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Found 22 Skills
Extract readable transcripts from Claude Code and Codex CLI session JSONL files
Python logging with loguru and platformdirs. TRIGGERS - loguru, structured logging, JSONL logs, log rotation, XDG directories.
Retrieve paper metadata from arXiv using keyword queries and save results as JSONL (`papers/papers_raw.jsonl`). **Trigger**: arXiv, arxiv, paper search, metadata retrieval, 文献检索, 论文检索, 拉取元数据, 离线导入. **Use when**: 需要一个初始论文集合（survey/snapshot 的 Stage C1），来源为 arXiv（在线检索或离线导入 export）。 **Skip if**: 已经有可用的 `papers/papers_raw.jsonl`，或数据源不是 arXiv。 **Network**: 在线检索需要网络；离线 `--input <export.*>` 不需要网络。 **Guardrail**: 只做 metadata；不要在 `output/` 写长 prose。
Generates a Jupyter notebook that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
Requirement-level progressive roadmap planning with JSONL output. Decomposes requirements into convergent layers (MVP→iterations) or topologically-sorted task sequences, each with testable completion criteria.
Implement, validate, and test JSONLogic rules for portable business logic. Use when working with JSONLogic syntax, creating rules for conditional logic, validating rule structures, testing rules against data, or converting business requirements to JSONLogic. Triggers on requests to "write jsonlogic", "validate jsonlogic", "create a rule", "business logic as JSON", "conditional logic", or any mention of JSONLogic rules.
Convert project plans to JSONL format (issues + dependencies). Use when users ask "convert plan to jsonl", "create jsonl from plan", "export plan as json" or "convert plan to taks", "create tasks from plan", "export plan as tasks".
Convert and browse session transcripts as HTML or Markdown. Supports Claude Code JSONL logs (auto-saved to ~/.claude/projects/) and GitHub Copilot CLI JSONL logs (auto-saved to ~/.copilot/session-state/*/events.jsonl). Auto-detects log source based on available directories and file format. Supports viewing the current session, a specific session by ID, agent background task output files, or all project sessions with optional date-range filtering.
Analyze claude-trace JSONL files for session health, patterns, and actionable insights. Use when debugging session issues, understanding token usage, or identifying failure patterns.
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography.
This skill should be used when inspecting, analyzing, or querying Claude Code session logs. Use when users ask about session history, want to find sessions, analyze context usage, extract tool call patterns, debug agent execution, or understand what happened in previous sessions. Essential for understanding Claude Code's ~/.claude/projects/ structure, JSONL session format, and the erk extraction pipeline.