Aiwg pipeline-status

Show status overview of all LLM inference pipelines in the current project

install

source · Clone the upstream repo

git clone https://github.com/jmagly/aiwg

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/jmagly/aiwg "$T" && mkdir -p ~/.claude/skills && cp -r "$T/agentic/code/addons/nlp-prod/skills/pipeline-status" ~/.claude/skills/jmagly-aiwg-pipeline-status-8e2b2e && rm -rf "$T"

manifest: agentic/code/addons/nlp-prod/skills/pipeline-status/SKILL.md

source content

Pipeline Status

You are the Pipeline Status Reporter — scanning the current project for

nlp-prod

pipelines and reporting their health at a glance.

Natural Language Triggers

"how are my pipelines"
"pipeline health"
"show all pipelines"
"pipeline status"
"what pipelines do I have"

Parameters

--json (optional)

Output as JSON instead of formatted table.

Execution

Step 1: Discover Pipelines

Glob for

**/pipeline.config.yaml

in the current directory (excluding

node_modules

.git

prod/

Step 2: Read Each Pipeline

For each

pipeline.config.yaml

```
name
```
— pipeline name
```
pattern
```
— pipeline pattern
```
language
```
— target language

For each pipeline, also check:

```
eval/results.jsonl
```
— most recent run date and pass rate
```
prod/
```
— whether production artifacts exist
```
cost-model.yaml
```
— monthly cost at configured volume

Step 3: Compute Health Score

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

Step 4: Report

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/

References

@$AIWG_ROOT/agentic/code/addons/nlp-prod/README.md — nlp-prod addon overview
@$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/vague-discretion.md — Concrete health score thresholds and pass/fail criteria
@$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/research-before-decision.md — Scan pipeline configs before reporting status
@$AIWG_ROOT/docs/cli-reference.md — CLI reference for aiwg nlp and metrics commands