「signals」の検索結果

Analyzes competitor products and companies by synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media into structured competitive intelligence. Produces feature comparison matrices scored across 12 dimensions, SWOT analyses, positioning maps, UX audits, pricing model breakdowns, action item roadmaps, and stakeholder presentation templates. Use when conducting competitor analysis, comparing products against competitors, researching the competitive landscape, building battle cards for sales, preparing for a product strategy or roadmap session, responding to a competitor's new feature or pricing change, or performing a quarterly competitive review.

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 production-ready observability strategies combining metrics, logs, and traces. Includes SLI/SLO design, golden-signals monitoring, alert optimization. Use when adding observability to a new service, refactoring alerting that is too noisy, or designing an SLO program before scaling production load.

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。

Produce a rigorous, sector-relative, multi-factor fundamental analysis of a publicly listed company — Indian (NSE/BSE) or US/global. Use when the user asks to analyse, research, evaluate, or value a stock, ticker, or listed company; asks whether a business is fundamentally strong, cheap, or expensive; compares companies or benchmarks one against its sector; or mentions OPM, ROCE, ROE, ROIC, P/E, EV/EBITDA, free cash flow, NIM, GNPA, CASA, promoter holding or pledging. Use it for accounting-quality and forensic questions — "is the profit real", "why is profit rising but cash isn't", auditor qualifications, related-party concerns — which route to the forensic-only mode, and for IPOs and not-yet-listed companies — "should I apply to this IPO", DRHP/RHP or S-1 questions, price band, grey market premium — which route to the IPO mode. Use it even when the request sounds casual ("is Infosys any good?"). Do not use it for personalised investment advice, portfolio allocation, or trading signals.

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Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

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 pragmatic CI/CD pipelines from detected project stack signals — fast baseline generation, repeatable checks, environment-aware deployment stages. Use when setting up CI for a new project, refactoring existing pipelines, or standardizing deployment workflows across multiple repos.

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。

Cross-functional organizational health check combining signals from all C-suite roles. Scores 8 dimensions on a traffic-light scale with drill-down recommendations. Use when assessing overall company health, preparing for board reviews, identifying at-risk functions, or when user mentions org health, health check, or health dashboard.

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Summarize a customer interview transcript into a structured template with JTBD, satisfaction signals, and action items. Use when processing interview recordings or transcripts, synthesizing discovery interviews, or creating interview summaries.

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 time-windowed product pulse reports from configured signals.

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。

Find link prospects, discover contact paths, and draft outreach from SERPs and backlink signals.

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。

Runbook for turning a HubSpot renewal date into a prepped packet and a drafted outreach email for {{projectName}}. Covers the 90/60/30-day windows, what belongs in a renewal packet, at-risk signals, Google Calendar cross-checking, and the human approval gate before anything sends or discounts.

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Scoring rules for daily churn-risk detection — usage decline, support friction, billing/payment risk, and renewal proximity — plus how to combine them into a ranked at-risk list with a reason and a suggested next step per account.

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Discover WHICH new skills you should create, from your own work history plus your satisfaction/frustration signals. Read-only and proposal-only: it surfaces recurring pain that no existing skill, loop, or workflow covers, then hands you a ranked shortlist to build with CreateSkill. It never creates or edits a skill itself. Frustration is a first-class signal (a topic can look 'covered' while you keep hitting the same wall inside it), so it reads low ratings and recurrence markers, not just session topics. USE WHEN should I create a skill, what skills do I need, suggest skills, skill gap, based on my recent work, am I missing a skill, what should I build. NOT FOR creating/validating/testing/optimizing an individual skill (use CreateSkill) — this only decides WHAT to build, not how.

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。

Audit and fix agent readiness: the Lighthouse Agentic Browsing fraction, accessibility tree for agents, robots.txt and Content-Signal for AI agents, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known discovery files, and WebMCP tools. Exclude AI citability and brand signals (seo-geo) and commerce protocol depth (seo-ecommerce).

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 a site's backlink profile, anchors, toxic signals, competitors, gaps, and disavow candidates. Use only when links or referring domains are the requested focus.

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。

Audit and improve content for AI Overviews and answer engines, including citability, entity clarity, crawler access, brand signals, and passage structure.

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 ecommerce SEO across product pages, product schema, Shopping visibility, marketplace signals, and keyword gaps. Use only for stores, catalogs, or product listings.

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。

Codex-only writing and layout overlay for AI Berkshire investment research reports. Use whenever Codex creates, rewrites, revises, or critiques company/industry/fund research reports, especially long-form Markdown reports that need financial rigor, readable business mechanics, contrarian analysis, valuation-to-action guidance, investor-specific recommendations, restrained typography, and clear buy/hold/sell signals. Do not use this to modify Claude Code slash-command sources.

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。

First-pass triage of a possible production incident for Superset's hosted services. Gathers signals (recent deploys, Sentry spikes, health checks), proposes a severity and a status.superset.sh message, then stops for human approval. Use when someone reports an outage, errors spike, or asks "is something down?".

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 Angular 17+ standalone components, configures advanced routing with lazy loading and guards, implements NgRx state management, applies RxJS patterns, and optimizes bundle performance. Use when building Angular 17+ applications with standalone components or signals, setting up NgRx stores, establishing RxJS reactive patterns, performance tuning, or writing Angular tests for enterprise apps.

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。

Scan recent merges to main (and noteworthy direct commits) since a given window (last tag or date in state). Extract titles, labels, types, linked issues, and signals for breaking changes or security. Produces structured input for a release notes drafter. Use in changelog-drafter loops.

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。

Scan merged PRs and commits since a given reference, extract titles, labels, types, and signals. Produces structured input for release notes drafting.

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。

Findings-driven targeted code review. Understands the patch via patch-explainer and surrounding code via codebase-analysis, then consults a catalog of bug patterns (~11 categories under references/categories/). Picks only categories whose diff-signals match the patch and items whose "look for" hints match actual code shapes, groups them by review focus (function/file/feature), and spawns parallel subagents — each with a tight scope and selective checklist. Use for medium-to-large patches (50-1000 LOC) as a focused alternative to whole-patch ensemble or whole-file deep review. Triggers on: "review this patch/diff/change", "find bugs in this change", "scoped review", "what could go wrong here", "review using findings", or proactive PR review.

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。

Research competitor paid-ad presence, messaging, creative, formats, landing pages, keyword and auction signals, transparent ad libraries, and strategic gaps across supported platforms. Use for competitor ads, ad libraries, ad spy, competitive PPC analysis, competitor creative, Google Ads Transparency, Meta Ad Library, or paid-media competitor research.

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 FASTQ inputs, run local FastQC/MultiQC QC, interpret QC signals, and optionally execute fastp or Cutadapt trimming branches without overwriting raw reads.

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。