pipeline-status
Show status overview of all LLM inference pipelines in the current project
Best use case
pipeline-status is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
It is a strong fit for teams already working in Codex.
Show status overview of all LLM inference pipelines in the current project
Teams using pipeline-status should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pipeline-status/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pipeline-status Compares
| Feature / Agent | pipeline-status | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Show status overview of all LLM inference pipelines in the current project
Which AI agents support this skill?
This skill is designed for Codex.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
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