「core」の検索結果

Superforecasting with calibrated reasoning, Brier score tracking, and prediction ledger management

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 accessibility testing for WCAG 2.2 audits, CI gates, mobile audits, and EAA/ADA/VPAT evidence. Use when adding axe-core, Lighthouse, or screen-reader tests; not fixes.

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。

Designs tool and slash-command runtimes for coding-agent CLIs: registries, deferred loading, tool search, dispatch, forks. Use when modeling tool pools. Not approvals or plugins.

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。

Measures AI coding impact and extension robustness. Use when tracking delivery, quality trajectories, cost, experience, pilots, scorecards, or leadership reporting.

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 @aleabitoreddit ("Serenity")'s full mention archive (built by the follow-aleabito skill) to anticipate where her attention is moving and generate candidate ideas in her style. Two modes — (1) RADAR reads the live mention data for attention momentum (which tickers she is heating up on, new entrants, conviction core, theme rotation) via scripts/radar.js; (2) GENERATOR applies her empirically-mined patterns (theme-rotation logic, selection signature, catalyst playbook) to propose her likely next focus. Every candidate is gated through the serenity-method checklist. This is a CANDIDATE GENERATOR + CHECKLIST, never an oracle or buy/sell signal. Trigger on "what is Serenity ramping on / her next pick / aleabito radar / predict her next move / generate ideas like her / 她下一个可能看什么".

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。

Troubleshoots Monte Carlo AI agent alerts and traces — eval score drops, latency/token spikes, trajectory and validation breaches. Not for data incidents (monte-carlo-analyze-root-cause) or monitor creation (monte-carlo-monitoring-advisor).

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。

Triage Monte Carlo alerts interactively or build an automated workflow. Fetch, score, and troubleshoot alerts using MCP tools now, or design a reusable workflow that runs on a schedule.

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 the critical path smoke test gate before QA hand-off. Executes the automated test suite, verifies core functionality, and produces a PASS/FAIL report. Run after a sprint's stories are implemented and before manual QA begins. A failed smoke check means the build is not ready for QA.

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。

Improve a skill using a test-fix-retest loop. Runs static checks, proposes targeted fixes, rewrites the skill, re-tests, and keeps or reverts based on score change.

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。

Read a story file and implement it. Loads the full context (story, GDD requirement, ADR guidelines, control manifest), routes to the right programmer agent for the system and engine, implements the code and test, and confirms each acceptance criterion. The core implementation skill — run after /story-readiness, before /code-review and /story-done.

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 with feature attributions — return WHY each match scored where it did. Use when debugging recall quality, auditing for bias, or explaining results to a user.

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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 Nexscope data to reverse-lookup traffic keywords for any Amazon ASIN, including organic ranking, ad ranking, search volume, traffic share, organic/paid scores, ABA TOP3 click concentration, click conversion rate, year-over-year search volume changes, and weekly/monthly time windows. Trigger when the user mentions ASIN keyword analysis, ASIN reverse lookup, traffic keyword research, organic ranking query, ad ranking query, keyword position tracking, Nexscope keyword data, competitor keyword peeking, viewing which keywords drive traffic to a product, analyzing keyword performance for a specific ASIN, time windows by week/month/recent N days, ASIN reverse keyword lookup, traffic keywords, organic ranking, ad ranking, search volume, Nexscope keywords, competitor keyword reverse lookup, click concentration, click-to-purchase conversion, week-over-week search volume. Even if the user does not explicitly mention "Nexscope", if their need involves finding keywords associated with a specific Amazon product (ASIN), this skill should also be triggered.

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 Nexscope (Search Intelligence Framework) data to analyze ASIN traffic source composition and exposure distribution, covering current period/previous period/newly entered/exited period comparisons. Trigger when the user mentions ASIN traffic sources, traffic structure analysis, organic traffic vs paid traffic share, exposure score breakdown, period comparison, new/exited traffic keywords, competitor traffic analysis, SP ad keyword count, brand ad exposure, Amazon's Choice exposure, editorial recommendation exposure, Top Rated exposure, video ad exposure, organic search exposure ratio, PPC traffic sources, promotional deal traffic sources, recommendation slot structure breakdown, ASIN traffic analysis, traffic sources, organic traffic share, ad traffic share, exposure analysis, traffic structure, period-over-period comparison, keyword churn, Nexscope. Even if the user does not explicitly mention "Nexscope", if their need involves analyzing an ASIN's traffic sources, exposure channel distribution, cross-period comparison, or competitor traffic structure comparison, this skill should also be triggered.

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。

Perform image-based product search on the 1688 platform. Use an image URL to find visually similar supplier products, returning title, price, minimum order quantity, monthly sales, repurchase rate, trade score, and seller identity. Triggered when users mention 1688 image search, 1688 visual search, find supplier by image, reverse image search 1688, 1688 product sourcing by image, or Alibaba 1688 image search. Even if the user does not explicitly mention "image search," this skill should be triggered whenever a user provides an image URL and wants to find matching or similar products on the 1688 wholesale platform.

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。

Query and analyze Nexscope data for Amazon niche market insights, including market metrics, buyer reviews, competitive landscape, price trends, and growth trends. Trigger when the user mentions niche market analysis, market insights, niche market data, market competition analysis, brand concentration, new product launch success rate, out-of-stock rate, price trends, review insights, market demand score, niche market insights, market metrics, competition analysis, price trends, growth trends, Nexscope data, market intelligence, out-of-stock rate. Even if the user does not explicitly mention "Nexscope" or "niche market", if their need involves querying market-level intelligence for a specific Amazon niche market by market ID, this skill should also be triggered.

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。

Adapt a classroom task for specific learner needs while preserving the core learning objective intact. Use when differentiating for SEND, EAL, gifted, ADHD, dyslexia, or anxiety.

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 audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.

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 metric-driven optimization loops. Use when improving measurable outcomes such as search relevance, clustering quality, build performance, prompt quality, or scored behavior through experiments.

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 plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.

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。

Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.

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。

Retroactively audit an executed AI phase's evaluation coverage — scores each eval dimension as COVERED/PARTIAL/MISSING and produces an actionable EVAL-REVIEW.md with remediation plan

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。

Required bmod metadata. Never invoke this 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。

Convert a vague mini-program idea, feature request, or partially documented product into an evidence-calibrated product specification with target users, core problem, version-one scope, main and exception flows, page responsibilities, state matrices, and testable acceptance criteria. Use when users ask to define an MVP, clarify requirements, organize product flows, specify page behavior, resolve ambiguous product states, or prepare a stable handoff before architecture or implementation. Separates facts, user decisions, assumptions, unknowns, and future ideas; never invents product logic merely to complete a screen or technical plan.

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。