بنقرة واحدة
dogfood
Exploratory QA of web apps: find bugs, evidence, reports.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Exploratory QA of web apps: find bugs, evidence, reports.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Funny animal podcast creation assistant. Given a topic, automatically generates a comedic podcast video hosted by two animal "hosts." Full pipeline: character design, comedy script, character portraits, TTS voice + Seedance 2.0 video generation, subtitle/pop-text/sound-effect post-production. Trigger words: animal podcast, funny podcast, pet podcast, animal talk show, pet talk show.
Professional anime/2D art style generation skill. Covers 14 sub-styles (modern Japanese anime/moe, retro Japanese cel-shading, Japanese shonen, Japanese shojo, Ghibli, Makoto Shinkai, Chinese xianxia/ink wash, modern Chinese anime, Chinese 3D fantasy, Korean webtoon, Korean impasto, Western cartoon, chibi/moe, 2D cyberpunk) + 5 anti-failure iron laws + cross-style shared rules (character lock / facial proportion spec / stroke consistency / universal negative). Core capabilities: precise style targeting, consistent character identity, cross-style conversion. Trigger: "anime", "2D art", "manga", "illustration", "Japanese anime", "Chinese anime", "Korean webtoon", "webtoon", "Western cartoon", "ghibli", "shinkai", "ufotable", "trigger style", "cel-shading", "impasto", "chibi", "moe", "catgirl", "Chinese 3D fantasy", "xianxia", "ink wash", "hanfu character", "cyberpunk anime", "draw an anime character", "make an anime avatar", "anime character", "anime style". NOT for: photorealistic (use image agent default) / s
Specialized in anime/2D/character stylization for image generation and conversion. Covers Japanese, Chinese, Korean, and Western art style families. Uses provenance analysis to trace reference images' style DNA, performs a 10-dimension analysis → 3-dimension collapse to precisely lock the style's essence, then matches the optimal tool and prompt approach for generation. Trigger on: "anime-ify", "2D style", "convert to anime", "cel-shading", "ghibli style", "Korean watercolor", "fantasy 3D", "chibi", "Japanese anime style", "style conversion", "manga style", "character illustration", "anime style", "webtoon style", or any request involving converting content into a specific anime/2D art style. Key distinction: User requests generation or conversion to a specific anime/2D art style. Do NOT trigger for: photorealistic photography style, pure logo design, general image editing (crop/background removal etc.).
Audiobook creation assistant. Converts book text into multi-character narrated audio, supporting audiobook production, multi-character voiceover, novel narration, TTS voiceover, and read-aloud scenarios. Automatically identifies dialogue and narration, assigns a distinct voice to each character, intelligently adds pause markers, and generates natural, fluent audiobook audio. Trigger phrases: audiobook, read aloud, TTS book, multi-character voiceover, novel narration, book narration, voice acting, narration, 有声书, 朗读, 读书, 多角色配音, 小说朗读, 读书配音. Supports chapter-level generation — user confirms the first chapter, then remaining chapters continue sequentially.
Beat-sync video editing skill. Input music (URL / local file / AI-generated) + video or image assets, automatically performs energy-tension analysis → smart trimming → beat detection → beat-synced timeline generation → asset matching → ffmpeg concatenation, outputting a beat-synced video perfectly aligned to the music. Supports image slideshows, video clips, and mixed assets as input. Supports every-N-beat asset switching, automatic intro/chorus segmentation, user-annotated keypoints, and other beat-sync modes. Trigger words include: music beat sync, beat-sync editing, beat sync, beat-sync video, beat-synced editing, beat-sync video, music rhythm editing, rhythm beat sync, beat detection, edit to the beat, auto beat sync, music sync edit.
Video project push tool. Programmatically creates editing projects via Python scripts, with one-click push to JianyingPro or CapCut. Direct mode creates project files directly in the local JianyingPro/CapCut draft directory — open the app and start editing. Built on pyJianYingDraft (PyPI). Trigger words include: JianyingPro, CapCut, push, push to JianyingPro, capcut, export to JianyingPro.
| name | dogfood |
| description | Exploratory QA of web apps: find bugs, evidence, reports. |
| version | 1.0.0 |
| platforms | ["linux","macos","windows"] |
| metadata | {"hermes":{"tags":["qa","testing","browser","web","dogfood"],"related_skills":[]}} |
| author | Hermes Agent |
| license | MIT |
This skill guides you through systematic exploratory QA testing of web applications using the browser toolset. You will navigate the application, interact with elements, capture evidence of issues, and produce a structured bug report.
browser_navigate, browser_snapshot, browser_click, browser_type, browser_vision, browser_console, browser_scroll, browser_back, browser_press)The user provides:
./dogfood-output)Follow this 5-phase systematic workflow:
{output_dir}/
├── screenshots/ # Evidence screenshots
└── report.md # Final report (generated in Phase 5)
For each page or feature in your plan:
Navigate to the page:
browser_navigate(url="https://example.com/page")
Take a snapshot to understand the DOM structure:
browser_snapshot()
Check the console for JavaScript errors:
browser_console(clear=true)
Do this after every navigation and after every significant interaction. Silent JS errors are high-value findings.
Take an annotated screenshot to visually assess the page and identify interactive elements:
browser_vision(question="Describe the page layout, identify any visual issues, broken elements, or accessibility concerns", annotate=true)
The annotate=true flag overlays numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands.
Test interactive elements systematically:
browser_click(ref="@eN")browser_type(ref="@eN", text="test input")browser_press(key="Tab"), browser_press(key="Enter")browser_scroll(direction="down")After each interaction, check for:
browser_console()browser_vision(question="What changed after the interaction?")For every issue found:
Take a screenshot showing the issue:
browser_vision(question="Capture and describe the issue visible on this page", annotate=false)
Save the screenshot_path from the response — you will reference it in the report.
Record the details:
Classify the issue using the issue taxonomy (see references/issue-taxonomy.md):
Generate the final report using the template at templates/dogfood-report-template.md.
The report must include:
MEDIA:<screenshot_path> for inline images)Save the report to {output_dir}/report.md.
| Tool | Purpose |
|---|---|
browser_navigate | Go to a URL |
browser_snapshot | Get DOM text snapshot (accessibility tree) |
browser_click | Click an element by ref (@eN) or text |
browser_type | Type into an input field |
browser_scroll | Scroll up/down on the page |
browser_back | Go back in browser history |
browser_press | Press a keyboard key |
browser_vision | Screenshot + AI analysis; use annotate=true for element labels |
browser_console | Get JS console output and errors |
browser_console() after navigating and after significant interactions. Silent JS errors are among the most valuable findings.annotate=true with browser_vision when you need to reason about interactive element positions or when the snapshot refs are unclear.MEDIA:<screenshot_path> so they can see the evidence inline.