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Never use mutation commands on a production library.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "asr", "source": "asr-skill:skills/asr/SKILL.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/asr_skill/skills/asr/SKILL.md", "bytes": 8733, "tokens": 1916, "description": "Local speaker-attributed transcription with Nemotron diarization on CPU and Qwen3-ASR on Apple Silicon. The current agent can prepare, edit, and finalize a transcript. Acoustic inference stays on the machine. 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workflow needs to start a command through a trusted localhost API, inspect process status, read output logs, schedule durable one-shot jobs, run a small set of YAML-declared always-on services, or run YAML-declared periodic jobs.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "bright_horizons_photo_sync", "source": "bright-horizons-photo-sync-skill:skills/bright_horizons_photo_sync.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/bright_horizons_photo_download/skills/bright_horizons_photo_sync.md", "bytes": 6958, "tokens": 2320, "description": "增量备份已授权 Bright Horizons 家庭账号可见的 My Bright Day 事件和媒体，续传中断的下载，校验本地文件，并去重导入 macOS Photos 的指定 album。", "description_from": "frontmatter", "scan_hits": {} }, { "name": "chat_gpt_oauth", "source": "chat-gpt-oauth-skill:skills/chat_gpt_oauth.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/chat_gpt_oauth/skills/chat_gpt_oauth.md", "bytes": 9881, "tokens": 2937, "description": "ChatGPT/Codex OAuth Skill: 当用户需要理解、实现或排查以下能力时加载本 skill：", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_circle_post", "source": "circle-post-skill:skills/skill_circle_post.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/circle_post/skills/skill_circle_post.md", "bytes": 4000, "tokens": 936, "description": "Circle Post Skill: Use this skill when a user asks you to convert Markdown into a Circle post, publish a reviewed TipTap JSON file, update an existing Circle post, list spaces/posts, or safely delete a Circle post.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "ct_education", "source": "ct-education-skill:skills/ct_education.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/ct_education_skill/skills/ct_education.md", "bytes": 29780, "tokens": 6432, "description": "Use the existing ct-edu CLI for local educational chest CT exploration, native-slice linkage, limited CPU candidate masks, video rendering, and quality iteration in external private workspaces.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "dataforseo", "source": "dataforseo-skill:skills/dataforseo.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/dataforseo_skill/skills/dataforseo.md", "bytes": 3391, "tokens": 731, "description": "DataForSEO Skill: Use this skill when an agent needs external SEO demand data: keyword suggestions, related keywords, search volume, CPC, keyword difficulty, search intent, SERP feature hints, or domain-ranked keywords.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "design_critique", "source": "design_skill:skills/design_critique.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/design_skill/skills/design_critique.md", "bytes": 5625, "tokens": 1251, "description": "Design Critique: Structured design feedback across multiple dimensions.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "design_skill", "source": "design_skill:skills/design_skill.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/design_skill/skills/design_skill.md", "bytes": 25947, "tokens": 5330, "description": "UI Design Judgment: Give AI coding agents a structured framework for evaluating, critiquing, and improving user interfaces across iOS, Android, and Web.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "design_system", "source": "design_skill:skills/design_system.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/design_skill/skills/design_system.md", "bytes": 7089, "tokens": 1649, "description": "Design System Audit: Audit a UI for design system consistency: token usage, component coverage, naming conventions, and hardcoded value detection.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "frontend_design", "source": "design_skill:skills/frontend_design.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/design_skill/skills/frontend_design.md", "bytes": 8440, "tokens": 1663, "description": "Frontend Design: Create distinctive, production-grade Web interfaces that avoid generic AI-generated aesthetics.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "dinov3_classifier_skill", "source": "dinov3-classifier-skill:skills/skill.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/dinov3_classifier_skill/skills/skill.md", "bytes": 29341, "tokens": 6429, "description": "DINOv3 Classifier Skill: Convert a narrow, repeated visual question into a local classifier without treating proposed vision-model labels, aggregate metrics, or existing reference datasets as ground truth.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "eink_diary", "source": "eink_diary:skills/eink_diary.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/eink_diary/skills/eink_diary.md", "bytes": 11546, "tokens": 4026, "description": "墨记 eink_diary Skill（根 skill）: 给定一个时间窗，自动完成：读取该窗口内用户的真实近况 → 让 AI 把它理解成一段画面描述 → 用图像模型生成一幅竖版 3:4 适配 1200×1600 的意象画 → 刷到 13.3\" E6 电子纸 → 归档当天图与画面描述。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_firecrawl", "source": "firecrawl-skill:skills/skill_firecrawl.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/firecrawl_skill/skills/skill_firecrawl.md", "bytes": 25116, "tokens": 6177, "description": "Firecrawl Skill: Real-time web search, URL content extraction, and account credit-usage reporting through the Firecrawl v2 API.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "firewalla", "source": "firewalla-local-skill:skills/firewalla.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/firewalla_skill/skills/firewalla.md", "bytes": 3948, "tokens": 944, "description": "Local-first, read-only Firewalla visibility and analysis through the firewalla-skill CLI.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "skill_genai_portrait", "source": "genai_portrait_skill:skills/skill_genai_portrait.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/genai_portrait_skill/skills/skill_genai_portrait.md", "bytes": 7170, "tokens": 1384, "description": "Agent-driven portrait and headshot editing with identity preservation, photographic coherence, model iteration, masks, and alpha outputs.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "godaddy", "source": "go-daddy-skill:skills/godaddy.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/go_daddy_skill/skills/godaddy.md", "bytes": 5452, "tokens": 1234, "description": "Read-first GoDaddy domain inventory, authoritative DNS retrieval, and guarded dry-run-first DNS record creation through a least-privilege CLI. Use for owned-domain lists, expiration and protection review, nameserver inspection, DNS inventory, or an explicitly authorized DNS create. Do not use for purchases, transfers, nameserver changes, updates, replacements, or deletes.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "skill_google_maps_routing", "source": "google-maps-routing-skill:skills/skill_google_maps_routing.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/google_maps_routing_skill/skills/skill_google_maps_routing.md", "bytes": 4944, "tokens": 1110, "description": "Google Maps Routing Skill — Agent Reference: If setup is missing, follow the Google Maps API setup section in `README.md`.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "blender_animation", "source": "gpt_3d_skill:skills/blender_animation.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/gpt_3d_skill/skills/blender_animation.md", "bytes": 8559, "tokens": 1734, "description": "Blender Cinematic Animation (`blender_animation`): Type: Workflow.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "blender_compositing_plate", "source": "gpt_3d_skill:skills/blender_compositing_plate.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/gpt_3d_skill/skills/blender_compositing_plate.md", "bytes": 16285, "tokens": 3670, "description": "Blender Compositing Plate (`blender_compositing_plate`): Type: Workflow.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "blender_modeling", "source": "gpt_3d_skill:skills/blender_modeling.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/gpt_3d_skill/skills/blender_modeling.md", "bytes": 10398, "tokens": 2058, "description": "Blender Procedural Modeling (`blender_modeling`): Type: Workflow.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "character_rigging_mocap", "source": "gpt_3d_skill:skills/character_rigging_mocap.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/gpt_3d_skill/skills/character_rigging_mocap.md", "bytes": 18701, "tokens": 3444, "description": "Character Rigging and Motion Capture (`character_rigging_mocap`): Type: Workflow.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "hybrid_ai_video", "source": "gpt_3d_skill:skills/hybrid_ai_video.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/gpt_3d_skill/skills/hybrid_ai_video.md", "bytes": 23819, "tokens": 4777, "description": "Hybrid AI Video (`hybrid_ai_video`): Type: Workflow.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_gpt_3d", "source": "gpt_3d_skill:skills/skill_gpt_3d.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/gpt_3d_skill/skills/skill_gpt_3d.md", "bytes": 8476, "tokens": 1709, "description": "3D Pipeline Root Router (`skill_gpt_3d`): Evaluate incoming user tasks and route to the appropriate focused domain skill:", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "web_walkthrough", "source": "gpt_3d_skill:skills/web_walkthrough.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/gpt_3d_skill/skills/web_walkthrough.md", "bytes": 15702, "tokens": 3065, "description": "WebGL Architectural Walkthrough (`web_walkthrough`): Type: Workflow.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "grok_oauth", "source": "grok-oauth-skill:skills/grok_oauth.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/grok_oauth/skills/grok_oauth.md", "bytes": 6779, "tokens": 2062, "description": "SuperGrok / xAI OAuth Skill: 当用户需要理解、实现或排查以下能力时加载本 skill：", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "ecg_plot", "source": "health-quantification:skills/ecg_plot.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/health_quantification/skills/ecg_plot.md", "bytes": 2636, "tokens": 944, "description": "ECG Plot: 这个命令只读一份已经导出的 JSON，不查询数据库，也不调用 `ecg export`。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "health_quantification", "source": "health-quantification:skills/health_quantification.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/health_quantification/skills/health_quantification.md", "bytes": 11018, "tokens": 3652, "description": "Health Quantification Skill: 通过 Python CLI 访问 SQLite 数据库完成数据查询与写入，由 AI 承担多维分析、可视化与 Markdown 报告撰写。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_image_generation", "source": "image-generation-skill:skills/skill_image_generation.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/image_generation_skill/skills/skill_image_generation.md", "bytes": 7383, "tokens": 1808, "description": "Image Generation Skill: Use this skill when the user asks an AI agent to generate images, edit an image with a prompt, upscale an existing image, or choose between Gemini and OpenAI image models for a local artifact-producing workflow.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "innovation_assistant", "source": "innovation-assistant-skill:skills/innovation_assistant.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/innovation_assistant_skill/skills/innovation_assistant.md", "bytes": 9591, "tokens": 2051, "description": "Skill: Innovation Assistant: Innovation is not a lightning strike; it is disciplined recombination.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "sit_pipeline", "source": "innovation-assistant-skill:skills/sit_pipeline.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/innovation_assistant_skill/skills/sit_pipeline.md", "bytes": 6479, "tokens": 1316, "description": "SIT Pipeline: Template-Driven Innovation on an Existing Product: Produce candidate product ideas by mechanically transforming the product's configuration with fixed templates, then scanning each new form for benefits.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "think_bigger_pipeline", "source": "innovation-assistant-skill:skills/think_bigger_pipeline.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/innovation_assistant_skill/skills/think_bigger_pipeline.md", "bytes": 10048, "tokens": 2244, "description": "Think Bigger Pipeline: Choice Mapping for Open Problems: Decompose the problem, search for proven solution parts (tactics) inside and outside the domain, combine them systematically, and select with explicit preference criteria.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_intake", "source": "intake-skill:skills/skill_intake.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/intake_skill/skills/skill_intake.md", "bytes": 15056, "tokens": 3642, "description": "Install, verify, operate, and debug the Apple Voice Memos Intake Skill via python -m intake_skill.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "app_store_release", "source": "ios-development-skill:skills/app_store_release.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/ios_development_skill/skills/app_store_release.md", "bytes": 18396, "tokens": 3955, "description": "App Store Release: A release run is complete only when all of the following criteria are satisfied:", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "ios_development", "source": "ios-development-skill:skills/ios_development.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/ios_development_skill/skills/ios_development.md", "bytes": 7751, "tokens": 1711, "description": "iOS Development: Provide a unified entry point and routing table for iOS development, establishing shared toolchain, signing, build, device, and privacy conventions across all focused skills.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "real_device", "source": "ios-development-skill:skills/real_device.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/ios_development_skill/skills/real_device.md", "bytes": 25556, "tokens": 5291, "description": "Real Device Development and Testing: Enable an agent to execute automated workflows on a paired physical device—building, installing, passing files into the app container, triggering runs with deep links, benchmarking, and retrieving structured results—without requiring a user to open Xcode, manually tap buttons, or copy logs.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "simulator_testing", "source": "ios-development-skill:skills/simulator_testing.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/ios_development_skill/skills/simulator_testing.md", "bytes": 15235, "tokens": 3351, "description": "Simulator Testing Skill: This skill supports two complementary verification workflows: - **Behavioral testing**: Running unit tests and XCTest/XCUITest suites using accelerated test loops.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "watch_development", "source": "ios-development-skill:skills/watch_development.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/ios_development_skill/skills/watch_development.md", "bytes": 12343, "tokens": 2619, "description": "Apple Watch Development and Testing: Enable an agent to prepare, build, sign, and install watchOS applications on a physical Apple Watch from the command line using official Apple tooling.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_kit", "source": "kit-skill:skills/skill_kit.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/kit_skill/skills/skill_kit.md", "bytes": 4690, "tokens": 1027, "description": "Kit Skill: Use Kit API v4 for agent-controlled broadcast publishing from local Markdown files and read-only analytics.", "description_from": "heading+first_sentence", "scan_hits": { "assigned_secret": [ "API_KEY=replace-with-your-kit-api-key" ], "email": [ "newsletter@example.com" ] } }, { "name": "koyeb", "source": "koyeb-skill:skills/koyeb/SKILL.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/koyeb_skill/skills/koyeb/SKILL.md", "bytes": 10505, "tokens": 2440, "description": "Operate Koyeb serverless applications, services, deployments, sleep/wake lifecycles, domains, and secrets using the official Koyeb CLI and standard-library launcher.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "logto_management_skill", "source": "logto-management-skill:skills/skill.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/logto_management_skill/skills/skill.md", "bytes": 9555, "tokens": 2244, "description": "Safely discover, inspect, back up, and manage Logto tenant configuration through a JSON CLI and Python library.", "description_from": "frontmatter", "scan_hits": { "assigned_secret": [ "password=initial_password" ], "email": [ "alice@example.com" ] } }, { "name": "m5stack_sticks3", "source": "m5stack-sticks3-skill:skills/m5stack_sticks3.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/m5stack_sticks3_skill/skills/m5stack_sticks3.md", "bytes": 17283, "tokens": 6480, "description": "M5StickS3 开发技能: M5StickS3 和前代 StickC/Plus/Plus2 在硬件上有大量不兼容之处。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "m5stack_sticks3_esp_idf", "source": "m5stack-sticks3-skill:skills/m5stack_sticks3_esp_idf.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/m5stack_sticks3_skill/skills/m5stack_sticks3_esp_idf.md", "bytes": 8038, "tokens": 2712, "description": "M5StickS3 开发技能 — ESP-IDF 部分: 使用 ESP-IDF（非 Arduino）开发 StickS3 固件，尤其是裸驱 ST7789P3 LCD、ES8311 音频、BLE HID 和实时 WebSocket 语音路径。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "m5stack_sticks3_m5unified", "source": "m5stack-sticks3-skill:skills/m5stack_sticks3_m5unified.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/m5stack_sticks3_skill/skills/m5stack_sticks3_m5unified.md", "bytes": 19327, "tokens": 7300, "description": "M5StickS3 开发技能 — M5Unified / Arduino 部分: 使用 Arduino IDE / arduino-cli + M5Unified + M5GFX 库开发 StickS3 固件。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "m5stack_sticks3_test_loop", "source": "m5stack-sticks3-skill:skills/m5stack_sticks3_test_loop.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/m5stack_sticks3_skill/skills/m5stack_sticks3_test_loop.md", "bytes": 11506, "tokens": 3910, "description": "M5StickS3 开发技能 — 自主刷写与测试闭环: 做 StickS3 固件 bring-up 或持续迭代的 agent，无论使用 ESP-IDF 还是 Arduino/M5Unified 框架，均适用本文件。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "maven", "source": "maven-skill:skills/maven/SKILL.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/maven_skill/skills/maven/SKILL.md", "bytes": 9323, "tokens": 3082, "description": "Connect to an authenticated Maven browser session via CDP to observe courses, list cohorts, export validated Enrolled student CSV reports, read Lightning Lesson drafts and aggregate stats, read course reviews (public landing-page reviews and per-cohort post-course survey ratings), and list promo codes with their active/paused status or locate a code's pause toggle for a human, without writing.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "narwal", "source": "narwal-skill:skills/narwal/SKILL.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/narwal_skill/skills/narwal/SKILL.md", "bytes": 16890, "tokens": 4149, "description": "Read-only status inspection, floor plan maps, bounded live telemetry, and connection diagnostics for port-9002 WebSocket Narwal robot vacuums, plus an opt-in confirmed control mode (pause, resume, stop, dock, start/room clean). Flow 2 smoke tested; other compatible Flow/Freo models are candidates.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "bilingual_subtitles", "source": "online-media-skill:skills/bilingual_subtitles.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/online_media_skill/skills/bilingual_subtitles.md", "bytes": 9108, "tokens": 1829, "description": "Agent-Led Bilingual Subtitles: Produce bilingual subtitles whose language, sentence boundaries, terminology, coverage, and timing can be audited.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "download_and_transcribe", "source": "online-media-skill:skills/download_and_transcribe.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/online_media_skill/skills/download_and_transcribe.md", "bytes": 11199, "tokens": 2606, "description": "Download And Transcribe Skill: Acquire permitted online media and turn it into reusable local artifacts.", "description_from": "heading+first_sentence", "scan_hits": { "assigned_secret": [ "SECRET=\"current-client-secret" ] } }, { "name": "media_metadata", "source": "online-media-skill:skills/media_metadata.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/online_media_skill/skills/media_metadata.md", "bytes": 6518, "tokens": 1404, "description": "Media Metadata Skill: Make downloaded audio usable in a local music library by filling stable metadata: title, artist, album, album artist, track number, year, genre, comment, and cover art.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "medley_source_identification", "source": "online-media-skill:skills/medley_source_identification.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/online_media_skill/skills/medley_source_identification.md", "bytes": 3863, "tokens": 842, "description": "Medley Source Identification Skill: Produce a structured source-song list with timestamps, lyric evidence, supporting URLs, confidence, and review flags.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "music_library_dedup", "source": "online-media-skill:skills/music_library_dedup.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/online_media_skill/skills/music_library_dedup.md", "bytes": 5993, "tokens": 1241, "description": "Music Library Dedup Skill: Find duplicate songs across the user's local music libraries using embedded audio tags, choose a deterministic canonical copy per duplicate cluster, and stop for explicit human approval before any file is trashed.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "online_media", "source": "online-media-skill:skills/online_media.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/online_media_skill/skills/online_media.md", "bytes": 3572, "tokens": 716, "description": "Online Media Root Skill: Route an agent to the correct online media workflow.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "source_search", "source": "online-media-skill:skills/source_search.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/online_media_skill/skills/source_search.md", "bytes": 9686, "tokens": 2151, "description": "Source Search Skill: Find candidate media sources worth downloading or verifying.", "description_from": "heading+first_sentence", "scan_hits": { "assigned_secret": [ "SECRET=\"current-client-secret-from-private-notes-or-upstream-issue" ] } }, { "name": "skill_open_router_data_scraper", "source": "open_router_data_scraper:skills/skill_open_router_data_scraper.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/open_router_data_scraper/skills/skill_open_router_data_scraper.md", "bytes": 4309, "tokens": 1483, "description": "OpenRouter Data Scraper Skill: 通过逆向 OpenRouter 前端 stats 端点，按天/周粒度抓取模型活动数据，存入本地 SQLite 去重累积，突破 OpenRouter 31 天 trailing window 限制，长期构建 LLM 市场流量历史时间序列。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_opencode_agent_to_agent", "source": "opencode_skill:skills/skill_opencode_agent_to_agent.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/opencode_skill/skills/skill_opencode_agent_to_agent.md", "bytes": 20069, "tokens": 4387, "description": "OpenCode Agent-to-Agent Skill: Teaches discovery, identity, a plain-text envelope, and receipt classification so an AI agent can contact another session over the existing append path.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_opencode_data", "source": "opencode_skill:skills/skill_opencode_data.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/opencode_skill/skills/skill_opencode_data.md", "bytes": 5914, "tokens": 1275, "description": "OpenCode Data Skill: Use this skill when a user asks you to inspect local OpenCode SQLite data, archive sessions, compact a database, or query token/session data across main and archive databases.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_opencode_periodic_job", "source": "opencode_skill:skills/skill_opencode_periodic_job.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/opencode_skill/skills/skill_opencode_periodic_job.md", "bytes": 8132, "tokens": 1767, "description": "OpenCode Periodic Job Skill: Use this skill when a user asks to schedule a recurring OpenCode job, automate an OpenCode prompt on a daily/weekly/monthly cadence, test a cron-triggered OpenCode submission, or maintain a prompt file that cron submits through the OpenCode Skill CLI.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_opencode_submission", "source": "opencode_skill:skills/skill_opencode_submission.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/opencode_skill/skills/skill_opencode_submission.md", "bytes": 14965, "tokens": 3239, "description": "OpenCode Submission Skill: Use this skill when a user asks you to submit one prompt to OpenCode, append a follow-up prompt to an existing OpenCode session, run a template-driven batch of OpenCode sessions, or group QA work by slug.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_opencode_throughput", "source": "opencode_skill:skills/skill_opencode_throughput.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/opencode_skill/skills/skill_opencode_throughput.md", "bytes": 5760, "tokens": 1408, "description": "OpenCode Throughput Skill: Use this skill when a user asks you to measure how fast their local OpenCode setup actually generates tokens — per provider/model — from the SQLite data OpenCode already records.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "procedural-video-frames", "source": "opus-video-audio-skill:skills/procedural-video-frames/SKILL.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/opus_video_audio_skill/skills/procedural-video-frames/SKILL.md", "bytes": 21386, "tokens": 5120, "description": "Render short videos as numbered PNG frames from a Python script, then mux them with ffmpeg -- the frame-sequence pipeline, not a DAW and not a text-to-video model. This entry file holds the rules for every render (framing math in angles and the shots that are geometrically impossible, additive linear compositing with one tone map, exposure that must fall as the field of view widens, verification against stale frames and unlooked-at shots, contact sheets, previewing on a phone, ffmpeg assembly) and routes to sub-documents for look development with an independent critic, fine detail across a large zoom (SDFs, level of detail, anti-aliasing, growth), reflections on water, glows/highlights/seams/on-screen text, handing a material layer such as cloth to Blender through a coding agent, narrated explainers (timing the picture to a voice-over, checking TTS takes by transcription, subtitles, illustration plates printed onto the page), explainers that must not read as slides (sets on one sheet with a sliding camera, persistent elements, element-wise arrivals keyed to words, diagrams drawn in the plates' register), and films cut to an existing song (measuring and snapping beats and sung lines, drum-driven glow, one-canvas reveals, a vector-display look, nested zooms and video feedback, lyrics placed where the picture is empty). Use this skill whenever a task involves rendering or animating a short video, reel, short, greeting, explainer, music video or title sequence programmatically; whenever frames are produced by code (numpy, PIL, matplotlib, Blender scripting) and assembled into an mp4; whenever a camera has to push in, pan or reframe over time; whenever compositing a subject over a background with glow, halo, haze or a reflection; or whenever a render looks washed out, too dark, flat, cheap, aliased, or has visible seams and edges, or reads as a slideshow. Reach for it even when the animation sounds trivial (\"just zoom into this image over 10 seconds\"), because the failure modes here -- inverted exposure laws, off-frame geometry, stale-frame review -- are silent and cost whole render passes.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "video-scoring-audio", "source": "opus-video-audio-skill:skills/video-scoring-audio/SKILL.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/opus_video_audio_skill/skills/video-scoring-audio/SKILL.md", "bytes": 20361, "tokens": 5100, "description": "Compose, render and objectively verify short music cues and sound effects for video, programmatically and without a DAW. Covers the MIDI + fluidsynth + soundfont pipeline for music that has to hit specific timecodes, the Google Lyria generative-music API (endpoint shape, the copyright filter, and its verified inability to honor timing instructions), loudness normalization and tail trimming with ffmpeg, and the measurement techniques an agent must use because it cannot hear audio. Use this skill whenever a task involves scoring or adding music, a soundtrack, a background track, a sting, an SFX bed, or any generated audio to a video, animation, reel, short, or slideshow -- and also whenever audio needs to line up with a storyboard or specific timestamps, when choosing between a generative music API and synthesizing it yourself, when normalizing loudness or fixing clipping in a rendered cue, or when comparing several instrument or arrangement variants, or when laying a music bed and sound effects under a voice-over. Reach for it even if the request sounds simple (\"just add some music to this clip\"), because the timing, clipping and cannot-hear-it failure modes here are silent and ship broken output.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "skill_outlook", "source": "outlook_skill:skills/skill_outlook.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/outlook_skill/skills/skill_outlook.md", "bytes": 13860, "tokens": 3437, "description": "Outlook Skill — Agent Reference: All commands run from the project root.", "description_from": "heading+first_sentence", "scan_hits": { "email": [ "colleague@example.com" ] } }, { "name": "skill_playwright_test", "source": "playwright-test-skill:skills/skill_playwright_test.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/playwright_test_skill/skills/skill_playwright_test.md", "bytes": 9338, "tokens": 2079, "description": "Playwright E2E Test Skill: Help an AI agent turn an unknown or unstable browser flow into a reliable E2E test or a defensible diagnosis by observing the live page step by step before writing automation.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_session_keeper", "source": "playwright-test-skill:skills/skill_session_keeper.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/playwright_test_skill/skills/skill_session_keeper.md", "bytes": 8763, "tokens": 1990, "description": "Browser Session Credential Keeper Skill: Establish a self-healing credential chain for web apps that issue browser-bound session tokens: the browser profile maintains an active login, while a script captures fresh tokens from real network traffic, verifies them, and updates the `.env` copy used by background automation.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "copy_workflow", "source": "presentation_skill:skills/copy_workflow.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/presentation_skill/skills/copy_workflow.md", "bytes": 6312, "tokens": 1298, "description": "Copy Workflow: Writer Drafts, Builder Checks: Before you begin drafting any on-screen copy or writing speaker notes for an HTML (canvas) deck, take a few minutes to read through this workflow guide.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "critic_review", "source": "presentation_skill:skills/critic_review.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/presentation_skill/skills/critic_review.md", "bytes": 3375, "tokens": 794, "description": "Critic Review: Abstract goals like making a deck feel \"premium\" and \"clear\" simply cannot be verified by the agent that built it.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "generated_assets", "source": "presentation_skill:skills/generated_assets.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/presentation_skill/skills/generated_assets.md", "bytes": 5685, "tokens": 1171, "description": "Generated Assets in HTML Decks: Generated assets are local visual components that live inside a DOM-composed slide.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "html_decks", "source": "presentation_skill:skills/html_decks.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/presentation_skill/skills/html_decks.md", "bytes": 17248, "tokens": 4002, "description": "HTML Mode: the Canvas Deck: HTML mode generates the deck as a web page: **one continuous, animated sheet** without slide borders.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "reference", "source": "presentation_skill:skills/reference.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/presentation_skill/skills/reference.md", "bytes": 6416, "tokens": 1483, "description": "Presentation Skill — Reference: Detailed contracts and acceptance criteria.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_presentation", "source": "presentation_skill:skills/skill_presentation.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/presentation_skill/skills/skill_presentation.md", "bytes": 24283, "tokens": 5112, "description": "Creates presentation slide decks for AI agents. Defaults to image-generated full-slide visuals (outline_visual.md, visual_guideline.md, Reveal.js viewer). Also builds HTML canvas decks: one borderless animated sheet with a camera, written in HTML, CSS and SVG, elements drawn or split on each click, textless plates, and copy drafted by a writing model and fact-checked by the builder. Use for slide decks, keynotes, lectures, teaching decks, speaker notes, or presentation scaffolds.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "speaker_notes", "source": "presentation_skill:skills/speaker_notes.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/presentation_skill/skills/speaker_notes.md", "bytes": 13349, "tokens": 2924, "description": "Speaker Notes — Spoken-Delivery Contract: Read this file when creating, expanding, or substantially rewriting speaker notes.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_resend_email", "source": "resend_email_skill:skills/skill_resend_email.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/resend_email_skill/skills/skill_resend_email.md", "bytes": 8374, "tokens": 1923, "description": "Resend Email Skill: Use Resend for agent-controlled email sending, received email handling, and team-wide suppression inspection and management.", "description_from": "heading+first_sentence", "scan_hits": { "assigned_secret": [ "API_KEY=replace-with-your-resend-api-key" ], "email": [ "anything@example.resend.app", "no-reply@example.com", "user@example.com" ] } }, { "name": "skill_suppression_analysis", "source": "resend_email_skill:skills/skill_suppression_analysis.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/resend_email_skill/skills/skill_suppression_analysis.md", "bytes": 13370, "tokens": 2903, "description": "Analyze Resend suppression lists, calculate deliverability metrics and bounce rates, classify bounces and spam complaints, trace originating emails via source IDs, mine post-suppression attempts, discover typo-rescue pairs, and generate actionable operator reports. Load when the user asks for suppression, bounce, complaint, or deliverability analysis.", "description_from": "frontmatter", "scan_hits": { "email": [ "user@example.com" ] } }, { "name": "skill_semantic_search", "source": "semantic-search-skill:skills/skill_semantic_search.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/semantic_search_skill/skills/skill_semantic_search.md", "bytes": 7534, "tokens": 1589, "description": "Indexes local text files with embeddings and searches them by natural-language query through the semantic-search CLI.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "smart_home_skill", "source": "smart_home_skill:skills/smart_home_skill.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/smart_home/skills/smart_home_skill.md", "bytes": 4434, "tokens": 930, "description": "Smart Home Skill: Operate a local Smart Home Skill service safely through its live OpenAPI schema and private household overlay.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "stripe_skill", "source": "stripe-skill:skills/stripe_skill.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/stripe_skill/skills/stripe_skill.md", "bytes": 6160, "tokens": 1263, "description": "Stripe Skill - Agent Reference: Use this skill when a user wants a read-only Stripe finance or sales analysis through the local `stripe_skill` repository.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_tavily", "source": "tavily-skill:skills/skill_tavily.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/tavily_skill/skills/skill_tavily.md", "bytes": 20257, "tokens": 4979, "description": "Runs Tavily-backed web search and URL extraction via python -m tavily_skill with stable JSON envelopes. Use when agents need terminal Tavily access, reproducible defaults, or file-oriented payloads outside MCP.", "description_from": "frontmatter", "scan_hits": {} }, { "name": "tts_clone", "source": "tts-clone-skill:skills/tts_clone.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/tts_clone_skill/skills/tts_clone.md", "bytes": 8572, "tokens": 1979, "description": "TTS Clone: Synthesize a new spoken line in an authorized adult speaker's voice using either Gemini 3.8 Flash TTS voice replication or a local Qwen3-TTS clone, keeping secrets off stdout and ensuring private audio is never stored in the repository.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_typefully_twitter", "source": "typefully-twitter-skill:skills/skill_typefully_twitter.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/typefully_twitter_skill/skills/skill_typefully_twitter.md", "bytes": 10009, "tokens": 2225, "description": "Typefully Twitter Skill — Agent Reference: Use this skill when the user asks an agent to work with X/Twitter through Typefully or analytics.x.com:", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "skill_unifi", "source": "unifi-skill:skills/skill_unifi.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/unifi_skill/skills/skill_unifi.md", "bytes": 3275, "tokens": 846, "description": "Skill: unifi-skill — read-only UniFi controller observation: Repo: `https://github.com/grapeot/unifi-skill` (master).", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "voice-lora-detect", "source": "voice-lora:skills/voice-lora-detect/SKILL.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/voice_lora/skills/voice-lora-detect/SKILL.md", "bytes": 9485, "tokens": 3734, "description": "评估中文 markdown 文章的 AI 味浓度，或判断文章像不像鸭哥（yage.ai）写的。当用户问“看看这篇文章 AI 味有多浓”“哪几段最像 AI 写的”“像不像 AI 写的”或“像不像鸭哥写的”时使用。运行分类器给出文章级概率、分档、AI 腔词率和分数最低的段落。", "description_from": "frontmatter", "scan_hits": {} }, { "name": "voice-lora-rewrite", "source": "voice-lora:skills/voice-lora-rewrite/SKILL.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/voice_lora/skills/voice-lora-rewrite/SKILL.md", "bytes": 18656, "tokens": 6877, "description": "用训练好的 voice-lora 模型（例如已发布的鸭哥文风模型 ai_smell_remover），把一篇 AI 起草的中文 markdown 文章逐段改成作者的文风，并产出供人审阅的对照页。触发说法包括“voice rewrite”“voice lora 改写”“去 AI 味”“改成作者的文风”。覆盖起服务、调用、采样参数，以及改完之后必须做的事实漂移核查。模型输出不能直接当最终稿。训练和复现模型见 skills/voice-lora/SKILL.md。", "description_from": "frontmatter", "scan_hits": {} }, { "name": "voice-lora", "source": "voice-lora:skills/voice-lora/SKILL.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/voice_lora/skills/voice-lora/SKILL.md", "bytes": 14250, "tokens": 5230, "description": "用一位作者自己写的中文文章，训练一个把 AI 写的中文改成这位作者文风的 LoRA，并用盲评判断它像不像。覆盖造数据、训练、选 checkpoint、评估、部署和多轮调优。", "description_from": "frontmatter", "scan_hits": {} }, { "name": "bestpractice_external_prose", "source": "writing-skill:skills/bestpractice_external_prose.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/writing_skill/skills/bestpractice_external_prose.md", "bytes": 16202, "tokens": 6274, "description": "外部中文 Prose 诊断词汇表（Manager Reference）: `workflow_external_writing.md` 的教训是：把同一条反-textbook 规则写进九个地方、再让模型自述\"我扫过了没问题\"，规则不会 bind——模型看得见症状（无提示冷读能读出\"低亲密度、克制\"），但自述式的 verdict 从不把症状转成阻断。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "external_prose_lint", "source": "writing-skill:skills/external_prose_lint.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/writing_skill/skills/external_prose_lint.md", "bytes": 6014, "tokens": 2159, "description": "External Prose Lint CLI: 对 external-facing 中文 Markdown 跑确定性文风扫描：数破折号、引号、括号补译、单句段、链接等，每条 finding 附上 skill 原文要求变成「请你判断」的问题。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "reference_writing_thesis_catalog", "source": 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"外部写作与成文工作流（Operational Spine）: 这是操作主干。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "workflow_internal_writing", "source": "writing-skill:skills/workflow_internal_writing.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/writing_skill/skills/workflow_internal_writing.md", "bytes": 15011, "tokens": 5629, "description": "内部写作工作流: 输出对象已共享项目上下文，目标是帮对方快速形成判断、复查证据、决定下一步。", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "workflow_news_aggregation", "source": "writing-skill:skills/workflow_news_aggregation.md", "path": "/Users/grapeot/co/knowledge_working/adhoc_jobs/writing_skill/skills/workflow_news_aggregation.md", "bytes": 11201, "tokens": 2319, "description": "Skill: News Aggregation Writing: A three-operation pipeline for recurring news-briefing production.", "description_from": "heading+first_sentence", "scan_hits": {} }, { "name": "writing_workflows", "source": "writing-skill:skills/writing_workflows.md", "path": 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