Plugins

A workbench for engineering and math problems, with nine tools for reading images, solving, plotting and verifying answers, plus four bundled skills. Needs Python with numpy and Pillow. Image reading, figure checking and formula typesetting rely on a vision provider you have configured a key for, so those images are sent to it, and without a key they fail while the other tools still work.

DSH plugin bundling the kimi-ppt skill: create, edit and export PPT slides and presentations as a locally generated .pptx with Pillow-rendered previews, no browser needed.

DSH plugin bundling the kimi-ppt skill: create, edit and export presentations as a locally generated .pptx with Pillow-rendered previews. Derived from the MIT open-kimi-ppt-skill.

Third-party DSH bundle carrying the original deterministic-pixel-art Skill for editable JSON/PNG pixel art, Python rendering and recoloring within declared regions. Needs DSH 0.2.2-rc.1 (other DSH versions are not claimed compatible) and Python 3.10+ with Pillow.

Bundles 19 short-video production skills (1 entry point, 1 orchestration, 17 atomic) into the agent skill catalog: Chinese TTS pronunciation fixes, text-to-image and text-to-video, image panning, composition, covers, media verification, data review and four-platform publishing. Ships skills and scripts only, so a runnable pipeline still needs ffmpeg, Python 3 + Pillow, Node.js and playwright installed locally, your own Bilibili biliup credential and WeChat Channels / Douyin / Xiaohongshu login states, and the placeholder values under skills/*/scripts/ filled in with your own. An installer's own skill of the same name keeps precedence.

Turns one journal paper PDF into three deliverables by default — a Chinese group-meeting literature report deck (.pptx) in the Nanyang Normal University physics template, a matching Word speaker script, and a two-colour defence-prediction Q&A HTML (ten-odd questions with short direct answers). Requires Python 3 with python-pptx, python-docx, Pillow and pymupdf (pypdf fallback); install them with pip install -r requirements.txt.

Turns one journal paper PDF into a Chinese group-meeting literature report deck (.pptx) in the Nanyang Normal University physics template, plus a matching Word speaker script. Requires Python 3 with python-pptx, python-docx, Pillow and pymupdf (pypdf fallback); install them with pip install -r requirements.txt.

Deck-generation skill for the National College Student Career Planning Competition. It takes a contestant's profile, materials and photographs, drafts a page-by-page outline for confirmation, then renders a 15-20 page reading-type PPTX (under 15 pages is flagged as a page-count error) using 17 page types, three built-in themes or a user-supplied template; the build audits the estimated layout (text overflow, per-page word count, missing evaluation dimensions, material gaps) and the rendered PDF is checked again for out-of-bounds and overlapping text. Requires Python 3.9+ with python-pptx, python-docx and Pillow; LibreOffice (soffice) and pdftotext are optional for the PDF layout re-check.

Prepares a repository README before pushing: scans repo facts, renders banner and process-diagram images (needs Pillow and the Microsoft YaHei font), lints layout and section numbering, then commits and pushes with retry.

Windows desktop automation for DeepSeek Harness: accessibility-first observe/act loop with 30 standard tools (element/coordinate targets, auto/a11y/event strategy routing), superseded-observations and no-replay safety semantics, persistent kill switch, RuntimeId/rect-drift stale protection, chat cards and a floating panel (live ops, frame wall, env auto-detect settings), tiled vision describe for low-res models, JSONL audit trail and a pid allowlist. Requires Windows 10+ and Python 3.9+ with uiautomation and pillow (installable from the settings page).

Registers the remove-pdf-background-gray skill for DSH: whitens gray/off-white scan backgrounds in image-based PDFs while preserving resolution, page geometry, and anti-aliased text edges (lossless Flate write-back), via a single Python script (pypdf + Pillow + numpy).

Bundled Pixiv skill backed by a Python CLI: search illustrations, manga and novels, browse daily/weekly/monthly/rookie/original/AI/R-18 rankings, look up works, novel series and artists, and download originals, with multi-page works merged into PDF, ugoira converted to GIF and novel series merged into TXT; requires Python 3.9+ with requests, img2pdf and Pillow installed (pip install -r requirements.txt), and R-18 content needs your own Pixiv cookie.

Registers one `ocr` tool for text-only models: it runs the offline script bundled in the repo over a single image or PDF and returns the recognized text — Windows native OCR first, then RapidOCR, then Tesseract. Recognition runs locally with no network request and no API key. Needs Python 3.11 on PATH plus `pip install winocr pillow` (winocr is the preferred engine; RapidOCR and Tesseract are fallbacks). Per-line results for images, per-page results for PDFs.

Registers the remove-pdf-background-gray skill for DSH: whitens gray/off-white scan backgrounds in image-based PDFs while preserving resolution, page geometry, and anti-aliased text edges (lossless Flate write-back), via a single Python script (pypdf + Pillow + numpy).

Registers the video-studio skill in the DSH skill catalog. It builds a Chinese-narrated video from a script: edge-tts synthesizes the narration, the timeline is derived from each line's measured audio duration rather than estimated from text length, Pillow renders every frame deterministically and pipes the raw frames to ffmpeg as rawvideo, burned-in subtitles and SRT/ASS export come from that same timeline, physical-model sound effects and a generated score are mixed sample by sample with numpy, and QA runs in four layers: container, signal, temporal and page level (the page layer only when the browser engine is used). An optional Playwright-driven browser engine renders the whole film as Canvas2D instead, sending PNG frames through image2pipe. Needs network access for the narration, ffmpeg on PATH and Python with numpy, Pillow and edge-tts; the browser engine also needs Playwright with a local Edge/Chromium.

Measures how much a short clip changed and when — changed-area share, displacement, cumulative drift — returning one labelled key-frame image plus the number behind each event; it does not pick shots for a storyboard. Needs Python 3.10+ with numpy and Pillow; reading mp4 also needs ffmpeg and ffprobe on PATH.

Video study toolkit for DeepSeek Harness: fetch a public Bilibili single part or YouTube video, sample every 3 seconds plus change-triggered frames, transcribe locally with faster-whisper, page through section grids, and render a 16:9 study deck. Windows only, and needs the DSH pwsh tool plus Python 3.12 x64 with Pillow and FFmpeg/FFprobe; fetching adds yt-dlp, transcription needs faster-whisper with your own local model files, and deck output needs python-pptx.

Turn a feature that actually runs into a trustworthy delivery demo video, or a defect into a traceable feedback video: real screen recordings, edge-tts narration with word-clock subtitles, a self-contained ffmpeg pipeline, and a deterministic quality gate (spec, audio, loudness, black/freeze/silence scans) that refuses to pass a fake or broken demo. Feedback episodes add severity/phase chips, a repro-steps overlay and a ticket-ready report bound to the artifact SHA256. Preflight and artifact verification included. Requires Python 3.9+, ffmpeg/ffprobe on PATH, and `pip install edge-tts Pillow`; narration calls the public edge-tts endpoint.

Lets an AI play Slay the Spire 2: screenshot plus mouse/keyboard tools, a local card lookup, and a readable play log. Requires Windows and Python 3.10 or newer with Pillow.

Syntax-highlighted code for reports via one highlight_code tool: 68 languages, 15 themes, 11 output formats (inline HTML, Word-ready SVG/PNG, LaTeX), incl. the classic SyntaxHighlighter themes. PNG output needs Python 3 with Pillow; other formats need no external runtime.

Registers two tools for DeepSeek Harness. `doc_ocr` turns scanned PDFs and images into structured text (per-page text, financial tables with a balance-sheet tie-out check, contract-clause hits) through a local RapidOCR engine or a Zhipu GLM-OCR cloud engine, and `doc_preprocess` turns a directory of raw files into an Obsidian-ready knowledge base through a seven-stage pipeline with backend routing and sha256-based incremental skipping. Numbers that fail the tie-out are reported, not invented. The local engine needs Python 3 with rapidocr-onnxruntime, pypdfium2 and Pillow (pip install -r requirements.txt); the GLM engine needs ZHIPU_API_KEY.