「scratch」の検索結果

Use this skill when the user wants intellectual understanding — learning how or why something works, not getting a task done or soliciting Claude's judgment. Trigger for: - Explicit learning requests: teach, explain, ELI5, walk me through, quiz me, flashcards, "I'm rusty on"; definitions ("what is X") - Terse concept names implying "help me understand this": "Galois theory," "transformers, from scratch" - Confusion signals: "won't stick," "keep mixing these up," "not getting it" - Learning-path questions: prerequisites, sequencing, what to study before X - Conceptual questions about mechanisms, causes, or dynamics Don't trigger for: - Tasks: coding, writing, calculation, translation, factual lookup, news updates - Personal troubleshooting; resource/textbook recommendations - Claude's evaluative verdict: opinion prompts ("do you think X", "settle this", "honest take", "is X dead / still taken seriously") and interpretive takes ("was X really as harsh as people say")

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Builds a GPT and BPE tokenizer from scratch. Use when implementing autograd, attention, a nanoGPT loop, muP hyperparameter transfer, WSD annealing, loss spikes, or mid-training.

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Generate missing documentation from scratch for a feature, module, or entire project. (gstack)

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Stage exploration before instruction on complex problems. The learner produces two attempted approaches before consolidation — which builds on those attempts, not from scratch. Use for genuinely hard problems where struggle produces deeper learning.

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Require the learner to explain a concept in their own words before the AI evaluates or extends it. Ensures the AI works from the learner's understanding rather than providing an explanation from scratch.

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 the user wants to edit, review, or improve existing marketing copy, or refresh outdated content. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' 'copy sweep,' 'tighten this up,' 'this reads awkwardly,' 'clean up this text,' 'too wordy,' 'sharpen the messaging,' 'refresh this content,' 'update this page,' 'this content is outdated,' or 'content audit.' Use this when the user already has copy and wants it improved or refreshed rather than rewritten from scratch. For writing new copy, see copywriting.

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Bootstrap Trigger.dev into an existing project from scratch: authenticate the CLI, install @trigger.dev/sdk and @trigger.dev/build, write trigger.config.ts with the project ref and task dirs, scaffold a /trigger directory with a first task, wire tsconfig and .gitignore, set TRIGGER_SECRET_KEY, and run the dev server. Load this when a project has no trigger.config.ts yet and the user asks to "add Trigger.dev", "set up Trigger.dev", "initialize Trigger.dev", or get a first task running, including in a monorepo. Once the project is set up and you are writing task code, switch to the trigger-authoring-tasks skill.

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Build and grow developer and user communities from scratch — covering ambassador programs, community-led growth (CLG), event operations, content strategy, and health metrics. Grounded in real playbooks from Notion, Lovable, AFFiNE, ClickUp, Asana, and Lark. Use when: building a community for an open-source project, SaaS product, or brand; recruiting and managing ambassadors or community leaders; planning community events (online/offline meetups, AMAs, hackathons); designing ambassador tier/points systems; setting community health KPIs; launching a Build-in-Public strategy; setting up Discord, Slack, or Lark communities; running developer relations (DevRel); or when the user asks about community operations, ambassador programs, CLG, user advocacy, KOC/KOL cultivation, UGC flywheels, community monetization, or any variation of "how do I grow my community".

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Generate custom skill/command from template. Use when creating a new skill or slash command from scratch, or scaffolding a skill file with correct frontmatter.

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Generate custom agent from template. Use when creating a new subagent from scratch, or scaffolding an agent file with correct frontmatter.

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Master orchestrator to build an application from scratch to a production-ready release, enforcing strict step-by-step progression and continuous documentation / Orkestrator utama untuk membangun aplikasi dari nol hingga rilis siap produksi dengan dokumentasi bertahap.

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Remove the AI-script tells viewers hear in a TikTok spoken script and caption: 2026 vocabulary by density, reveal bridges, staccato stacks, stacked triads, performed sincerity, written-not-spoken phrasing, "hey guys" filler; caps em dashes. Includes --mode audit pre-film check (hook, completion design, caption fit) and --mode profile. Not for beating AI detectors (no edit reliably does). Not for writing from scratch (use tt-hook-scripter). Keywords: humanize script, de-AI, audit before filming.

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Map a trending TikTok sound or format to your niche so the ride is authentic, not cringe. Runs a fit check (does it match your niche, is it still early, what is the native joke), then scripts the twist that makes the trend yours: the recognizable beat plus a niche-specific turn. Covers sound timing, the trend lifecycle, and when to skip a trend entirely. Use to ride a trend without looking late or forced. Not for a from-scratch hook (use tt-hook-scripter) or a posting plan (use tt-content-planner).

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Computational thinking as a problem-solving discipline independent of programming languages. Covers the four pillars (decomposition, pattern recognition, abstraction, algorithm design), computational problem-solving methodology (understand, decompose, generalize, formalize, verify), abstraction levels (from hardware through user interface), modeling and simulation, automata and formal languages (DFA, NFA, regular expressions, context-free grammars, Turing machines), computational complexity classes (P, NP, NP-complete, undecidable), and constructionist pedagogy (learning by building, Logo, Scratch, physical computing). Use when approaching unfamiliar problems, teaching problem-solving strategies, analyzing problem complexity, or bridging between domain knowledge and computational solutions.

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Build and maintain a persistent, LLM-maintained markdown wiki following Andrej Karpathy's LLM Wiki pattern — the agent incrementally compiles raw sources into an interlinked knowledge base instead of re-deriving answers from scratch. Use this whenever the user wants to ingest a source (an article, paper, transcript, chapter, meeting notes), file knowledge or conversation findings into the wiki, query the accumulated knowledge base, lint or health-check the wiki, or mentions "the wiki", "knowledge base", "ingest this", "file this", "what do we know about X", or accumulating notes on a topic over time — even if they don't say "LLM Wiki" explicitly.

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When the user wants to edit, review, or improve existing marketing copy. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' 'copy sweep,' 'tighten this up,' 'this reads awkwardly,' 'clean up this text,' 'too wordy,' or 'sharpen the messaging.' Use this when the user already has copy and wants it improved rather than rewritten from scratch. For writing new copy, see copywriting.

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Use when creating a new Claude Code plugin from scratch — orchestrates prerequisite check, user discussion, parallel research, design with verification, atomic implementation, multi-layer validation, documentation, and final verification. For existing plugin improvement, use /plugin-creator:plugin-lifecycle instead.

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Create stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.

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Read or convert documents (PDF, Office, OpenDocument, RTF, EPUB, CSV) in a research workflow: read directly or convert, pick the converter from document structure, and decide whether the Markdown is tracked or scratch. Use when a document must be read, quoted, summarized, searched, extracted, or added to a source library; scans go to $vlm-ocr.

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Use when the user wants to create a new CLAUDE.md from scratch for a repository, bootstrap project memory for a new repo, set up a repo for Claude Code for the first time, or completely rewrite an existing CLAUDE.md deemed unsalvageable. Produces a lean, high-signal file under 80 lines (120 hard cap) by scanning the repo for mechanical content (stack, commands, structure) and interviewing the user for judgment content (WHY, gotchas, out-of-scope, approval gates, testing philosophy, external docs like Confluence pages). NOT for auditing or incrementally improving an existing CLAUDE.md — that is the claude-md-improver skill's role.

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The commit-R protocol: a test-driven rewrite of one compartment of Anabasis v2. Commit what is in flight, remove the compartment's tests in an "R:" commit, write them again from a stated hypothesis so the production code fails on purpose, remove the production files in a second "R:" commit, then write the production code again, rewrite the neighbouring functions the old shape bent, and delete what served only it. Use when the operator says "commit R", "commit-R protocol", "remove and rewrite the tests" or "rewrite from scratch", or asks for a test-first pass that looks for a faster, simpler path to the same outcome.

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Create new AgentOps skills via interactive interview. Supports from-scratch and clone modes with tiered complexity.

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。