「categorize」の検索結果

Use when creating or backfilling AI-categorized daily GitHub releases from branch 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。

Review documentation for quality, clarity, SEO, and technical correctness. Optimized for Docus/Nuxt Content but works with any Markdown documentation. Use when asked to: "review docs", "check documentation", "audit docs", "validate documentation", "improve docs quality", "analyze documentation", "check my docs", "review my documentation pages", "validate MDC syntax", "check for SEO issues", "analyze doc structure". Provides actionable recommendations categorized by priority (Critical, Important, Nice-to-have).

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。

Crawl GitHub trending repositories, analyze with LLM for Chinese insights, categorize by themes, compute diffs against history, and generate Markdown reports. Default brief mode stops at trend analysis; optional detailed mode appends per-project analysis. Supports incremental gap-filling and selective re-analysis with caching.

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。

Build and maintain a provenance-clean proof library — inventory every real proof asset (analytics, reviews, permitted testimonials, case studies, press, certifications), categorize it, rewrite each cleared item in 3 lengths, fuse top assets with narrative, and gate everything through FTC testimonial rules before it can ship. Use when "proof library", "gather our proof", "authority builder", "collect testimonials", "social proof assets", "proof points for the sales page", "what results can we claim", or any request to assemble evidence for marketing claims. NEVER invents proof — missing proof is reported as a gap.

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 a self-contained HTML changelog report showing recent documentation changes with interactive course generation buttons. Use this skill when the user asks for a docs changelog, documentation report, what changed recently, recent doc updates as a report, or runs `/docs --report`. Discovers changes via git history, categorizes them, summarizes what's new, and produces a stunning Obsidian & Amber themed HTML report where each entry has a "Create Course" button that copies the course command to clipboard.

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。

Restructure and enrich the tdmcp documentation site for artists — categorized/collapsible sidebar (EN+PT), interactive goal-based guide home, new step-by-step tutorials, copy-prompt/level-badge components. Use whenever the user asks to make the docs more interactive, easier to navigate, better organized, add categories/subcategories, add tutorials, improve docs UX/IA, or for follow-ups: re-run, continue, fix QA findings, add one more tutorial, re-organize a section, or update the docs structure. NOT for syncing docs with a release (tdmcp-docs-roadmap-update) or cookbook entries (tdmcp-cookbook-examples).

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 says 'find old content', 'what is stale', 'what needs refreshing', or 'what should i archive'. Categorizes pages and posts as fresh, aging, stale, or archive-candidate by age, traffic signal, and internal link count, with a refresh, redirect, or archive call per item.

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 you need to auto-log all tool interactions into a self-organizing vector DB with instant recall. Intercepts interactions, extracts key information, generates embeddings, stores in LanceDB, auto-categorizes, and builds a retrieval index. Inspired by OpenClaw SQLite LCM + summary DAG pattern.

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。

Operator-side analysis of historical ML experiment PRs in Senpai research tracks. Use this skill whenever the user asks to: analyze experiments, categorize PRs, bucket experiments, summarize what's been tried, understand experiment history, review merged vs closed results, or asks "what experiments have we run / worked / failed". Also triggers for: "pull the latest experiments", "what's been tried so far", "category breakdown of PRs", "which experiments succeeded", "noam track analysis". When a branch name is mentioned (e.g. "noam branch", "on the noam branch"), pass it as the base branch to scope the fetch to just those PRs.

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。

"Implement LDA topic modeling to discover latent topics in document collections. Use this skill when the user needs to extract topics from a text corpus, categorize documents by theme, or explore thematic structure — even if they say 'what are the main topics', 'topic extraction', or 'document clustering by theme'.".

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。

Track, categorize, and prioritize technical debt across the codebase. Scans for debt indicators, maintains a debt register, and recommends repayment 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。

Write SEO-optimized long-form articles targeting specific keywords using the They Ask You Answer Big 5 framework (Marcus Sheridan). Articles are categorized by Big 5 type (Cost, Problems, Versus, Best/Reviews, How-To) and structured accordingly. The "answer first" rule applies to every article. Use when the user asks for an SEO article, blog post for ranking, "rank for keyword X", organic content, search-optimized post, pillar page, or content for organic traffic. Includes keyword targeting, search intent matching, internal linking suggestions, and meta tags. For improving an existing article, see copy-review. For deciding what to write about, see social-calendar pillars and icp-research. For repurposing it, see content-repurposer.

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。

Phase 1 of the SRS workflow. User gives a project topic → AI runs a deep, categorized brainstorm (5 mandatory rounds) to fully understand actors, features, scope boundary, tech constraints, and business rules before writing anything.

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。

Phase 1 of the SRS workflow. User gives a project topic → AI runs a deep, categorized brainstorm (5 mandatory rounds) to fully understand actors, features, scope boundary, tech constraints, and business rules before writing anything.

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 or update CHANGELOG.md from git history. Parses conventional commits, groups by version tags, categorizes into Added/Fixed/Changed/Breaking sections using keep-a-changelog format, and creates comparison links. Use when you need to create a changelog, update release notes, document version history, or prepare release documentation.

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 a production-grade OEE (Overall Equipment Effectiveness) measurement pipeline for a manufacturing line — data ingestion from MQTT/OPC-UA/Modbus/CSV, A×P×Q calculation engine, Six Big Losses categorizer, MTBF/MTTR/TEEP metrics, Grafana or Streamlit dashboard.

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。

Generates backend or frontend engineering specs in structured Jira format with description, categorized acceptance criteria, routes, dev notes, and table schemas.

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。

White-box security audit. Blue-teamer and lead red-teamer run in parallel isolation for an independent first pass — neither sees the other's output during reconnaissance. A synthesis step categorizes findings into four prescriptive buckets (anchoring-suppressed, convergent, blue-flagged-unverified, divergent), producing a unified target list. Focused red-teamers then deep-dive each target. Iterates when exploit chains are discovered. Heavy and thorough by design. Advisory only — produces findings and proposes tickets; does not implement remediation.

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。

Remove AI-generated code smells (slop) from branch changes or an explicit file list. Locks behavior with regression tests FIRST, then runs categorized cleanup via parallel `deep` agents in batches of 5, then verifies with quality gates. Covers 10 slop categories including performance equivalences, excessive complexity (object annotations, if/elif variant chains), and oversized modules (250+ pure LOC with mandatory modular refactoring). MUST USE when the user asks to "remove slop", "clean AI code", "deslop", "clean up AI-generated code", "remove AI slop", or wants to clean up AI-generated patterns from recent changes. Triggers - "remove ai slops", "clean ai code", "deslop", "cleanup AI generated", "remove AI slop", "clean up AI-generated code", "strip slop", "ai-slop cleanup".

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。

Transform technical git commits into polished, user-friendly changelogs and release notes. Automatically categorizes changes, translates developer language into customer-friendly copy, and generates professional changelog entries.

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 privilege-safe U.S. corporate legal billing summaries from time and expense data. Converts entries into client-friendly narratives with categorized hours, rates, expenses, and milestones while enforcing engagement-letter rules and ethical billing safeguards. Use when asked to create a billing summary, invoice narrative, timekeeper breakdown, expense summary, or fee narrative.

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 structured asset and liability summaries from financial documents for legal proceedings. Extracts valuations, ownership classifications, and encumbrances, then outputs net-worth overview, categorized schedules, disputed items, and documentation gaps. Use when preparing financial summaries for divorce/dissolution, estate planning, bankruptcy, business valuation, or settlement negotiations. Trigger on: net worth summary, marital estate, balance sheet, asset schedule, liability schedule, community/separate property.

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 a monthly money check-in with an AI assistant: categorize spending, spot leaks, set one savings target, and view it through the 50/30/20 lens — judgment-free. Use once a month to stay aware of where your money goes. General educational information, not financial advice.

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。

Systematic VPS cleanup — analyze files, categorize by importance, safely delete temporary/old data to free disk space

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。