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Found 80 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
Use when working on multiple branches simultaneously, context switching without stashing, reviewing PRs while developing, testing in isolation, or comparing implementations across branches - provides git worktree commands and workflow patterns for parallel development with multiple working directories.
Use after /execute to verify the changes are correct. Reads .handoff/{config,plan,task}.md, runs the verification commands from plan (typically test + lint; typecheck is execute's job), compares plan-vs-code, writes review.md, and on success cleans up handoff state. For multi-phase plans, advances phase markers instead of full cleanup until the last phase. STRONGLY recommended to run in a fresh chat — fresh context is the entire point of this stage. Part of the agent-handoff bundle (4 skills) — install /setup-handoff, /plan, /execute, /verify together.
Polish a single H3 unit file under `sections/` into survey-grade prose (de-template + contrast/eval/limitation), without changing citation keys. **Trigger**: subsection polisher, per-subsection polish, polish section file, 小节润色, 去模板, 结构化段落. **Use when**: `sections/S*.md` exists but reads rigid/template-y; you want to fix quality locally before `section-merger`. **Skip if**: subsection files are missing, evidence packs are incomplete, or `Approve C2` is not recorded. **Network**: none. **Guardrail**: do not invent facts/citations; do not add/remove citation keys; keep citations within the same H3; keep citations subsection-scoped.
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.
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.
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.
Multi-route literature expansion + metadata normalization for evidence-first surveys. Produces a large candidate pool (`papers/papers_raw.jsonl`, target ≥1200) with stable IDs and provenance, ready for dedupe/rank + citation generation. **Trigger**: evidence collector, literature engineer, 文献扩充, 多路召回, snowballing, cited by, references, 元信息增强, provenance. **Use when**: 需要把候选文献扩充到 ≥1200 篇并补齐可追溯 meta（survey pipeline 的 Stage C1，写作前置 evidence）。 **Skip if**: 已经有高质量 `papers/papers_raw.jsonl`（≥1200 且每条都有稳定标识+来源记录）。 **Network**: 可离线（靠 imports）；雪崩/在线检索需要网络。 **Guardrail**: 不允许编造论文；每条记录必须带稳定标识（arXiv id / DOI / 可信 URL）和 provenance；不写 output/ prose。
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
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.