How do I install the Pipeline Check skill?

Run npx skillmds@latest add nanocoai/pipeline-check in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Pipeline Check safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Pipeline Check?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Pipeline Check free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Pipeline Check?

nanocoai (@nanocoai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Pipeline Check by nanocoai — Agent Skill

Name: Pipeline Check
Author: nanocoai

Pipeline Check

Check that the scheduled data work behind the reports actually ran and produced something sensible: the daily reshaping scripts, the API pulls, the tables the reports read from. Use each morning before anyone opens a report, when a report looks wrong and nobody knows whether it is the data or the query, after a schema or API change, when a new audience or source is added to the pipeline, or when a scheduled job failed or finished suspiciously fast.

nanocoai cb8cd9d 2.6 KB Updated Sep 17, 2026

File contents

Pipeline check

Goal

A broken or stale pipeline is found before anyone opens a report built on it, not after.

Procedure

Check it ran at all, and when it finished. A job that did not run leaves yesterday's data in place, which reads as a quiet day rather than as a failure. This is the single most valuable check here.
Check the output volume against normal. Row counts per audience and per table, against what that day of the week usually produces. A run that finished in a third of the usual time with a tenth of the rows succeeded technically and failed in every way that matters.
Check the freshness of what it depends on. An upstream API that returned an empty response produces a clean run over nothing.
Spot check the shape, not just the count. Nulls where there should be values, dates outside the expected window, an audience whose rows all landed on one site.
Trace anything odd to the change that caused it. A schema change, a new audience, a source field renamed. memory/conventions/pipelines.md records what each job reads and writes.
Say what is affected downstream: which reports and which audiences read the tables in question, so somebody knows who not to send a report to this morning.
Draft the fix or the rerun and hold it, unless a rerun is safe, idempotent and recorded as such.

Boundaries

Never rerun a job that is not recorded as safe to rerun. A partial second run is worse than a missing one.
Never patch data by hand in a production table to make a report look right.
Never report a job as healthy on the basis that it exited without an error.
Never let a report go out on data you know is stale without saying so.
Keep personal and confidential data out of any log or message written while investigating.

What to record

Keep memory/conventions/pipelines.md current: what each scheduled job reads and writes, when it runs, what a normal output volume looks like, whether it is safe to rerun, and which reports depend on it. Add each failure and its cause, since the same upstream API times out again.

---name: pipeline-checkdescription: Check that the scheduled data work behind the reports actually ran and produced something sensible: the daily reshaping scripts, the API pulls, the tables the reports read from. Use each morning before anyone opens a report, when a report looks wrong and nobody knows whether it is the data or the query, after a schema or API change, when a new audience or source is added to the pipeline, or when a scheduled job failed or finished suspiciously fast.---# Pipeline check## GoalA broken or stale pipeline is found before anyone opens a report built on it, not after.## Procedure1. **Check it ran at all, and when it finished.** A job that did not run leaves yesterday's data in place, which reads as a quiet day rather than as a failure. This is the single most valuable check here.2. **Check the output volume against normal.** Row counts per audience and per table, against what that day of the week usually produces. A run that finished in a third of the usual time with a tenth of the rows succeeded technically and failed in every way that matters.3. **Check the freshness of what it depends on.** An upstream API that returned an empty response produces a clean run over nothing.4. **Spot check the shape, not just the count.** Nulls where there should be values, dates outside the expected window, an audience whose rows all landed on one site.5. **Trace anything odd to the change that caused it.** A schema change, a new audience, a source field renamed. `memory/conventions/pipelines.md` records what each job reads and writes.6. **Say what is affected downstream:** which reports and which audiences read the tables in question, so somebody knows who not to send a report to this morning.7. **Draft the fix or the rerun and hold it,** unless a rerun is safe, idempotent and recorded as such.## Boundaries- Never rerun a job that is not recorded as safe to rerun. A partial second run is worse than a missing one.- Never patch data by hand in a production table to make a report look right.- Never report a job as healthy on the basis that it exited without an error.- Never let a report go out on data you know is stale without saying so.- Keep personal and confidential data out of any log or message written while investigating.## What to recordKeep `memory/conventions/pipelines.md` current: what each scheduled job reads and writes, when itruns, what a normal output volume looks like, whether it is safe to rerun, and which reportsdepend on it. Add each failure and its cause, since the same upstream API times out again.

Frequently asked questions