データ・AIのスキル

When you want to clean and reformat content (usually from your terminal) for pasting into Slack, Notion, Twitter/X, LinkedIn, email, GitHub, or plain text. Strips ANSI codes, box-drawing chars, terminal prompt artifacts, and applies destination-specific formatting. Default input is the system clipboard; default output is both the clipboard and a chat preview. Triggers on "/paste", "/paste [destination]", "clean this for X," "format for slack/notion/twitter/linkedin/email/github," "render as markdown," "paste-ready," "strip formatting," "make this copy-pastable." Default destination is plain. Also scans for secrets (API keys, tokens, .env values) and warns before copying anything sensitive.

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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).

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When you want to capture into, compile, query, lint, or connect your personal Second Brain. Wraps the Karpathy LLM Wiki schema (Obsidian or any markdown vault) — raw/ (unprocessed sources), wiki/ (AI-compiled interlinked topic pages), outputs/ (generated artifacts). Tool-agnostic in design but defaults to a vault at ${SECOND_BRAIN_VAULT:-$HOME/Documents/SecondBrain}/. Six modes — capture (drop something into raw/), compile (process unprocessed raw files into wiki pages, update INDEX.md), query (answer a question from the wiki, save to outputs/), lint (orphans / contradictions / stale / unprocessed raw / topic gaps), connect (suggest new wikilinks between pages), search (quick lookup). Triggers on "/second-brain," "/sb," "capture this," "save this to my brain," "compile the wiki," "process raw notes," "query my wiki," "ask my brain," "lint the wiki," "find connections," "search my notes." Complements deep-research (external corpus) — this is the internal corpus.

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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."

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Use when searching, installing, or improving AI skills and prompts via prompts.chat or skills.sh. Triggers on skill search, prompt lookup, install skill, improve prompt, prompts.chat.

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Session relay — compresses what's been done so far (writes a one-liner for /compact) and generates a "next task" prompt (a baton) for the user to hand off. Use on requests like "relay / hand off / next session / continue after compact / baton / handoff / clean up context and continue next task." Does not run /compact itself (a skill cannot do that) — it produces the one-liner the user pastes in.

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Expert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.

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Turn a one-line description of a repetitive task into a reusable, self-guarding slash command. loop-forge diagnoses the task into one of 5 loop shapes (Batch / Pipeline / Refine / Watch / Explore), interviews for the blanks, and auto-injects two safety devices the user didn't know they needed — an independent verifier (maker ≠ checker) and a hardstop (a budget/count/cooldown ceiling) — then previews the result and stamps it as a `/command` they can run forever. Use when the user says "/loop", "/loop-forge", "/make-it-loop", "automate this", "make this repeatable", "turn this into a command", "do this for all 100 items", "do X every time Y happens", "generate several and pick the best", or otherwise wants to capture a recurring task as a reusable slash command instead of re-typing the prompt by hand. Works in any language: it interviews the user and writes the stamped command in the user's own language. Non-goal — it does not schedule unattended runs (launchd/cron) or publish externally on its own; it stamps the reusable command and stops there.

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Use *before* starting work in a domain you don't know well, to surface the "unknown unknowns" — the things you don't even know to ask about — and learn just enough to prompt and decide well. Implements the "blind spot pass" pattern from Anthropic's Fable "finding your unknowns" field guide. Triggers when you say "I'm new to this", "I don't know what to ask", "teach me before we start", "blind spot pass", "unknown unknowns", "find my unknowns", or the Korean "내가 뭘 모르는지 알려줘" / "이 분야 처음인데" / "먼저 가르쳐줘" / "블라인드 스팟", or when you hand off a task while admitting you're a non-expert in that field (color grading, video editing, legal, tax, finance, design, an unfamiliar codebase, etc.). Do NOT use for simple factual questions, domains you already know, or directly-actionable work — answer those directly or route to planner / architect.

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Use when cutting a GodotPrompter release or bumping its version — the version-bump sequence, tag-triggered workflow, and the marketplace manifests that must follow.

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。

Bootstrap skill — establishes how to find and use GodotPrompter skills, with platform-specific tool mapping

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Use when writing or editing a SKILL.md, a skill's references/*.md, or an agent definition in this repo — required frontmatter, section ordering, the GDScript-then-C# example convention, and C# parity exemptions.

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。

Use when creating data containers in Godot — custom Resources for configuration, items, stats, and editor integration

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。

Use when implementing dialogue — data structures for branching dialogue, conditions, and UI presentation

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。

Add Temps analytics to React applications with comprehensive tracking capabilities including page views, custom events, scroll tracking, engagement monitoring, session recording, and Web Vitals performance metrics. Use when the user wants to: (1) Add analytics to a React app (Next.js App Router, Next.js Pages Router, Vite, Create React App, or Remix), (2) Track user events or interactions, (3) Monitor scroll depth or element visibility, (4) Add session recording/replay, (5) Track Web Vitals or performance metrics, (6) Measure user engagement or time on page, (7) Set up product analytics or telemetry. Triggers: "add analytics", "track events", "session recording", "web vitals", "user tracking", "temps analytics", "react analytics".

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Expert blueprint for TileMapLayer and TileSet systems for efficient 2D level design. Covers terrain autotiling, physics layers, custom data, navigation integration, and runtime manipulation. Use when building grid-based levels OR implementing destructible tiles. Keywords TileMapLayer, TileSet, terrain, autotiling, atlas, physics layer, custom data.

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Expert blueprint for save/load systems using JSON/binary serialization, PERSIST group pattern, versioning, and migration. Covers player progress, settings, game state persistence, and error recovery. Use when implementing save systems OR data persistence. Keywords save, load, JSON, FileAccess, user://, serialization, version migration, PERSIST group.

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Expert blueprint for data-oriented design using Resource/RefCounted classes (item databases, character stats, reusable data structures). Covers typed arrays, serialization, nested resources, and resource caching. Use when implementing data systems OR inventory/stats/dialogue databases. Keywords Resource, RefCounted, ItemData, CharacterStats, database, serialization, @export, typed arrays.

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Expert blueprint for player mortality loops: progress-index checkpoints, soul graves, ghost/spirit layers, I-frame restitution, world-progress persistence, and death analytics. Use when implementing respawn, shrine checkpoints, corpse-run retrieval, or second-chance mechanics. Keywords revival, respawn, checkpoint, soul grave, ghost mode, I-frames, death analytics, progress index.

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Expert blueprint for fighting games including frame data (startup/active/recovery frames, advantage on hit/block), hitbox/hurtbox systems, input buffering (5-10 frames), motion input detection (QCF, DP), combo systems (damage scaling, cancel hierarchy), character states (idle/attacking/hitstun/blockstun), and rollback netcode. Based on FGC competitive design. Trigger keywords: fighting_game, frame_data, hitbox_hurtbox, input_buffer, motion_inputs, combo_system, rollback_netcode, cancel_system, advantage_frames.

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 app states and per-entity state data. Keywords: Bevy, States, NextState, OnEnter.

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 components and resources as data. Keywords: Bevy, Resource, Res, Component, data.

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。

Train a custom anime LoRA on the ANIMA base model with Citron's local Gradio trainer (kohya sd-scripts), <6GB VRAM, character/style LoRAs; covers setup, dataset prep, training params, and using the result in the anima-base workflow

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Train custom LoRAs with ostris AI-Toolkit. Covers WAN 2.2/2.1 (people, styles, video motion) and Z-Image (Turbo & Base, low-VRAM image LoRAs). Use when the user wants to train a WAN or Z-Image LoRA; covers local + RunPod setup, dataset prep, key params, and using the result in a ComfyUI workflow.

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。