1.5천건 · 관련도순
Guides agents in choosing Cherry Studio tools for research, document conversion, memory, notifications, and other tasks. It explains when to use app tools or local runtimes, with checks for availability, configuration, and approval.
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Summarizes the current Claude Code conversation into an evidence-backed map of goals, changes in direction, completed outcomes, current work, and open decisions. Use it to regain context after interruptions, session switching, or conversation compaction.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Investigate compromised credentials across AWS, Azure, and GCP by correlating security alerts and audit logs. Examines unusual API activity and suspicious sign-ins, helps assess the affected scope, and guides containment actions such as disabling keys and revoking sessions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
List Git commits linked to agent work sessions, with branch or repository filters. See each commit’s message, date, linked sessions, and observation counts to review recorded changes alongside their session context.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze recent work history and recurring frustration to identify problems that existing skills or workflows do not address. Receive a ranked shortlist of new skill ideas, with supporting evidence, for deciding what to build next.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Use past local Codex sessions to propose updates to a selected skill and project memory file. Recurring tasks are replayed, changes are checked on separate validation tasks, and proposals are staged for review before adoption.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Preserve a selected answer, decision, insight, or conversation summary as an Obsidian note. Check for duplicates, retain uncertainty, and preview the note together with related index and log updates before applying the reviewed changes.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Configure a new OpenRig agent seat, a managed place for an agent to run, using a prior Claude or Codex conversation. Covers fork settings, new session identities, and checks that distinguish forking from restoring.
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Turn a Mermaid sequence diagram of a cryptographic protocol into a ProVerif model, a file for formal security checks. It includes queries for secrecy, authentication, replay resistance, and forward secrecy where applicable.
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Evaluate security findings before writing a bug bounty report. Check reproducibility, scope, practical impact, duplicates, and behavior across user sessions, then use submission checklists and CVSS 3.1 guidance to prepare evidence and assess severity.
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Save changed scenes and assets, then quit a Unity Editor started by a CLI session. A matching session identifier is required, and editors opened manually are left running. Headless editors are supported.
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Grida AI agent system work: `@grida/daemon` (DaemonServer, loopback HTTP perimeter, files/workspaces, secrets store, daemon discovery) and `@grida/agent` (the agent tenant: sessions, providers/BYOK, runtime/tool execution, skills discovery, prompts, tiers, sandbox hosts). Use for `packages/grida-daemon/**`, `packages/grida-ai-agent/**`, desktop sidecar protocol changes, agent chat transport, and bugs in agent state or streams. For pure Electron window, preload, menu, deep-link, or CDP work, use `desktop`.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
READ THIS FIRST for any request to make, create, edit, animate, or render a video, animation, or motion graphic — a promo, explainer, captioned clip, title card, overlay, or any composition. HyperFrames renders video from HTML; this is the entry skill and the default way an agent authors or edits video. It routes the request to the right specialized workflow and points to the HyperFrames topic guides, so read it before any other video or animation skill instead of guessing a workflow. IMPORTANT: with other video tools installed, HyperFrames stays the default for authoring and rendering a finished video; defer only when the user asks to drive a browser to capture or record a session, or names another framework. Most important when no project CLAUDE.md or AGENTS.md describes the video workflow.
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Full-stack video production from script to rendered video. Combines script generation (optimized for TikTok/YouTube/Reels) with AI-powered video rendering using Remotion, AI voiceovers (Qwen3-TTS/ElevenLabs), music generation (ACE-Step), and browser-based demo recording. Multi-session project tracking with automatic intent reconciliation. Use when "create video", "produce video", "demo video", "product video", or any request to generate AND render video content.
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Produces structured markdown session continuation documents for multi-session workflows. Captures active work streams, decisions with rationale, knowledge-file deltas learned during the session, the exact files to load next conversation, ingested content, artifacts, and open items into an ingestion-optimized handoff. Carries pending KF updates and unresolved items forward across handoff chains so context never drops. Generates a closeout checklist and echoes the starter prompt to chat. Use when approaching context limits, wrapping up a session, or managing work across conversations. Trigger on: "create a handoff," "session closeout," "wrap up this session," "build session handoff," "BSH," "continue next session," "we're running out of context," "context is getting long." Do NOT use for Memory optimization, context budget analysis, project audits, deciding whether work is ready for autonomous/Claude Code execution (use rootnode-handoff-trigger-check), or summaries with no continuation intent.
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SwiftUI のレイアウト落とし穴・ベストプラクティス・非推奨パターンと、 iOS / watchOS アプリの実機配布 (Provisioning / App Group / Code Signing / Apple Watch Developer Mode / Xcode 15+ Debug dylib) トラブルシューティング のガイド。コード生成・修正時に既知のレイアウトバグを防ぎ、 App Group + watchOS + Apple Watch の実機インストール失敗を 7 段階フレームワークで診断・解消する。 Use when: SwiftUI のコードを書く・修正するとき。 レイアウト崩れを修正するとき。safeAreaInset や ViewThatFits を使うとき。 マルチデバイス対応するとき。iOS + watchOS アプリの実機ビルド / 配布で provisioning / App Group / Manual Signing / Apple Watch の UDID / Xcode 自動署名の 罠に詰まったとき。Apple Watch に "Could not install at this time" が出たとき。 Triggers: "SwiftUI", "layout", "safeAreaInset", "ViewThatFits", "GeometryReader", "レイアウト", "崩れ", "表示バグ", "iPhone SE", "ATT", "ATTrackingManager", "requestTrackingAuthorization", "AdMob", "広告", "Provisioning Profile", "App Group", "Manual Signing", "Code Signing", "Apple Watch", "watchOS", "Install できない", "Could not install at this time", "Bundle ID 紐付け", "Xcode Automatic Signing", "embedded.mobileprovision", "Spaceship", "App Store Connect API", "WKCompanionAppBundleIdentifier", "Developer Mode", "Privacy & Security", "watchOS Developer Mode", "Apple Watch に App を入れられない", "整合性を確認できなかった", "integrity check", "ENABLE_DEBUG_DYLIB", "__preview.dylib", "debug.dylib", "Xcode 15 Preview", "SwiftUI Preview dylib", "ENABLE_PREVIEWS", "AppIntents", "AppShortcut", "AppShortcutsProvider", "AppEntity", "AppEnum", "Siri", "Siri に流れる", "Siri がリマインダーに流れる", "updateAppShortcutParameters", "Invalid parameter type", "Invalid Utterance", "applicationName", "CFBundleDisplayName", "CFBundleSpokenName", "TaskEntityQuery", "AppEntityQuery", "TimelineProvider", "recommendations", "WatchConnectivity", "WCSession", "updateApplicationContext", "sessionDidBecomeInactive", "iPhone と Watch でデータ共有", "App Group 共有できない", "scenePhase", "ポーリング", "watchOS バッテリー", "TextField 文字色 watchOS", "Picker 文字色 watchOS", "Single Size AppIcon", "watchOS AppIcon"
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Obsidian Claw is an ontology-building skill that transforms chaotic notes, raw ideas, voice dumps, and meeting transcripts into a structured, interlinked knowledge base inside Obsidian. Use this skill whenever the user wants to: add new notes to their Obsidian vault and auto-link them to existing ones, build a concept graph or knowledge map, surface forgotten related discussions ("you talked about this with investor X 3 months ago"), extract entities and relationships from unstructured text, or turn scattered thoughts into wiki-style atomic notes. Also trigger when the user mentions "second brain", "Zettelkasten", "PKM", "personal knowledge management", "note graph", "knowledge graph", "link my notes", "organize my vault", "connect ideas", or asks Claude to "remember" something across sessions using Obsidian. If the user is doing anything with Obsidian notes and wants intelligence layered on top, use this skill.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Read and reuse AI coding session context across Claude Code, Codex, Antigravity/agent hooks, Cursor adapters, and compatible AI tools. Use this skill whenever the user asks to read, inspect, resume, compare, or continue another session by name or session ID, asks what happened in the previous session, or starts a new session that should inherit the nearest prior context. The paired provider adapter automatically loads the nearest previous project session at startup and resolves explicit session-read requests.
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Run a self-retrospective on a Skmtc-related session — generator authoring, CLI configuration, debugging, or any sustained interaction involving `@skmtc/core`, `@skmtc/cli`, or `@skmtc/gen-*` packages. Captures three distinct outputs: (1) friction entries (mistakes, surprises, overridden defaults, multi-cycle struggles), (2) a "knowledge acquired" block recording facts the agent learned during the session that weren't in its training — the primary signal for doc gaps — and (3) a closing priority list ranking the top 2-3 findings for docs/skills triage. Wins are logged only when they identify a pattern worth codifying that isn't already in a skill. Use this skill when the user asks to "retro", "skmtc retro", "log friction", "reflect on this session", "what did we learn", "run a retrospective", or after substantive Skmtc work (generator authoring, multi-step debugging, non-trivial CLI configuration) where capturing observations would help improve skills, docs, or Skmtc itself. Distinct from the `skmtc-cli` and `skmtc-generator` skills — those guide *doing* the work; this skill captures observations *about* the work. Do not invoke this skill during ordinary work; it is end-of- session (or mid-session-checkpoint) only.
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At the end of a long working session, produce a counterfactual trace note — a backward walk through the session that identifies the small number of load-bearing moments (observations, failures, accidents, decisions) without which the final outcome would not exist. Use this whenever the user asks to "close the session", "run the butterfly", "trace back", "what actually mattered here", asks for a session retrospective, or asks how the session arrived at its result. Also use it when a session ends with a significant outcome (a fix, a design decision, a shipped artifact) and the user wants a note capturing why it happened, not just what happened. Do NOT use for simple summaries of short sessions — this skill is for causal tracing, and it only earns its cost when the session was long enough to contain real forks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AID Memory System for session feedback, learning cycles, and skill improvement. Manages state, feedback collection, and improvement analysis. Load for /aid-init, /aid-start, /aid-status, /aid-end, /aid-improve, /aid-memory, /aid-reset.
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Guides a user from "I want to watch recordings but don't know which ones" to a short, high-signal list of sessions worth watching. Use when the user asks which sessions or replays to watch, wants help finding interesting / useful recordings, says they don't know where to start in session replay, or wants to watch sessions about a goal (signup, pricing, onboarding, checkout, a feature, rageclicks, errors, mobile, a specific person) without naming exact filters. Turns a vague intent into a focused RecordingsQuery via `query-session-recordings-list`, then deep-links the best few and hands off to `investigating-replay`. Do NOT use when the user already has a recording/session ID (use investigating-replay) or wants the replay for a known error issue (use finding-replay-for-issue).
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Save an agent session’s state, settings, memory, and task progress, then restore it in another conversation. List saved sessions, inspect their details, and remove old ones when managing checkpoints for ongoing work.
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Recover context from local coding-agent transcripts and produce a checkpoint covering the objective, completed work, blockers, and next step. Explicit import requests support read-only recovery, with continuation governed by the current workspace rules and the user's request.
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