaces cross-concept coordination patterns (event handlers, scheduled jobs) rather than data transformations, note that
/wyx:synccovers those.
PIPELINE.md Format
Write the spec as a PIPELINE.md file placed next to the data transformation code (one per directory).
# pipeline: [Name]
## purpose
[Single sentence: what data this pipeline produces and for whom]
## sources
- [name]: [table/file/API] → [key fields] ([row estimate or "unbounded"])
## stages
### [stage-name] [tool: <tool-name>]
in: [source or previous stage output]
out: [what this stage produces]
[1-3 lines: transformation logic]
quality: [invariant for this stage]
### [stage-name] [tool]
in: [...]
out: [...]
[...]
quality: [invariant]
## outputs
- [name]: [table/format] → [key fields] ([row estimate])
## invariants
- [data quality rules that must always hold across the full pipeline]
- [e.g. "output rows <= input rows" or "scores in [-1.0, 1.0]"]
- [e.g. "no null values in amount_tax_excluded after stage 2"]
## triggers
- [what causes this pipeline to run: sync event, API call, schedule, manual]
## data boundary
- [which concept owns the source data — read through its service, not direct SQL]
- [which concept owns the output data — write through its service, not direct INSERT]
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.
- 5d ago First seen · 132 lines · 66 tokens per session scan A 8f11fe267c42
pipeline is a skill published in the GitHub repository jlifyio/wyx (3 stars, last pushed 27d ago), licensed MIT. It adds 66 tokens to every session and 1,394 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
prompt-tuning
Tune a prompt, or anything whose quality is measured by non-deterministic model output, without chasing noise - a noise baseline before the first edit, medians over repeated runs, enforcement AFTER generation rather than in the wording. Use when iterating on prompts or model-judged output.
mutation-test
Mutation testing with two engines. Uses the project's NATIVE mutation runner (StrykerJS / Infection / mutmut / PIT / cargo-mutants) when one is configured — installing it on explicit consent when it is not — for a reproducible, comparable score; and an LLM-guided engine for the mutation classes native mutators cannot…
json-to-toon
Transforms JSON input into Token-Oriented Object Notation (TOON) to reduce token consumption in LLM prompts and context windows. Applies the full TOON spec: inline primitive arrays, tabular format for uniform object arrays, and list format for heterogeneous or nested structures. Invoked when the user asks to compress…
deep-plan
Creates detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.
repo-refresh
全库更新扫描器。在用户要求"全库更新"、"扫一遍仓库"、"检查文档是否过时"、"repo refresh"、"stale scan"、"audit the repo"、"哪些文档跟代码对不上"、"清理冗余"、"找漂移" 等语境下自动唤起。对整个仓库（md 文档 + 代码一视同仁）做系统式扫描，找出并修复"陈旧、过时、冗余、错误、漂移"五类内容；每条 finding 必须带 file:line 证据。它是 rules/12-repo-wide-sync.md 主动半区的执行入口。.
optimization-audit
Comprehensive optimization audit with two modes and a single tier. Planning mode designs performance strategy, capacity planning, and scaling architecture. Audit mode scans code and infrastructure for performance anti-patterns, inefficient algorithms, N+1 queries, missing caching, concurrency issues, and resource…