262 件 ・ 関連度順
概要と使いどころ
Structure a performance review with self-assessment, manager template, and calibration prep. Use when review season kicks off and you need a self-assessment template, writing a manager review for a direct report, prepping rating distributions and promotion cases for calibration, or turning vague feedback into specific behavioral 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。
Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:memory-review or asks what has been learned and what should be promoted or pruned.
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, test, and score the quality of skills within the claude-skills ecosystem. Comprehensive meta-skill: structure validation, Python script testing (syntax + imports + runtime + output format), multi-dimensional quality scoring with letter grades and tier classification (BASIC/STANDARD/POWERFUL). Use when authoring a new skill, auditing existing skills for tier promotion, setting up pre-commit hooks for skill quality, or integrating skill QA into CI.
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 5 creative, cost-effective marketing ideas with channels, messaging, and engagement rationale. Use when brainstorming marketing campaigns, planning product promotion, or looking for creative marketing tactics.
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。
Draft launch or promotion copy for a shipped feature.
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 auto-memory entries and propose promotions to OPENSPACE.md, OPENSPACE.local.md, or retained auto-memory topics; detect duplicates, stale facts, conflicts, and ambiguous entries before making 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。
Use when a LoopX worker is operating an auto-research lane, demo pane, frontier item, evidence packet, promotion/retirement decision, or visible tmux/Codex auto-research rehearsal. Identity must come from the LoopX role profile and quota/frontier packet; this skill only provides role-specific execution checklists, artifact contracts, and stop conditions.
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。
Automates the complete Xiaohongshu (XHS / Little Red Book) content operation workflow: pain-point topic collection → style case collection → topic selection → content writing → publishing → performance tracking. Use when user mentions xiaohongshu auto posting, xhs auto post, little red book posting, xiaohongshu post, xiaohongshu auto posting, xiaohongshu content operations, xhs content marketing, post to xiaohongshu, publish on xhs, post on xiaohongshu, xiaohongshu promotion, xiaohongshu operations, xiaohongshu automation, xhs automation, track xhs performance, track xiaohongshu performance, xiaohongshu data tracking, xiaohongshu analytics, switch xhs account, update xhs keywords.
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。
Extracts comprehensive wholesale product data from 1688.com product detail pages: title, tiered pricing, SKU variants with dimensions/weight, product images, seller info, shop scores, buyer protection, cross-border flags, product attributes, coupon/promotion data, and review stats. Use when user mentions 1688, 1688.com, wholesale China, alibaba wholesale, B2B China sourcing, Chinese wholesale scraper, 1688 product scrape, 1688 offer, 1688 detail, extract 1688 data, pull 1688 listings, get wholesale price, 1688 supplier info, factory stats 1688, 1688 SKU variants, 1688 product attributes, 1688 shop score, DSR score 1688, 1688 buyer protection, 1688 cross-border, 1688 dropship. Also applies to: scraping bulk product data from 1688 by offer ID list, monitoring 1688 supplier metrics, extracting 1688 pricing tiers for resale analysis.
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。
This skill helps users extract structured best-selling product data from Amazon via the BrowserAct API. Agent should proactively apply this skill when users express needs like search for best selling products on Amazon, extract Amazon product data based on keywords, find top rated Amazon products, monitor Amazon competitor prices and sales, discover trending products on Amazon marketplace, extract Amazon product titles prices and ratings, gather Amazon product sales volume for market research, search Amazon best sellers in specific region, collect Amazon product reviews and promotion details, analyze Amazon product availability and badges, get Amazon product data for market analysis.
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。
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。
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。
Write complete nurture email sequences with subject lines, preview text, and body copy using proven copywriting formulas. Use when given ICP, offer, and objections to generate full email flows, creating welcome sequences, writing promotional emails, or maintaining voice consistency throughout email campaigns. Platform: Google and Meta.
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 to flash a promoted BSP image to a Jetson DUT in RCM mode via flash.sh or l4t_initrd_flash.sh. Do NOT use for BSP customization, image promotion, or carrier derivation.
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] Recurring lessons worth promoting: turn repeated project or cross-project lessons into atomic, confidence-scored instinct candidates with scoped promotion and export boundaries. Use when the user says: instinct-ledger, instinct ledger, project instincts, project-scoped instincts, project scoped instincts, global instincts, instinct review, instinct candidate.
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 "plan the participation ramp before we promote", "how much account history or karma do we need in this community", or "design entry incentives and member lifecycle for our own Discord"; produces the per-community pre-promotion warming plan — account-history/tenure expectations (Estimated, named sources), a give-before-ask ledger spec, a per-community etiquette + rule digest with last-verified dates, and the warming → active graduation criteria that channel-registry requires as state-transition evidence — plus the owned-community variant (entry paths + member lifecycle for your own Discord/Slack/forum/企业微信私域). Not for launch-day submissions or T-0 threads — use community-launch-runner. 社区预热/先给后取/账号养成/毕业标准
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 "launch on Product Hunt / Hacker News", "prepare community or directory launch submissions", or "plan the launch submission waves"; produces per-platform submission packages — a Product Hunt tagline / gallery / first-comment skeleton, a factual Show HN title and text, per-subreddit posts with a self-promotion rules table, tiered directory waves, and a regional channel matrix including Chinese communities — plus a platform red-line check (never solicit votes or organize voting rings) and T-0 submission-status lines for the launch registry. Not for paid amplification — use content-amplifier; not for creator channels — use campaign-planner; not for launch telemetry readouts — use launch-monitor; not for ongoing community presence or pre-launch karma-building outside the launch window — use participation-warmup-planner. 社区发布/PH提交包/Show HN/目录波次/平台红线/中文渠道
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 "run a deliverability pre-flight before I send", "check my SPF/DKIM/DMARC/BIMI", "why am I landing in spam / promotions", or "score my sender reputation and list hygiene"; runs the ONE-TIME pre-send SEND S1 authentication pre-flight and builds the SEND S (Sender-integrity / Deliverability) evidence read — DNS + DMARC-RUA auth, domain/IP reputation, inbox placement, content/link/render, and point-in-time bounce/complaint hygiene — using Pass/Partial/Fail/Unknown/N/A states and scoring only at complete applicable coverage. Not for the recurring hygiene trend — use list-hygiene-monitor; not for final EQS or veto verdicts — use email-quality-auditor; not for segments/suppression lists — use list-segment-builder. 邮件送达率预检/SPF DKIM DMARC认证/发件域声誉
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。
Design isolated Algolia development, staging, preview, and production targets with explicit promotion rules. Use when environments share credentials or index names, or previews need bounded search data. Trigger with "Algolia environments", "Algolia staging setup", or "preview index".
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 text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
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。
Builds and maintains the leveling framework and pay structure — level definitions, salary bands, benchmarking, pay equity, and how raises and promotions are decided. Use this to design or revise leveling, set or adjust salary bands, benchmark against market, handle a compensation request or counteroffer, run a review cycle, or diagnose pay compression and equity issues.
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 converting academic papers to promotional and presentation formats, including interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster). This skill is suitable for paper dissemination, conference preparation, creating explorable academic homepages, generating video abstracts, or producing printable posters from LaTeX or PDF source.
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 evaluating Hindenburg-style investigative short reports: evidence chains, promoter networks, stock promotion, related parties, disclosure failures, and catalyst-driven short narratives.
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。
Decode a counteroffer after you resign — what the raise, promotion promise, or title bump really signals, the statistics-informed risks of staying, and a clear-eyed decision framework. Use when asked my company countered my resignation, should I accept a counteroffer, they offered me more to stay, or decode this retention offer. Produces a component-by-component decode with 🔴🟡🟢 severity, the questions that expose which promises are real, and the stay/go decision sheet.
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。