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Found 73 Skills
Process images and support adding captions to images. Two caption modes: bottom frame style (like a picture frame) and internal sticker style (similar to Xiaohongshu effect). Implemented with PIL/Pillow, supports Chinese and Emoji.
Creates animated GIFs optimized for Slack with proper dimensions (128x128 emoji, 480x480 message), FPS control, color optimization, and file size constraints. Use when asked to "make a GIF for Slack", "create an animated emoji", "design a Slack GIF", or "animate this for Slack". Provides GIFBuilder utilities, frame management, color palette reduction, and validation tools ensuring GIFs meet Slack requirements. Works with Python Pillow (PIL) for frame generation, color quantization, and GIF export with FPS 10-30 and duration under 3 seconds for emoji.
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
Logic coherence pass for per-H3 section files: enforce a clear paragraph-1 thesis and surface paragraph-island risks (connector stats are diagnostic, not a quota) before merging. **Trigger**: logic polisher, section logic, thesis statement, connectors, 段落逻辑, 连接词, 论证主线, 润色逻辑. **Use when**: `sections/S*.md` exist but read like paragraph islands; you want a targeted, debuggable self-loop before `section-merger`. **Skip if**: sections are missing/thin (fix `subsection-writer` first) or evidence packs/briefs are scaffolded (fix C3/C4 first). **Network**: none. **Guardrail**: do not add new citations; do not invent facts; do not change citation keys; do not move citations across subsections.
Use this skill whenever the user asks for a security analysis, vulnerability assessment, security audit, or any form of Security Assessment Report (SAR) over a codebase, infrastructure, API, database, or system. Triggers include: "audit my code", "find security issues", "run a security check", "generate a SAR", "check for vulnerabilities", "is this code secure", or any request that involves evaluating the security posture of a project. Also triggers when the user uploads or references source code, config files, environment variables, or architecture diagrams and asks for a security opinion. Do NOT use for generic coding tasks, code reviews focused on quality rather than security, or performance optimization unless a security angle is explicitly present.
CUDA kernel development, debugging, and performance optimization for Claude Code. Use when writing, debugging, or optimizing CUDA code, GPU kernels, or parallel algorithms. Covers non-interactive profiling with nsys/ncu, debugging with cuda-gdb/compute-sanitizer, binary inspection with cuobjdump, and performance analysis workflows. Triggers on CUDA, GPU programming, kernel optimization, nsys, ncu, cuda-gdb, compute-sanitizer, PTX, GPU profiling, parallel performance.
Audit-style editing pass for `output/DRAFT.md`: remove template boilerplate, improve coherence, and enforce citation anchoring. **Trigger**: polish draft, de-template, coherence pass, remove boilerplate, 润色, 去套话, 去重复, 统一术语. **Use when**: a first-pass draft exists but reads like scaffolding (repetition/ellipsis/template phrases) or needs a coherence pass before global review/LaTeX. **Skip if**: the draft already reads human-grade and passes quality gates; or prose is not approved in `DECISIONS.md`. **Network**: none. **Guardrail**: do not add/remove/invent citation keys; do not move citations across subsections; do not change claims beyond what existing citations support.
Write structured notes for each paper in the core set into `papers/paper_notes.jsonl` (summary/method/results/limitations). **Trigger**: paper notes, structured notes, reading notes, 论文笔记, paper_notes.jsonl. **Use when**: survey 的 evidence 阶段（C3），已有 `papers/core_set.csv`（以及可选 fulltext），需要为后续 claims/citations/writing 准备可引用证据。 **Skip if**: 还没有 core set（先跑 `dedupe-rank`），或你只做极轻量 snapshot 不需要细粒度证据。 **Network**: none. **Guardrail**: 具体可核对（method/metrics/limitations），避免大量重复模板；保持结构化字段而非长 prose。
Create a novelty/prior-work matrix comparing the submission’s contributions against related work (overlaps vs deltas). **Trigger**: novelty matrix, prior-work matrix, overlap/delta, 相关工作对比, 新颖性矩阵. **Use when**: peer review 中评估 novelty/positioning，需要把贡献与相关工作逐项对齐并写出差异点证据。 **Skip if**: 缺少 claims（先跑 `claims-extractor`）或你不打算做新颖性定位分析。 **Network**: none (retrieval of additional related work is out-of-scope unless provided). **Guardrail**: 明确 overlap 与 delta；尽量给出可追溯证据来源（来自稿件/引用/作者陈述）。
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
Evidence-grounded to-be design (ADRs + design doc) and an implementation orchestration plan (a machine-readable task ledger). Activates when the user asks to design a target state, plan the work, or produce a task plan for a goal.
Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.