「wiki」の検索結果

This skill should be used when the user asks to "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing", or needs to eliminate AI writing patterns from prose. Supports both English and Chinese text. Based on Wikipedia's "Signs of AI writing" guide, detects and fixes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, AI vocabulary, negative parallelisms, and excessive conjunctive phrases.

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 when the user wants to repair or strengthen Obsidian wikilinks among existing canonical project notes, especially across papers, knowledge notes, experiments, results, and writing.

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。

VS-Enhanced Academic Style Auditor - Academic Writing Quality Analysis Identifies 24+ writing patterns that reduce scholarly quality, adapted from Wikipedia AI Cleanup guidelines Use when: checking drafts before submission, improving academic writing quality, preparing for style improvement Triggers: writing quality, style audit, pattern check, writing review, academic style check

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 signs of AI-generated writing from academic medical papers. Use when editing or reviewing manuscripts to make them sound more natural and professionally written. Based on Wikipedia's "Signs of AI writing" guide, adapted for medical literature.

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。

Coordinate Lark/Feishu CLI tasks from Codex. Use when a request involves Feishu/Lark docs, wiki, Drive files, Sheets, Base, Calendar, Minutes, IM, Mail, Tasks, Contacts, Slides, whiteboards, approvals, attendance, or when a Lark URL/token must be read, edited, summarized, routed, or turned into a real work artifact.

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。

Recover deleted GitHub content using the Wayback Machine and Archive.org APIs. Use when repositories, files, issues, PRs, or wiki pages have been deleted from GitHub but may persist in web archives. Covers CDX API queries, URL patterns, and systematic recovery 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。

Comprehensive data visualization toolkit for creating beautiful, mathematically elegant visualizations with D3.js, Chart.js, and custom SVG. Use when (1) building interactive data visualizations, (2) designing color palettes for charts, (3) choosing scales and visual encodings, (4) creating data pipelines from Census/SEC/Wikipedia APIs, (5) crafting narrative-driven data stories, (6) making perceptually accurate charts, or (7) implementing force-directed networks, timelines, or geographic maps.

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。

Configure Obsidian graph colors by tag, folder, category, or visibility by updating .obsidian/graph.json. Use for graph colorization; preserve other graph settings and back up before 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。

Ingest GitHub Copilot CLI/session history into Obsidian as distilled knowledge. Use for importing or mining Copilot history; not for general Copilot help, simple session search, or backup.

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。

Ingest Claude Code conversation/session history into Obsidian as distilled knowledge. Use for importing or mining past Claude sessions, .claude data, project/session history, or audit logs.

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。

Independently check whether an implementation matches its stated goal and return PASS/WARN/FAIL with actionable issues. Use for second-opinion validation of an implementation or generated output.

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。

Adjust the user's Obsidian visual layout with CSS snippets. Use for restyling tabs, sidebars, note surfaces, properties, backlinks, graph or file-explorer UI, or diagnosing CSS changes that did not take effect.

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 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.

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。

Search the session-brain graph for a past agent session by topic and load the winning transcript into the current conversation. Use for finding which session handled X, when Y was last worked on, or whether this was done before. Read-only; requires a session-brain graph.

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 the load-bearing files, symbols, callers, and dependencies of an unfamiliar codebase before making changes. Use for architecture orientation, impact analysis, what-would-break questions, or finding callers. Uses CodeGraph when available and a dependency-free builtin fallback.

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。

Golang ecosystem watch list — official sources (go.dev/blog, pkg.go.dev, tour.golang.org, golang-nuts), newsletters (Golang Weekly, Awesome Go Newsletter), communities (r/golang, gophers.slack.com, Go Forum, go.dev/wiki), blogs (Dave Cheney, Ardan Labs, Rob Pike), YouTube channels (Gopher Academy, GopherCon EU/UK), conferences, and Go contributors to follow on GitHub, X and Bluesky. Use when seeking Golang learning resources, discovering new libraries or tools, finding community channels or meetups, picking Go people to follow, or keeping up with Go language changes and releases. Not for querying a specific module's versions, docs, or vulnerabilities from the CLI (→ See `samber/cc-skills-golang@golang-pkg-go-dev` 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。

[omh] Missed route or run lessons to record: classify and review self-improvement store routes as an auxiliary review lane before durable writes, then record workflow attempts as metadata-only traces, evals, review queues, patch proposals, regression cases, audits, indexes, and exports. Use when the user says: workflow-learning, workflow learning, route-signal, self-improvement store routing, store route review, memory skill wiki routing, learning trace, learning audit.

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。

Answers questions about a code repository using source file analysis. Use when the user asks a question about how something works, wants to understand a component, or needs help navigating 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。

Generates four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager. Use when the user wants onboarding documentation for a 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。

Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or 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。

Generates AGENTS.md files for repository folders — coding agent context files with build commands, testing instructions, code style, project structure, and boundaries. Only generates where AGENTS.md is missing.

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。

Analyzes git commit history and generates structured changelogs categorized by change type. Use when the user asks about recent changes, wants a changelog, or needs to understand what changed in the repository.

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 asks to "figure out where our direct traffic really comes from", "measure dark social", "add a how-did-you-hear-about-us field", or "show social drives signups without click data"; produces a share-link/UTM hygiene spec for owned share surfaces, a self-reported attribution field design that replaces an existing form field (free-text first, coded later), a GA4 direct-traffic decomposition read (deep-URL directs, mobile-app skew, private-push correlation) with every derived number hard-labeled Estimated/proxy, and a branded-search-lift proxy from GSC plus Wikipedia pageviews — the declared dark-social method behind ECHO O2. Not for paid-channel attribution reconciliation (platform-claimed vs analytics conversions) — use attribution-reconciler. 暗社交归因/直接流量分解/自报来源字段/分享链路UTM

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 asks to "track our share of voice", "what share of the conversation do we own vs competitors", or "trend our SOV this quarter"; computes SOV% = brand mentions ÷ (brand + competitor panel mentions) per platform per period on a LOCKED competitor panel — a panel switch invalidates the trend (restart the series and log the break; the ECHO O3 rule) — plus a sentiment-weighted SOV variant (sentiment labeled Estimated unless human-coded) and a Wikipedia-pageviews attention-share alternative; built from keyless listening connectors, gdelt.py news echo, and user exports — public counts only, closed platforms are never scraped. Not for backlink or offsite SEO signals — use offsite-signal-analyzer. 声量份额/竞品声量对比/提及份额/注意力份额

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。