Initialize a new workspace by copying the standard artifact template (STATUS.md, CHECKPOINTS.md, UNITS.csv, DECISIONS.md + folders). **Trigger**: workspace init, initialize workspace, workspace template, 初始化 workspace. **Use when**: 启动任何 pipeline run（必须先有 workspace 工件与目录骨架）。 **Skip if**: workspace 已初始化且不希望覆盖既有文件（除非显式 `--overwrite`）。 **Network**: none. **Guardrail**: 不要修改 `.codex/skills/workspace-init/assets/` 模板；默认不覆盖已有文件。

Extract per-subsection “anchor facts” (NO PROSE) from evidence packs so the writer is forced to include concrete numbers/benchmarks/limitations instead of generic summaries. **Trigger**: anchor sheet, anchor facts, numeric anchors, evidence hooks, 写作锚点, 数字锚点, 证据钩子. **Use when**: `outline/evidence_drafts.jsonl` exists and you want stronger, evidence-anchored writing in `sections/*.md`. **Skip if**: evidence packs are incomplete (fix `evidence-draft` first). **Network**: none. **Guardrail**: NO PROSE; do not invent facts; only select from existing evidence snippets/highlights.

Download a small corpus of open-access arXiv survey/review PDFs about LLM agents and extract text for style learning. **Trigger**: agent survey corpus, ref corpus, download surveys, 学习综述写法, 下载 survey. **Use when**: you want to study how real agent surveys structure sections (6–8 H2), size subsections, and write evidence-backed comparisons. **Skip if**: you cannot download PDFs (no network) or you don't want local PDF files. **Network**: required. **Guardrail**: only download arXiv PDFs; store under `ref/` and keep large files out of git.

Build a 2+ level taxonomy (`outline/taxonomy.yml`) from a core paper set and scope constraints, with short descriptions per node. **Trigger**: taxonomy, taxonomy builder, 分类, 主题树, taxonomy.yml. **Use when**: survey/snapshot 的结构阶段（NO PROSE），已有 `papers/core_set.csv`，需要生成可映射且读者友好的主题结构。 **Skip if**: 已经有批准过且可映射的 taxonomy（不要无意义重构）。 **Network**: none. **Guardrail**: 避免泛化占位桶；保持 2+ 层且每节点有具体描述。

Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.

Finance Guru™ Core Context Loader Auto-loads essential Finance Guru system configuration and user profile at session start. Ensures complete context availability for all financial operations.

Runs unit tests to quickly verify changes during the development loop.

Runs all necessary checks (lint, tests) and pushes to GitHub. Use this as the final safety gate.

Fetches comments and reviews from the current GitHub Pull Request and formats them as Markdown.

Prepares the codebase for a commit by formatting code and helping identify temporary comments.

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.

Fix Dependabot security alerts by updating vulnerable npm dependencies. Use when the user mentions "dependabot", "security alerts", "vulnerability", "CVE", or wants to update packages with security issues.

Comprehensive API testing patterns including contract testing, REST/GraphQL testing, and integration testing. Use when testing APIs or designing API test strategies.

Enable, disable, and manage debug mode for agentdev sessions. Records all tool invocations, skill activations, hook triggers, and agent delegations to JSONL. Use when debugging agent behavior, optimizing workflows, or analyzing session performance.

This skill provides guidance for writing test factories in the Packmind codebase. It should be used when creating or updating factory functions in `**/test/*Factory.ts` files to ensure realistic test data with variety.

Comprehensive kanban board and task management via ktui CLI. Use for project tracking, todo lists, task dependencies, workflow automation, and board management. Activates when user mentions boards, tasks, kanban, or project management. If the `ktui` command is not available, but `uv` is available utilize `uvx kanban-tui` instead.

Multi-agent development workflow system. Load when orchestrating development tasks, spawning subagents, or managing workflow phases.