How do I install the Dstack Pipeline Health skill?

Run npx skillmds@latest add upsolve-labs/dstack-pipeline-health 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 Dstack Pipeline Health 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 Dstack Pipeline Health?

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 Dstack Pipeline Health free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Dstack Pipeline Health?

Upsolve-Labs (@upsolve-labs) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Dstack Pipeline Health by upsolve-labs

Name: Dstack Pipeline Health
Author: Upsolve-Labs

Dstack Pipeline Health

Data pipeline health check. Checks data freshness, row count trends, and schema anomalies across tables. Outputs a health dashboard with OK/WARN/FAIL per table. Use for daily pipeline monitoring, SLA checks, or debugging stale data. Trigger phrases: "check pipeline health", "is my data fresh?", "data SLA", "pipeline monitoring", "are my tables up to date?", "something seems stale".

Upsolve-Labs 3e53e7a 3.2 KB Updated Sep 17, 2026

File contents

Update Check (run first)

_UPD=$(~/.claude/skills/data-stack/bin/data-stack-update-check 2>/dev/null || .claude/skills/data-stack/bin/data-stack-update-check 2>/dev/null || true)
[ -n "$_UPD" ] && echo "$_UPD" || true

If output shows UPGRADE_AVAILABLE <old> <new>: read ~/.claude/skills/data-stack/skills/dstack-upgrade/SKILL.md and follow the "Inline upgrade flow". If JUST_UPGRADED <from> <to>: tell user "Running data-stack v{to} (just updated!)" and continue.

/pipeline-health

You are running a health check across data pipelines or tables.

Before Starting

If the user has specified tables, use those. Otherwise discover them first:

analyze_data("List all available tables and their most recent record timestamp.")

Phase 1: Freshness Check

analyze_data("For these tables: <tables>. Show: (1) most recent record timestamp, (2) how many hours ago that was, (3) row count for today vs 7 days ago.", thread_id=<id>)

Freshness thresholds (infer expected cadence from 7-day history if not specified):

OK — updated within expected window
WARN — 1.5× past expected cadence
FAIL — 2× past expected cadence, or no data today

Phase 2: Row Count Anomalies

analyze_data("For each table, compare today's row count to the 7-day average. Flag any table where today's count is less than 50% or more than 200% of the average.", thread_id=<id>)

Phase 3: Schema Drift

analyze_data("Are there any columns with unusual null rates today compared to last week? Any columns that appear to have stopped receiving data?", thread_id=<id>)

Phase 4: Output Health Dashboard

PIPELINE HEALTH REPORT
As of: <timestamp>
──────────────────────────────────────────────────────────────────
Table              Freshness       Row Δ (7d avg)   Schema    Status
──────────────────────────────────────────────────────────────────
<table_1>          <X>h ago        +3%              OK        ✓ OK
<table_2>          <X>h ago        -62%             —         ⚠ WARN
<table_3>          <X>h ago        no data today    DRIFT     ✗ FAIL

ISSUES:
  FAIL  <table>: <specific issue>
  WARN  <table>: <specific issue>

RECOMMENDED ACTIONS:
  1. <most critical action>
  2. <second action>

Rules

Run all three phases even if Phase 1 already shows failures.
If no expected cadence is known, infer it from the last 7 days of update patterns.
Do not speculate on causes — report observations, recommend investigation.
If a table has no timestamp column, note it as "freshness unverifiable".

---name: dstack-pipeline-healthdescription: Data pipeline health check. Checks data freshness, row count trends, and schema anomalies across tables. Outputs a health dashboard with OK/WARN/FAIL per table. Use for daily pipeline monitoring, SLA checks, or debugging stale data. Trigger phrases: "check pipeline health", "is my data fresh?", "data SLA", "pipeline monitoring", "are my tables up to date?", "something seems stale".license: MIT---## Update Check (run first)```bash_UPD=$(~/.claude/skills/data-stack/bin/data-stack-update-check 2>/dev/null || .claude/skills/data-stack/bin/data-stack-update-check 2>/dev/null || true)[ -n "$_UPD" ] && echo "$_UPD" || true```If output shows `UPGRADE_AVAILABLE <old> <new>`: read `~/.claude/skills/data-stack/skills/dstack-upgrade/SKILL.md` and follow the "Inline upgrade flow". If `JUST_UPGRADED <from> <to>`: tell user "Running data-stack v{to} (just updated!)" and continue.# /pipeline-healthYou are running a health check across data pipelines or tables.## Before StartingIf the user has specified tables, use those. Otherwise discover them first:```analyze_data("List all available tables and their most recent record timestamp.")```## Phase 1: Freshness Check```analyze_data("For these tables: <tables>. Show: (1) most recent record timestamp, (2) how many hours ago that was, (3) row count for today vs 7 days ago.", thread_id=<id>)```Freshness thresholds (infer expected cadence from 7-day history if not specified):- **OK** — updated within expected window- **WARN** — 1.5× past expected cadence- **FAIL** — 2× past expected cadence, or no data today## Phase 2: Row Count Anomalies```analyze_data("For each table, compare today's row count to the 7-day average. Flag any table where today's count is less than 50% or more than 200% of the average.", thread_id=<id>)```## Phase 3: Schema Drift```analyze_data("Are there any columns with unusual null rates today compared to last week? Any columns that appear to have stopped receiving data?", thread_id=<id>)```## Phase 4: Output Health Dashboard```PIPELINE HEALTH REPORTAs of: <timestamp>──────────────────────────────────────────────────────────────────Table Freshness Row Δ (7d avg) Schema Status──────────────────────────────────────────────────────────────────<table_1> <X>h ago +3% OK ✓ OK<table_2> <X>h ago -62% — ⚠ WARN<table_3> <X>h ago no data today DRIFT ✗ FAILISSUES: FAIL <table>: <specific issue> WARN <table>: <specific issue>RECOMMENDED ACTIONS: 1. <most critical action> 2. <second action>```## Rules- Run all three phases even if Phase 1 already shows failures.- If no expected cadence is known, infer it from the last 7 days of update patterns.- Do not speculate on causes — report observations, recommend investigation.- If a table has no timestamp column, note it as "freshness unverifiable".

Frequently asked questions