Build per-H3 writer context packs (NO PROSE): merge briefs + evidence packs + anchor facts + allowed citations into a single deterministic JSONL, so drafting is less hollow and less brittle. **Trigger**: writer context pack, context pack, drafting pack, paragraph plan pack, 写作上下文包. **Use when**: `outline/subsection_briefs.jsonl` + `outline/evidence_drafts.jsonl` + `outline/anchor_sheet.jsonl` exist and you want to make C5 drafting easier/more consistent. **Skip if**: upstream evidence is missing or scaffolded (fix `paper-notes` / `evidence-binder` / `evidence-draft` / `anchor-sheet` first). **Network**: none. **Guardrail**: NO PROSE; do not invent facts/citations; only use citation keys present in `citations/ref.bib`.

Evidence self-loop for surveys: read evidence bindings + evidence packs, then write an actionable upstream TODO plan (which stage/skill to fix) before writing more prose. Writes `output/EVIDENCE_SELFLOOP_TODO.md`. **Trigger**: evidence self-loop, evidence loop, evidence gaps, binding gaps, blocking_missing, 证据自循环, 证据缺口回路. **Use when**: C4 outputs exist (`outline/evidence_bindings.jsonl`, `outline/evidence_drafts.jsonl`) but writing looks hollow or C5 is BLOCKED due to thin evidence. **Skip if**: you are still pre-C3 (no notes/evidence bank yet), or you want to draft anyway and accept a lower evidence bar. **Network**: none. **Guardrail**: analysis-only; do not edit evidence/writing artifacts; do not invent facts/citations; only write the TODO report.

Audit the workspace against the pipeline artifact contract (DONE outputs + pipeline target_artifacts). Writes `output/CONTRACT_REPORT.md`. **Trigger**: contract audit, artifact contract, missing artifacts, target_artifacts, CONTRACT_REPORT. **Use when**: you want an auditable PASS/FAIL view of whether a workspace is complete and self-contained (end of run or before sharing). **Skip if**: you are still intentionally mid-run and don’t care about completeness yet (but it’s still useful as a snapshot). **Network**: none. **Guardrail**: analysis-only; do not edit content artifacts; only write the report.

Create per-subsection evidence packs (NO PROSE): claim candidates, concrete comparisons, evaluation protocol, limitations, plus citation-backed evidence snippets with provenance. **Trigger**: evidence draft, evidence pack, claim candidates, concrete comparisons, evidence snippets, provenance, 证据草稿, 证据包, 可引用事实. **Use when**: `outline/subsection_briefs.jsonl` exists and you want evidence-first section drafting where every paragraph can be backed by traceable citations/snippets. **Skip if**: `outline/evidence_drafts.jsonl` already exists and is refined (no placeholders; >=8 comparisons per subsection; `blocking_missing` empty). **Network**: none (richer evidence improves with abstracts/fulltext). **Guardrail**: NO PROSE; do not invent facts; only use citation keys that exist in `citations/ref.bib`.

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.

Planner-pass coverage + redundancy report for an outline+mapping, producing `outline/coverage_report.md` and `outline/outline_state.jsonl`. **Trigger**: planner, dynamic outline, outline refinement, coverage report, 大纲迭代, 覆盖率报告. **Use when**: you have `outline/outline.yml` + `outline/mapping.tsv` and want a verifiable, NO-PROSE planner pass before writing. **Skip if**: you don't want any outline/mapping diagnostics (or you have a frozen/approved structure and will not change it). **Network**: none. **Guardrail**: NO PROSE; do not invent papers; only report coverage/reuse and propose structural actions as bullets.

Write the survey's front matter files (Abstract, Introduction, Related Work, Discussion, Conclusion) in paper voice, with high citation density and a single evidence-policy paragraph. **Trigger**: front matter writer, introduction writer, related work writer, abstract writer, discussion writer, conclusion writer, 引言, 相关工作, 摘要, 讨论, 结论. **Use when**: you are in C5 (prose allowed) and need the paper-like shell to stop the draft reading like stitched subsections. **Skip if**: `Approve C2` is missing in `DECISIONS.md`, or `citations/ref.bib` is missing. **Network**: none. **Guardrail**: no invented facts/citations; no pipeline jargon in final prose; no repeated evidence disclaimers; only use keys present in `citations/ref.bib`.

Fill `outline/tables_index.md` from `outline/table_schema.md` + evidence packs (NO PROSE in cells; citation-backed rows). **Trigger**: table filler, fill tables, evidence-first tables, index tables, 表格填充, 索引表. **Use when**: table schema exists and evidence packs are ready; you want a compact, citation-backed index table to support later writing and Appendix table curation. **Skip if**: `outline/tables_index.md` already exists and is refined (>=2 tables; citations in rows; no placeholders). **Network**: none. **Guardrail**: do not invent facts; every row must include citations; do not write paragraph cells.

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

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

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.

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.

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.

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.

Claude Code hooks configuration specialist. Use when creating hooks for tool validation, logging, notifications, or custom automation in Claude Code.

Skills for spawning external processes - AI coding agents and generic CLI commands in new terminal windows. Parent skill category for agent and terminal spawning.

**Use MCP repo-map tools when:** - Searching for symbols by name pattern (faster than Grep) - Getting all symbols in a file (faster than Read + parsing)