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概要と使いどころ
A continuously adaptive skill suite that empowers Clawdbot to act as a versatile coder, business analyst, project manager, web developer, data analyst, and NAS metadata scraper. It intelligently discovers free resources, adapts to user context, and ensures reliable, proven guidance across multiple domains.
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。
Schedule and manage social media posts across Instagram, X (Twitter), Bluesky, TikTok, Threads, LinkedIn, Facebook, Pinterest, and YouTube using the AdaptlyPost API. Use when the user wants to schedule social media posts, manage social media content, upload media for social posting, list connected social accounts, check post status, cross-post content to multiple platforms, or automate their social media workflow. AdaptlyPost is a SaaS tool — no self-hosting required.
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。
Native video analysis using Google Gemini API. Upload and analyze video files — describe scenes, extract text/UI, answer questions about content, transcribe speech, identify objects and actions. Use when: (1) User sends a video file and wants it analyzed, (2) Video summarization or description needed, (3) Extracting text, UI elements, or information from screen recordings, (4) Answering questions about video content, (5) Comparing multiple videos, (6) Analyzing tutorials, demos, or walkthroughs.
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。
AI-powered presentation generation using 2slides API. Create slides from text content, match reference image styles, or summarize documents into presentations. Use when users request to "create a presentation", "make slides", "generate a deck", "create slides from this content/document/image", or any presentation creation task. Supports theme selection, multiple languages, and both synchronous and asynchronous generation modes.
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。
Synthesizes multiple case summaries into one unified Case Summary Report. Triggers when the user has two or more case summaries, document analyses, or memoranda and needs them consolidated into a single report with patterns, contradictions, and gaps identified.
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。
Extracts structured data from large sets of legal documents into tabular format for review, analysis, and reporting. Processes contracts, agreements, correspondence, filings, and other legal documents in bulk — extracting key terms, dates, parties, obligations, risks, and custom fields into organized tables. Use when conducting due diligence document review, bulk contract extraction, compliance audits across document sets, lease portfolio analysis, employment agreement review, regulatory filing review, or any task requiring structured extraction from multiple documents. Trigger keywords: tabular review, bulk extraction, document review table, data room review, batch document analysis, contract extraction, portfolio review, structured extraction, document comparison table.
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。
Manages multi-organization appellate amicus briefs with single-pen drafting, coalition sign-offs, conflict triage, and compliant disclosures. Use when coordinating a coalition amicus brief, managing amicus sign-on, handling FRAP 29 or Supreme Court Rule 37.6 disclosures, or preparing cover and interest sections for multiple amici.
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。
Manages end-to-end workflow for multi-organization amicus coalition briefs in appellate courts. Covers single-pen drafting governance, position alignment, conflict resolution, documented sign-offs, FRAP 29/Rule 37.6 disclosures, FRAP 26.1 corporate disclosures, cover formatting for multiple amici, and compliant filing. Use when coordinating coalition amicus briefs, managing sign-offs, drafting joint friend-of-court briefs, handling multi-amici filing, or resolving inter-organization conflicts. Triggers on phrases like "coalition brief," "12 orgs joining this amicus," or "coordinate the amicus."
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。
Breaks work into ordered, parallel-dispatchable tasks with an execution DAG. Output format is consumable by CI matrices and parallel agent runners — each task has a stable ID, declared file writes, conflict edges, and branch suffix. Use when you have a spec, brief, or shaper output and need to decompose it into implementable units. Use when a task feels too large, when scope spans multi-repo or multi-week work, or when parallel execution across multiple agents is on the table.
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。
Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".
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。
Download YouTube videos with customizable quality and format options. Use this skill when the user asks to download, save, or grab YouTube videos. Supports various quality settings (best, 1080p, 720p, 480p, 360p), multiple formats (mp4, webm, mkv), and audio-only downloads as MP3.
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。
Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.
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。
Generate creative domain name ideas for your project and check availability across multiple TLDs (.com, .io, .dev, .ai, .app). Find the perfect domain name without manual checking.
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。
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, or when you need to organize work across multiple parallel streams.
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。
Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
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。
Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
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。
Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch.
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。
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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。
Design desktop window systems that feel native on macOS and Windows: split views, inspectors, auxiliary windows, tiling, snap-safe sizes, default placement, and chrome density. Use when a Tauri/Electron/WinUI/SwiftUI app feels awkward, overlapping, over-chromed, or generic, or when you need a real geometry contract for multiple surfaces. NOT for mobile layouts, marketing pages, or CSS-only component styling without multi-window or multi-pane behavior.
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。
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
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。
Plan dependency-aware atomic commits for changes that span multiple logical concerns. Use when a refactor, feature, or API change would benefit from reviewable, independently meaningful commit boundaries. To triage an already-dirty working tree and propose commits for existing uncommitted changes, use `planning-commits` instead.
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。
Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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。
Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.
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。
Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.
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。