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Run the test, lint, and typecheck commands for the repository, then summarize failures and next actions for coding agents.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
| name | repo-quality-gates |
| description | Run the test, lint, and typecheck commands for the repository, then summarize failures and next actions for coding agents. |
Use this skill when you need to validate a change or interpret failing automation for this repository.
pip install langchain langchain-core langchain-openai langchain_experimental langgraphpython -m pytest test_validate_run.py -qpython validate_run.py --latest --log-path notebook_run_log.txt --window 180python validate_artifact_quality.py --latestpython -m pytest tests/flake8 .For notebook-completion work, the current W14 baseline is IDD_run_run_default_id-20260504-1338-b3079aea. A clean proof means:
validate_run.py scores 12/12.validate_artifact_quality.py scores 9/9.final_report.html, final_report.md, and final_report.pdf exist and embed resolving visualizations.This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
Use the write_todos tool effectively for task planning and decomposition in Deep Agents. Use when users want to (1) implement task planning with write_todos, (2) break down complex tasks into subtasks, (3) track agent progress through todos, (4) debug why todos aren't completing, (5) design todo structures for different task types (research, coding, analysis), (6) understand todo status lifecycle and best practices, or (7) visualize todo progression from LangSmith traces.
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications.
Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications. Use when users want to (1) implement multi-agent systems, (2) coordinate multiple specialized agents, (3) choose between coordination patterns, (4) set up supervisor-subagent workflows, (5) implement router-based agent selection, (6) create parallel orchestrator-worker patterns, (7) implement agent handoffs, (8) design state schemas for multi-agent systems, or (9) debug multi-agent coordination issues.
Implement LangGraph error handling with current v1 patterns. Use when users need to classify failures, add RetryPolicy for transient issues, build LLM recovery loops with Command routing, add human-in-the-loop with interrupt()/resume, handle ToolNode errors, or choose a safe strategy between retry, recovery, and escalation.