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概要と使いどころ
Use for authorized binary analysis in Binary Ninja, including HLIL/MLIL/LLIL inspection, strings/imports/exports, cross-references, types, patch review, Python API automation, and optional Binary Ninja MCP or localhost HTTP 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 Understanding by Design (UbD) to plan one OpenMAIC classroom or a course series around transferable conceptual understanding. Use when the user wants backward design, enduring understandings, essential questions, GRASPS performance evidence, WHERETO learning experiences, or a concept-centered lesson rather than a list of facts. Do not use for visual style cloning, teacher-style imitation, or direct PPT import.
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。
Building one page that looks like a page the course already has — copy the model page with `duplicate_scene`, then rewrite the copy's words with one `patch_stage` op per content slot. Use when a new page has to inherit an existing page's design, whether the model page came from an imported deck or the course wrote it earlier. Not for revising a page that is already the page you want (that is `pro-editing`), and not for the course-level work of cataloguing layouts and assigning one per outline entry (that is `style-clone`).
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。
Migrate Java code from javax.* to jakarta.* namespace. Use when upgrading to Tomcat 11, Jakarta EE 10, or when javax imports are detected in the codebase.
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。
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
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。
Converts Excel (.xlsx) workbooks into Markdown so their contents can be accurately analyzed, summarized, searched, or extracted from. Use this skill whenever the user shares, references, or asks about a .xlsx file — even if they don't say "convert" or "markdown" explicitly. This includes requests to "read", "summarize", "review", "extract data from", "compare", "chart", or "analyze" a spreadsheet, workbook, budget, data export, or tracker. Always run the bundled conversion script to produce Markdown first; do not attempt to parse .xlsx content directly or write ad-hoc extraction code. Also use this skill for batch requests involving a whole folder of Excel workbooks. IMPORTANT: When the user references a folder or set of documents containing multiple file types (.pdf, .docx, .xlsx), invoke ALL three sibling skills — convert-pdf-to-md, convert-word-to-md, and convert-excel-to-md — so no file type is silently skipped.
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。
Converts PDF (.pdf) documents into Markdown so their contents can be accurately analyzed, summarized, searched, or extracted from. Use this skill whenever the user shares, references, or asks about a .pdf file — even if they don't say "convert" or "markdown" explicitly. This includes requests to "read", "summarize", "review", "extract data from", "compare", or "analyze" a PDF report, paper, invoice, form, contract, or scanned document. Always run the bundled conversion script to produce Markdown first; do not attempt to parse PDF content directly or write ad-hoc extraction code. Also use this skill for batch requests involving a whole folder of PDF documents. IMPORTANT: When the user references a folder or set of documents containing multiple file types (.pdf, .docx, .xlsx), invoke ALL three sibling skills — convert-pdf-to-md, convert-word-to-md, and convert-excel-to-md — so no file type is silently skipped.
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。
Converts Word (.docx) documents into Markdown so their contents can be accurately analyzed, summarized, searched, or extracted from. Use this skill whenever the user shares, references, or asks about a .docx file — even if they don't say "convert" or "markdown" explicitly. This includes requests to "read", "summarize", "review", "extract data from", "compare", or "analyze" a Word document, resume, report, contract, or proposal. Always run the bundled conversion script to produce Markdown first; do not attempt to parse .docx content directly or write ad-hoc conversion code. Also use this skill for batch requests involving a whole folder of Word documents. IMPORTANT: When the user references a folder or set of documents containing multiple file types (.pdf, .docx, .xlsx), invoke ALL three sibling skills — convert-pdf-to-md, convert-word-to-md, and convert-excel-to-md — so no file type is silently skipped.
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 Azure Container Registry via the az acr CLI including registries, images, cloud builds, ACR Tasks, authentication, tokens, geo-replication, and networking. Use when working with ACR, az acr commands, pushing/importing/purging container images in Azure, or when the user mentions Azure Container Registry.
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 OpenViking from OpenClaw through @openviking/openclaw-plugin: long-term memory, session archives, resource and Agent Skill import, semantic recall, recall trace debugging, and externalized tool-result recovery. Prefer this skill when the user wants to use, query, debug, or operate OpenViking context from an OpenClaw agent. For first-time plugin installation, use the install-openviking-memory skill 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。
Load when the user asks to add, import, compile, or ingest a research paper/PDF into OpenViking, especially when they mention ov-add-paper, ARA, claims, evidence, figures, tables, or paper-to-OV knowledge resources.
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。
Load when an agent needs to add, manage, browse, search, modify, or remove resources in OpenViking. Trigger on explicit user requests about resource management or context search, when the user mentions `ov add-resource`, `ov task watch`, `ov export`, `ov import`, `ov backup`, `ov restore`, `ov ls`, `ov tree`, `ov read`, `ov write`, `ov mkdir`, `ov rm`, `ov mv`, `ov find`, `ov search`, `ov grep`, `ov glob`, or when an agent needs to inspect, search, or organize the `viking://resources/` namespace.
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 the user's task involves Ant Design (antd) — writing antd components, debugging antd issues, querying antd APIs/props/tokens/demos, migrating between antd versions, or analyzing antd usage in a project. Triggers on antd-related code, imports from 'antd', or explicit antd questions.
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 for the CopilotKit CLI — `npx copilotkit@latest`. Covers proving a project's wiring with `verify` before debugging anything by hand, scaffolding with `create`, signing in and selecting a hosted Intelligence project, agent-assisted onboarding of an existing app, generating type-safe agent ids, and importing thread history. Reach for `verify` first whenever a CopilotKit app is not working.
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。
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa
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 building your own cognee processing — writing custom tasks, chaining them into a pipeline with run_custom_pipeline or the lightweight run_pipeline (from cognee.pipelines import run_pipeline), storing custom DataPoints with add_data_points, running custom extraction/enrichment over the existing graph with memify, checking pipeline run status, or debugging how data flows between tasks (batch_size, data_per_batch, ctx, Drop, enriches).
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 this skill any time a .xlsx file is involved -- as input, output, or both. This includes: creating spreadsheets, financial models, dashboards, or trackers; reading, parsing, or extracting data from any .xlsx file; editing, modifying, or updating existing workbooks; working with formulas, charts, pivot tables, or templates; importing CSV/TSV data into Excel format. Trigger whenever the user mentions 'spreadsheet', 'workbook', 'Excel', 'financial model', 'tracker', 'dashboard', or references a .xlsx/.csv filename.
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。
Migrate from OpenClaw to NanoClaw v2. Detects an existing OpenClaw installation, extracts identity, channel credentials, scheduled tasks, and other config, then guides interactive migration. Triggers on "migrate from openclaw", "openclaw migration", "import from openclaw".
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。
Migrate legacy NanoClaw and Claude-native memory into the shared memory tree and provider-neutral standing instructions. Run after an update reports the shared-memory breaking change, or when a group still has .seed.md, legacy CLAUDE.md/CLAUDE.local.md, Claude auto-memory, or an unindexed imported-agent-memory.md. Triggers on "migrate memory", "legacy memory", "the agent forgot everything after the switch".
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。
Tiered LLM extraction pattern for large corpus processing (email archives, document dumps, transcript libraries). A utility-tier model triages and classifies at speed; the reasoning tier does the default deep read; the deep tier is the escalation for the highest-value content. Prevents spending deep-tier money on noise while ensuring the important content gets the best eyes. A deterministic privacy wall runs before any LLM call.
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。
Import AI-assistant chat exports (ChatGPT, Claude, Perplexity) and agent session transcripts into the brain as one dated page per conversation under conversations/, validate each page against the native conversation parser, extract facts via the native conversation-facts flow, and keep the archive gap-free with a detect-and-backfill loop. Then answer archive questions: "when did I first discuss X", trace how an idea evolved across past conversations, pull a specific thread.
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。
Validate and auto-repair YAML frontmatter on brain pages. Catches malformed pages before they enter the brain (missing closing ---, nested quotes, slug mismatches, null bytes, empty frontmatter, YAML parse failures), explains files sync holds instead of importing, and drives the previewed `gbrain repair frontmatter` fix. Wraps the `gbrain frontmatter` CLI for agent-driven workflows.
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, modify, test, or package Python provider adapters under python/providers, including framework-specific dependencies, public imports, type inference, and provider metadata. Use for Python provider work only; use python-sdk for core SDK changes.
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。
Produces a one-page cross-functional business snapshot for SMB owners — cash position (the ledger: MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), sales trend (PayPal/Square/Stripe), pipeline movement (HubSpot), this week's commitments (Calendar), urgent watch-list items (Gmail or M365, Slack), and the single most important thing needing attention today. Proactively tries every available connector and gracefully scopes to whatever is connected — one connector gives a partial pulse; the full stack gives the full picture. Trigger when the user asks how the business is doing, wants a snapshot, a daily brief, a Friday recap, or says anything like "what am I missing" or "catch me up on the business." A start-of-week briefing routes to /monday-brief, which runs this skill as its first link.
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。