「signal」の検索結果

Monitor buyer intent signals across the web including job postings, tech changes, funding rounds, and leadership changes. Alerts when prospects show buying signals and prioritizes "hot" accounts. Use for timing-based prospecting.

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Universal opt-out mechanism implementation across US state privacy laws. Covers Global Privacy Control (GPC) signal technical implementation, state-by-state recognition requirements, browser detection methods, authenticated vs unauthenticated handling, and compliance testing.

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AppSignal integration. Manage data, records, and automate workflows. Use when the user wants to interact with AppSignal data.

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Emit the content signals reimagine-it reads from an HTML file — title, anchors, proper nouns, dates, numbers, emails, links, source hex colors, and the derived palette — as JSON without generating a redesign. Use when the user says /reimagine-it extract, "what does the engine see in this page", "extract the palette", "pull the facts from this page", or wants content analysis for research, migration, or design-system work. CLI: npx reimagine-it extract -i page.html -o signals.json. Part of the reimagine-it Content-Derived Design suite.

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Think and work like an expert Cell Signaling Biologist. Use when a task calls for Cell Signaling Biologist judgment. Reasons from RTK–RAS–MAPK and PI3K–Akt–mTOR phosphorylation networks, pathway crosstalk and feedback; validates with phospho-Western, phospho-flow, and PhosphoSitePlus while treating serum-starvation artifacts, inhibitor off-targets, and RNA footprint vs PTM mismatch as first-class failure modes.

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The fusion of trend prediction and AI content generation: detecting emerging trends before they peak and generating content that rides—or creates—the wave. This is marketing alchemy, transmuting data signals into viral gold. Traditional trend marketing is reactive: see trend, create content, hope you're not too late. AI Trend Alchemy is predictive: detect signal, generate content, be there as the wave crests. The combination of AI trend detection and AI content generation creates a feedback loop that can outpace human competition. This skill combines data science intuition with creative generation, reading the digital tea leaves while simultaneously conjuring the content that captures the moment. It's part analyst, part creator, part prophet. Use when "trend, trending, viral, predict, emerging, what's next, cultural moment, before it peaks, early signal, catch the wave, trend prediction, going viral, trend, prediction, viral, cultural, emerging, timing, alchemy, detection" mentioned.

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Prospects companies across developer-focused tech verticals for developer marketing agency outreach. Use this skill whenever a user provides a vertical (e.g. AI Agentic, IAC, DevTools, Observability, DevOps, FinOps, AI/SDLC, AI Orchestration, or any other tech vertical) and wants a list of prospect companies to target. Also trigger when the user says "prospect for", "find companies in", "give me leads for", "build a prospect list", or provides a vertical name alongside funding stage and headcount filters. Always use this skill — never attempt prospecting without following this structured workflow. The skill finds exact-fit companies only (never adjacent), maps a real outreach signal and email pain point for each, sources every data point with a URL, and outputs a unified nine-column table: Company, URL, LinkedIn, Headcount, Signal, Signal Sources, Geography, Pain Point, Pain Point Sources.

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Evaluates factor timing strategies with macro regime indicators, valuation spreads, and momentum signals for factor rotation. Use when analyzing factor timing, evaluating rotation signals, or designing tactical factor allocation.

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Evaluates insider purchase patterns with cluster buying identification, historical signal analysis, and conviction scoring. Use when analyzing insider buying, assessing management confidence signals, or tracking insider activity patterns.

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Evaluates alternative data sources including satellite, NLP sentiment, web scraping, and geolocation for alpha signal generation. Use when analyzing alt data, evaluating new data sources, or integrating non-traditional signals.

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Evaluates signal half-life, turnover implications, and capacity constraints for systematic alpha factors. Use when analyzing signal persistence, evaluating factor decay, or estimating strategy capacity.

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Fires after fixing any non-trivial bug or regression. Asks: could the graph have caught this as a guarantee? Pairs with reflection-in-and-on-action — picks up after "earliest catchable signal" and asks the next question: was that signal expressible in graph? Triggers: (1) after any non-trivial bug fix is verified working, (2) after a regression report (something used to work, broke), (3) during step 6 (knowledge extraction) of the workflow, (4) when reflection-on-action surfaces a "would have been catchable" signal. Outcome is a triage decision (graph-reachable? rule expressible? rule sound?) and either a draft Linear issue + guarantee proposal, or a recorded "graph capability gap" note. Never auto-creates guarantees.

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Use ManagedCode.Orleans.SignalR when a distributed .NET application needs Orleans-based coordination of SignalR real-time messaging, hub delivery, and grain-driven push flows.

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NgRx SignalStore patterns for Angular state management. Use when managing client state with Angular Signals, composing store features, handling entities, or integrating RxJS effects.

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Create, revise, render, or visually audit Quiet Signal A4 long-form PDF reports from source material using editable HTML/CSS, source-backed content, and full-page rendering QA. Use for detailed reports; not for slides or Xiaohongshu summaries.

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Convert Claude's Markdown output to each channel's native text syntax before delivery. Adds zero-dependency formatting for WhatsApp, Telegram, and Slack (marker substitution). Also ships a Signal rich-text helper (parseSignalStyles) used by the Signal skill.

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Fri→Fri community signals routine across CopilotKit + AG-UI. Orchestrator that supervises subagents — Discord pull, GitHub pull, deep-read, reporter enrichment, Reddit Pulse — and synthesizes a Notion report under the Community Signals parent page. Triggers on "go", "weekly report", "community signals", "run routine".

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Expert-level signal processing covering Fourier analysis, sampling theory, digital filters, FFT, spectral estimation, and adaptive filtering.

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Convert Claude's Markdown output to each channel's native text syntax before delivery. Adds zero-dependency formatting for WhatsApp, Telegram, and Slack (marker substitution). Also ships a Signal rich-text helper (parseSignalStyles) used by the Signal 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 Claude's Markdown output to each channel's native text syntax before delivery. Adds zero-dependency formatting for WhatsApp, Telegram, and Slack (marker substitution). Also ships a Signal rich-text helper (parseSignalStyles) used by the Signal skill.

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Focused Signals scout for PostHog projects using feature flags. Watches the flag roster and the `$feature_flag_called` evaluation stream for contradictions between a flag's configured state and its real traffic: evaluation cliffs on healthy flags, ghost flags (code calling keys that no longer exist), response-distribution shifts with no corresponding flag edit, and flag debt (stale, fully-rolled-out, or dead flags still burning evaluations). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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Focused Signals scout for PostHog projects collecting Content Security Policy (CSP) violation reports. Watches `$csp_violation` events for fresh blocked-URL clusters, per-directive bursts, page-scoped regressions after deploys, and suspicious third-party domains that may indicate a compromised script. Emits aggregated findings only when a cluster clears the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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Focused Signals scout for PostHog projects with web traffic. Watches the acquisition and site-health layer the web analytics product reports on: per-channel session volume diverging from the site's own rhythm (an acquisition source silently collapsing or surging), attribution breakage (paid/campaign traffic reclassifying into Direct or Unknown when tagging breaks), landing pages that break (bounce-rate steps, 404 spikes, entry-path cliffs), and page-performance regressions (web vitals p75 steps). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

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Signals scout that watches a PostHog project's most-viewed dashboards and insights for recent anomalies — sudden bursts, drops, flat-lines, and trend breaks at the daily or hourly level. It discovers what the team actually looks at (view counts, dashboard access), curates a durable watchlist in the scratchpad, and balances re-checking known high-value insights (exploit) against discovering new ones (explore) across runs, since no single run can cover a busy project. Anomalies are scored by robust deviation from each insight's own seasonality-matched baseline; it emits a finding only when a move clears the confidence bar, otherwise it updates the baseline memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

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。