agentic/code/addons/nlp-prod/skills/pipeline-status/SKILL.md
Show status overview of all LLM inference pipelines in the current project
npx skillsauth add jmagly/aiwg pipeline-statusInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
You are the Pipeline Status Reporter — scanning the current project for nlp-prod pipelines and reporting their health at a glance.
Output as JSON instead of formatted table.
Glob for **/pipeline.config.yaml in the current directory (excluding node_modules, .git, prod/).
For each pipeline.config.yaml:
name — pipeline namepattern — pipeline patternlanguage — target languageFor each pipeline, also check:
eval/results.jsonl — most recent run date and pass rateprod/ — whether production artifacts existcost-model.yaml — monthly cost at configured volume| Check | Points |
|-------|--------|
| pipeline.config.yaml valid | 10 |
| Prompt files exist | 10 |
| Evaluator prompt exists and separate | 20 |
| eval/cases.jsonl with ≥5 cases | 15 |
| Most recent eval pass rate ≥85% | 25 |
| Eval run within last 7 days | 10 |
| prod/ artifacts exist | 10 |
Score 90+ = Production Ready, 70-89 = Near Ready, <70 = Needs Work
Pipeline Status — <project> (<date>)
┌─────────────────────┬────────────────┬──────────┬──────────────┬────────┬──────────────────┐
│ Pipeline │ Pattern │ Lang │ Eval Pass │ Prod? │ Health │
├─────────────────────┼────────────────┼──────────┼──────────────┼────────┼──────────────────┤
│ product-extractor │ simple-chain │ Python │ 91% (today) │ ✓ │ Production Ready │
│ doc-classifier │ simple-chain │ Python │ 78% (3d ago) │ ✗ │ Near Ready │
│ qa-rag │ rag-pipeline │ TypeScript│ — │ ✗ │ Needs Work │
└─────────────────────┴────────────────┴──────────┴──────────────┴────────┴──────────────────┘
Actions recommended:
doc-classifier: Pass rate 78% < 85% threshold — run aiwg nlp eval pipelines/doc-classifier/
qa-rag: No eval run found — run aiwg nlp eval pipelines/qa-rag/
data-ai
Report which research-corpus radar sidecars are overdue for refresh. Computes staleness (days since last refresh vs the cadence window) for every radar, sorted most-overdue-first. Runs via `aiwg corpus radar-status`.
data-ai
Aggregate research-corpus radar sidecars into a corpus or per-cluster freshness report — totals, overdue count, per-cluster / per-GRADE / per-trajectory breakdowns, an overdue table, and per-radar rationale snippets. Runs via `aiwg corpus radar-report`.
testing
Scaffold radar/freshness sidecars for research-corpus REFs. Pulls title/authors from the citation sidecar and GRADE from the analysis doc, defaults the refresh cadence from GRADE and the cluster from a corpus-local map, and stamps documentation/radar/REF-XXX-radar.md. Runs via `aiwg corpus radar-init`.
data-ai
Compute an entity's publication trajectory — per-year paper counts, topic drift, hot-streak detection (≥3 consecutive A-grade years), and career phase. Runs via `aiwg corpus profile-temporal`.