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概要と使いどころ
Design a mastery experience sequence that systematically builds student confidence in a skill they avoid. Use when students say 'I can't do this', avoid tasks, or show learned helplessness.
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 a variation theory task using contrast, separation, and fusion to teach critical features of a concept. Use when students confuse similar concepts or fail to discern key distinctions.
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。
Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.
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。
When you or another skill needs to fetch the content of a social media post by URL — tweet, X thread, LinkedIn post, Instagram post, TikTok video, Bluesky post, Reddit thread, Mastodon status, Threads post, Hacker News thread. Returns normalized structured data (author, posted_at, text, engagement counts, media URLs, replies if requested) regardless of platform. Tries strategies in order: direct API (Bluesky, Mastodon, HN, Reddit), agent-browser with modal dismissal (LinkedIn, X preview), Wayback Machine (older posts), paid APIs (ScrapeCreators / Apify — only if env keys present). Triggers on "/social-fetch <url>," "fetch this tweet," "fetch this post," "what does this LinkedIn say," "read this thread," "pull this post." Used by deep-research (citing specific posts), jab-hook (inspiration account analysis), business-brainstorm (competitor / operator commentary).
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。
Monthly CFO workflow for a company or agency — pull raw data from bank + payment processor + payroll + expense management, categorize and reconcile, compute end-of-month cash via transaction-sum method, update a scenario projector for forward forecasting, write the monthly snapshot report, surface decisions to leadership. Modes — monthly (default; the standing report), weekly (thin cash pulse), scenario (ad-hoc modeling in the projector), pickup (resume where the prior run left off). Anonymized team-scope sibling to personal-cfo (which handles personal household finances). Composes with company-brain (report gets stored + wiki-indexed there), toolify (wire company-specific data sources), loopify (schedule the monthly + weekly runs). Triggers on "/company-cfo," "/cfo," "monthly cash report," "do the CFO snapshot," "CFO monthly," "let's run CFO," "cash projection," "runway forecast," "monthly financials," "cash pulse."
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。
When you paste raw human input — a call transcript (Grain, Zoom, Granola, Fathom), a text or email from a client/partner/friend, a voice-memo dump, or meeting notes — and want it converted into structured work. Extracts decisions, action items (yours vs theirs), bugs/feature requests, and facts worth keeping; files GitHub issues in the right repo, captures to the second-brain vault, and drafts (never sends) the reply. Triggers on "/ingest", "ingest this", "here's my call with X," "here's the transcript," "this is from [person]," "[person] asked me this," "from [person]:", a pasted transcript with speaker labels, or a forwarded client message that clearly expects processing. Person→project routing lives in a private config; unknown senders get asked about once, then remembered.
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。
When a solution seems impossible and the path forward is blocked. Refuses to take no for an answer: classifies what kind of "no" you hit, then runs lateral-thinking techniques (assumption autopsy, inversion, first principles, constraint toggling, analogical transfer, and more) until the wall cracks or is proven load-bearing. Generates at least 10 angles before evaluating any. AGENTS: use this on yourself when an API doesn't support what you need, an approach has failed twice, or a "that's not possible" sentence is forming — run the fast path BEFORE reporting a dead end. Archives walls to ~/.config/makerskills/unstuck/archive/. Sits upstream of decide. Triggers on "/unstuck," "I'm stuck," "this seems impossible," "we hit a wall," "there's no way to," "they said no," "dead end," "out of options," "I've tried everything," "work around this."
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。
When you want to extract content from a video — YouTube, Loom, Vimeo, Riverside, Zoom recording, local MP4, X/IG video, anything yt-dlp supports. Three depth modes user picks per invocation — transcript (just words, fast/free), visual (transcript + ffmpeg frame extraction + Claude vision pass on key moments), multimodal (Gemini native video ingestion if $GEMINI_API_KEY set, else dense Claude vision). Uses local Whisper for transcription (MLX-Whisper on Apple Silicon, faster-whisper elsewhere), falls back to platform-provided transcripts when available (Loom, Riverside, YouTube auto-subs). Saves to ~/Documents/videos/<source>-<slug>-<date>/ and optionally captures summary to second-brain raw/ as call-/meeting-/note-. Triggers on "/watch-video <url>," "watch this video," "transcribe this loom," "analyze this video," "summarize this recording," "key moments from this," "what happened in this video." This skill replaces and broadens the prior youtube-transcript skill.
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。
Expert blueprint for procedural content generation (dungeons, terrain, loot, levels) using FastNoiseLite, random walks, BSP trees, Wave Function Collapse, and seeded randomization. Use when creating roguelikes, sandbox games, or dynamic content. Keywords procedural, generation, FastNoiseLite, Perlin noise, BSP, drunkard walk, Wave Function Collapse, seeding.
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。
Expert patterns for Godot 3D physics (Jolt/PhysX), including Ragdolls, PhysicalBones, Joint3D constraints, RayCasting optimizations, and collision layers. Use for rigid body simulations, character physics, or complex interactions. Trigger keywords: RigidBody3D, PhysicalBone3D, Jolt, Ragdoll, Skeleton3D, Joint3D, PinJoint3D, HingeJoint3D, Generic6DOFJoint3D, RayCast3D, PhysicsDirectSpaceState3D.
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。
Consolidated expert library for professional Godot 4.7+ game and application development. Orchestrates 92 Domain Skills through architectural workflows, anti-pattern catalogs, performance budgets, and Server API patterns. Use when: (1) starting a new Godot project, (2) designing game or app architecture, (3) building entity/component systems, (4) debugging performance or physics issues, (5) choosing between 2D/3D approaches, (6) implementing multiplayer, (7) optimizing draw calls or script time, (8) porting between platforms, (9) migrating from 4.6 to 4.7. Primary entry point for ALL Godot development tasks. Keywords: Godot 4.7, AreaLight3D, HDR, Asset Store, godot-master.
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。
Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.
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。
Bevy ECS hub for Bevy 0.20+. Use when starting a Bevy game, choosing ECS boundaries, or picking a Bevy skill. Keywords: Bevy, ECS, bevy-master, schedules, plugins.
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。
Expert blueprint for procedural content generation (dungeons, terrain, loot, levels) using FastNoiseLite, random walks, BSP trees, Wave Function Collapse, and seeded randomization. Use when creating roguelikes, sandbox games, or dynamic content. Keywords procedural, generation, FastNoiseLite, Perlin noise, BSP, drunkard walk, Wave Function Collapse, seeding.
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。
Expert patterns for Godot 3D physics (Jolt/PhysX), including Ragdolls, PhysicalBones, Joint3D constraints, RayCasting optimizations, and collision layers. Use for rigid body simulations, character physics, or complex interactions. Trigger keywords: RigidBody3D, PhysicalBone3D, Jolt, Ragdoll, Skeleton3D, Joint3D, PinJoint3D, HingeJoint3D, Generic6DOFJoint3D, RayCast3D, PhysicsDirectSpaceState3D.
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。
Consolidated expert library for professional Godot 4.7+ game and application development. Orchestrates 92 Domain Skills through architectural workflows, anti-pattern catalogs, performance budgets, and Server API patterns. Use when: (1) starting a new Godot project, (2) designing game or app architecture, (3) building entity/component systems, (4) debugging performance or physics issues, (5) choosing between 2D/3D approaches, (6) implementing multiplayer, (7) optimizing draw calls or script time, (8) porting between platforms, (9) migrating from 4.6 to 4.7. Primary entry point for ALL Godot development tasks. Keywords: Godot 4.7, AreaLight3D, HDR, Asset Store, godot-master.
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。
Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.
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。
Bevy ECS hub for Bevy 0.20+. Use when starting a Bevy game, choosing ECS boundaries, or picking a Bevy skill. Keywords: Bevy, ECS, bevy-master, schedules, plugins.
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。
Bevy ECS skill for spatial queries. Keywords: Bevy, raycast, shapecast, picking, physics query.
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。
Audit a project's infrastructure and SaaS stack, then produce a cost report showing what the user currently pays and what they would save by consolidating onto Temps (self-hosted or Temps Cloud). Detects hosting platforms (Vercel, Netlify, Railway, Render, Heroku, Fly.io), analytics (PostHog, Plausible, Mixpanel, Amplitude, Fathom), error tracking (Sentry, Bugsnag, Rollbar, Honeybadger), session replay (LogRocket, FullStory, Hotjar, Highlight), uptime monitoring (Pingdom, UptimeRobot, Better Stack, Checkly), managed databases (Supabase, Neon, PlanetScale, MongoDB Atlas, Upstash, RDS), and transactional email (SendGrid, Postmark, Resend, Mailgun) from dependencies, config files, and env var names. Use when the user wants to: (1) Know how much they would save by switching to Temps, (2) Audit their SaaS/infrastructure spend, (3) Compare their current stack's cost against self-hosting, (4) Decide whether Temps is worth it, (5) Build a business case for consolidating tools. Triggers: "how much would I save", "temps savings", "cost comparison", "audit my stack", "am I overpaying", "saas spend", "calculate savings", "is temps cheaper".
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。
TDD and testing skills for iOS/macOS apps. Covers characterization tests, TDD workflows, test contracts, snapshot tests, test infrastructure, and deterministic quality gates (fitness functions, coverage ratchet, mutation testing). Use for test-driven development, adding tests to existing code, or building test infrastructure.
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。
mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of g
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。
This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, an
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。
MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis. It uses scoring matrices to be used in these pairwise distance calcualtions which can be adapted to any scoring for DNA or AA characters. E.g. by using literal distances MSA2dist calculates pairwise IUPAC distances.
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。