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Full-site blog health assessment scanning all blog files for quality scores, orphan pages, topic cannibalization, stale content, and AI citation readiness. Runs canonical batch analysis before site-wide checks. Produces per-post scores and a prioritized action queue. Use when the user asks for a site-wide blog health check across many posts ("blog audit", "blog health", "audit all posts", "site audit of the blog"). For one post, or a quality score with fixes for a few named files, use blog-analyze instead.

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Detect keyword cannibalization across blog posts by extracting primary keywords from titles and headings, clustering semantically similar targets, and flagging posts competing for the same search intent. Supports a free local mode (file analysis) and a paid DataForSEO API mode that runs only after the user approves the call count. Outputs a severity-scored report with merge or differentiate recommendations. Use when the user says "cannibalization", "keyword overlap", "competing pages". Not a site audit (blog-audit) or cluster planning (blog-cluster); it changes no files or redirects.

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Make a PortalJS portal harvestable by national/EU/US open-data portals — emit standards-compliant DCAT catalog feeds (DCAT 2/3, DCAT-AP, DCAT-US, national profiles) in JSON-LD, Turtle, and RDF/XML at build, with autodiscovery and per-profile conformance checking. Use when a portal needs to be harvested by data.europa.eu, data.gov, or a national open-data catalog.

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Auto-ingest a geospatial file (GeoJSON, Shapefile, GeoPackage, KML/KMZ, FlatGeobuf, CSV-with-geometry) into a PortalJS portal on the user's own machine, with no server. Normalizes CRS to EPSG:4326, derives a PMTiles render tier and a GeoParquet query tier, pushes all three artifacts to Cloudflare R2 via Git LFS, and appends one dual-tier datasets.json entry the showcase auto-renders. Use when the source is a vector geo format that needs a map or spatial-query view.

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Define a dataset's metadata profile — infer a Frictionless Table Schema from its data, add Data Package metadata (license, sources, keywords), and write it into datasets.json so the showcase renders a typed field table. Extend or customize via the L0-L3 profile ladder. Use when a registered dataset needs field types, constraints, or catalog metadata before publishing.

日本語の概要は準備中です。原文の説明を表示しています。

Dump DEX files from a running Android app for unpacking/deobfuscation. Activate when the user wants to unpack an Android APK, dump DEX from memory, extract decrypted DEX files, or defeat class-loading packing.

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Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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Generate a complete, beginner-friendly explanation of an entire code repository as a set of markdown files. Maps the repo structure with `treecap` (installing it if missing), studies the actual source, then writes a `CODE_EXPLANATION/` folder containing an `OVERVIEW.md` plus numbered deep-dive files (01_architecture.md, 02_models.md, ...) that explain how the code is built, what it does, and how the pieces fit together — illustrated with real code excerpts. Use this skill whenever the user wants to understand, document, onboard onto, or get a walkthrough of a codebase: phrases like "explain this repo", "explain the code", "code explanation", "walk me through this codebase", "help me understand how this is built", "document the architecture", or "what does this project do" — even if they don't say the word "skill". Prefer this over ad-hoc summaries whenever the goal is a durable, written explanation of a whole repo.

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Writing and maintaining lat.md documentation files — structured markdown that describes a project's architecture, design decisions, and test specs. Use when creating, editing, or reviewing files in the lat.md/ directory.

日本語の概要は準備中です。原文の説明を表示しています。

Update user profile information when user shares preferences, habits, or work details. When you need to save user preferences, remember user's work domain, or provide personalized service.

日本語の概要は準備中です。原文の説明を表示しています。

Skip profile question when user explicitly refuses to answer. When user says 'I don't want to answer' or 'skip this question', use this tool to stop asking about that item.

日本語の概要は準備中です。原文の説明を表示しています。

Preview the contents of a .akita-agent package file without installing it. Shows manifest, profile, bundled skills, and validation status.

日本語の概要は準備中です。原文の説明を表示しています。

Get local file path of image sent by user. When user sends image, system auto-downloads it. When you need to process user's image or analyze image content.

日本語の概要は準備中です。原文の説明を表示しています。

Export a local Agent profile as a portable .akita-agent package file. Use when user wants to share, backup, or distribute an Agent with its skills and configuration.

日本語の概要は準備中です。原文の説明を表示しています。

Get current user profile summary to understand user's preferences and context. When you need to check known user info or personalize responses.

日本語の概要は準備中です。原文の説明を表示しています。

List directory contents including files and subdirectories. When you need to explore directory structure, find specific files, or check what exists in a folder.

日本語の概要は準備中です。原文の説明を表示しています。

Search file contents using regex pattern across directories. Cross-platform pure Python implementation (no external tools needed). Returns matching lines with file paths and line numbers.

日本語の概要は準備中です。原文の説明を表示しています。

Get local file path of voice message sent by user. When user sends voice message, system auto-downloads it. When you need to process user's voice message or transcribe voice to text.

日本語の概要は準備中です。原文の説明を表示しています。

Import an Agent from a .akita-agent package file. Installs the Agent profile and any bundled skills to the local system.

日本語の概要は準備中です。原文の説明を表示しています。

Deliver artifacts (files/images/voice) to current IM chat via gateway, returning a receipt. Use this as the only delivery proof for attachments. Text replies are sent automatically - only use this for file/image/voice attachments.

日本語の概要は準備中です。原文の説明を表示しています。

Delete a file or empty directory. Non-empty directories are rejected for safety. Use run_shell for recursive deletion.

日本語の概要は準備中です。原文の説明を表示しています。

Tencent IMA OpenAPI skill for notes and knowledge base management. Use when user mentions knowledge base, notes, memos, file uploads, web page collection, or knowledge search. Supports notes CRUD, knowledge base file upload, web link addition, and content search.

日本語の概要は準備中です。原文の説明を表示しています。

Capture Windows desktop screenshot with automatic file saving. When you need to show desktop state, capture application windows, or record operation results. IMPORTANT - must actually call this tool, never say 'screenshot done' without calling. Returns file_path for deliver_artifacts.

日本語の概要は準備中です。原文の説明を表示しています。