Back to list
intent-solutions-io

nixtla-universal-validator

by intent-solutions-io

Claude Code plugin concepts for Nixtla - Generate TimeGPT pipelines, model benchmarks, and FastAPI services from natural language

1🍴 0📅 Jan 24, 2026

SKILL.md


name: nixtla-universal-validator description: "Validate Nixtla skills and plugins with deterministic evidence bundles and strict schema gates. Use when auditing changes or enforcing compliance. Trigger with 'run validation' or 'audit validators'." allowed-tools: "Read,Write,Bash(python:),Bash(bash:),Bash(pytest:*)" version: "1.0.0" author: "Jeremy Longshore jeremy@intentsolutions.io" license: MIT

Nixtla Universal Validator

Purpose

Produce deterministic, reviewable validation evidence (reports + JSON + logs) for a repo, plugin, or skill.

Overview

This skill combines two layers:

  • A multi-phase subagent workflow (for human-readable analysis + reconciliation)
  • A deterministic validator runner (for ground-truth logs and machine-readable summaries)

Validation runs as a pipeline with deterministic gates:

  • Discover what changed and what should be validated
  • Validate schemas/structure (skills + plugins) using canonical repo validators
  • Run behavioral checks (tests) when requested
  • Reconcile results into a single evidence bundle with pass/fail and next actions

This pattern generalizes beyond Nixtla by swapping the check catalog (a list of commands + expected artifacts).

Prerequisites

  • Python 3.11+
  • Repo validators available:
    • 004-scripts/validate_skills_v2.py
    • 004-scripts/validate-all-plugins.sh
  • Optional for plugin validation: jq

Instructions

Step 1: Create a run directory

Use the built-in runner to create a timestamped evidence bundle under reports/<project>/<timestamp>/.

Step 2: Pick a target scope

Choose one:

  1. Repo root: validate everything
  2. A plugin folder: 005-plugins/<plugin>
  3. A skill folder: .claude/skills/<skill> or 003-skills/.claude/skills/<skill>

Step 3: Run the deterministic validator suite

python {baseDir}/scripts/run_validator_suite.py \
  --target . \
  --project nixtla \
  --out reports/nixtla

List built-in profiles:

python {baseDir}/scripts/run_validator_suite.py \
  --list-profiles \
  --target . \
  --project nixtla \
  --out reports/nixtla

To validate a single plugin:

python {baseDir}/scripts/run_validator_suite.py \
  --target 005-plugins/nixtla-baseline-lab \
  --project nixtla-baseline-lab \
  --out reports/nixtla-baseline-lab

Step 4: (Optional) Include tests

python {baseDir}/scripts/run_validator_suite.py \
  --target . \
  --project nixtla \
  --out reports/nixtla \
  --run-tests

Step 4b: (Optional) Run an enterprise profile

python {baseDir}/scripts/run_validator_suite.py \
  --target . \
  --project nixtla \
  --out reports/nixtla \
  --profile enterprise \
  --fail-on-warn \
  --run-tests

Step 5: (Optional) Use the multi-phase subagent workflow

Run phases in order using the prompts in {baseDir}/agents/ and procedures in {baseDir}/references/. Each phase must write a report file under the run directory and return strict JSON per the phase contract.

Output

Each run creates a timestamped evidence bundle:

  • reports/<project>/<timestamp>/summary.json
  • reports/<project>/<timestamp>/report.md
  • reports/<project>/<timestamp>/checks/*.log

Error Handling

  1. Error: Validator command not found
    Solution: Confirm repo scripts exist and run from the repo root.

  2. Error: Plugin validation fails due to jq
    Solution: Install jq or run only skill validation.

  3. Error: Tests fail after schema passes
    Solution: Treat this as a behavioral regression; fix tests or code, then re-run.

Examples

Common validations:

# Strict schema/structure gates
python 004-scripts/validate_skills_v2.py --fail-on-warn
bash 004-scripts/validate-all-plugins.sh .

Generate an evidence bundle (profile-driven):

# Generate a single evidence bundle for a PR
python {baseDir}/scripts/run_validator_suite.py \
  --target . \
  --project pr-1234 \
  --out reports/pr-1234 \
  --run-tests

Resources

  • Subagent orchestration pattern: 000-docs/000a-planned-skills/templates/verification-pipeline/README.md
  • Canonical skills validator: 004-scripts/validate_skills_v2.py
  • Canonical plugin validator: 004-scripts/validate-all-plugins.sh
  • Subagent prompts: {baseDir}/agents/
  • Phase procedures: {baseDir}/references/

Score

Total Score

70/100

Based on repository quality metrics

SKILL.md

SKILL.mdファイルが含まれている

+20
LICENSE

ライセンスが設定されている

+10
説明文

100文字以上の説明がある

+10
人気

GitHub Stars 100以上

0/15
最近の活動

3ヶ月以内に更新がある

0/10
フォーク

10回以上フォークされている

0/5
Issue管理

オープンIssueが50未満

+5
言語

プログラミング言語が設定されている

+5
タグ

1つ以上のタグが設定されている

0/5

Reviews

💬

Reviews coming soon