菜单
Produces verified datasets, verified evaluation results, and a deployable contract bundle for a workflow. Use when you need provable correctness at data and evaluation boundaries. Trigger with 'verify workflow', 'validate contract', or 'run verification pipeline'.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | org-verification-pipeline |
| description | Produces verified datasets, verified evaluation results, and a deployable contract bundle for a workflow. Use when you need provable correctness at data and evaluation boundaries. Trigger with 'verify workflow', 'validate contract', or 'run verification pipeline'. |
| allowed-tools | Read,Write,Glob,Grep,Bash(python:*),Bash(jq:*) |
| version | 0.1.0 |
| author | Your Team <team@example.com> |
| license | MIT |
Produce verified artifacts (data, metrics, contracts) using a 5-phase gated workflow with deterministic scripts.
This skill runs a strict pipeline:
reports/<project>/<timestamp>/.agents/ and procedures in references/.report_path exists{baseDir}/scripts/verify_schema.py{baseDir}/scripts/verify_integrity.py and {baseDir}/scripts/verify_frequency.py{baseDir}/scripts/run_backtest.py{baseDir}/scripts/verify_api_examples.shreports/<project>/<timestamp>/0X-*.md: evidence reportsreports/<project>/<timestamp>/quality.json: quality summaryreports/<project>/<timestamp>/metrics.json: evaluation metricsreports/<project>/<timestamp>/contract/**: deployable contract bundleError: Phase JSON is invalid / missing keys
Solution: Re-run the phase and ensure strict JSON output contract is met.
Error: Verification script fails
Solution: Treat script output as ground truth; update mapping, data, or plan until scripts pass.
# Example: run schema verification (Phase 1)
python {baseDir}/scripts/verify_schema.py \
--input data/raw.csv \
--schema references/00-canonical-schema.json \
--out reports/example/2025-01-01T00-00-00Z/quality.json
{baseDir}/references/{baseDir}/agents/{baseDir}/scripts/Generate and configure Nixtla Skills using the CLI for forecasting workflows. Use when installing or updating skills. Trigger with 'install nixtla skills' or 'update nixtla'.
Generate benchmarking pipelines to compare forecasting models and summarize accuracy/speed trade-offs. Use when evaluating TimeGPT vs StatsForecast/MLForecast/NeuralForecast on a dataset. Trigger with "benchmark models", "compare TimeGPT vs StatsForecast", or "model selection".
Research and summarize Nixtla ecosystem updates and time-series forecasting content from the web and GitHub. Use when gathering release notes, recent changes, or best-practice references. Trigger with "Nixtla updates", "what's new with TimeGPT", or "find time-series papers".
Generate production-ready TimeGPT forecasting pipeline code from requirements. Use when scaffolding a pipeline with validation, logging, visualization, and repeatable runs. Trigger with "create TimeGPT pipeline", "build TimeGPT integration", or "generate forecast code".
Analyze Nixtla baseline forecasting results (sMAPE/MASE on M4 or other benchmark datasets). Use when the user asks about baseline performance, model comparisons, or metric interpretation for Nixtla time-series experiments. Trigger with "baseline review", "interpret sMAPE/MASE", or "compare AutoETS vs AutoTheta".
Orchestrate 6-phase changelog generation workflow with AI synthesis, multi-source data fetching (GitHub/Slack/Git), quality validation, and automated PR creation. Use when automating release notes, weekly changelogs, or documentation updates. Trigger with "generate changelog", "weekly changelog", or "automate release notes".