「signals」の検索結果

Elliott Wave Theory signal engine. Detects swing points through Zigzag, matches 5-wave impulse and 3-wave corrective structures, validates them with Fibonacci wave relationships, and generates trend-top / correction-complete signals. Pure in-house pandas implementation.

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。

Global macro analysis framework (central bank policy transmission / FX forecasting / geopolitical risk / capital flows), used to build macro factor signals that drive cross-asset allocation.

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。

Commodity analysis (oil supply-demand balance / gold pricing / copper as an economic predictor / inventory cycles / futures premium-discount structure / seasonality), generating directional commodity 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。

Harmonic Patterns signal engine. Identifies XABCD five-point structures such as Gartley/Bat/Butterfly/Crab based on Fibonacci geometry, and generates trading signals in the PRZ (Potential Reversal Zone).

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Stock Connect (Shanghai/Shenzhen-Hong Kong) fund flow analysis — Northbound (foreign into A-shares), Southbound (mainland into HK), sector allocation tracking, and cross-border arbitrage 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。

Ichimoku Kinko Hyo five-line system signal engine. A standalone Japanese technical-analysis school that generates trading signals from Tenkan/Kijun crossovers, cloud position, and Chikou confirmation. Pure pandas implementation.

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。

Liquidation level analysis and heatmap interpretation — identify leveraged position concentration, liquidation cascades, stop-hunt zones, and use liquidation data as support/resistance signals.

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Geopolitical risk analysis: quantify crisis signals, identify precursors, and build event-driven strategies for war, sanctions, and supply disruption scenarios.

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Event-driven strategy based on sentiment-scored signals from news, announcements, and macro events. The LLM acts as the NLP engine, and event data follows a CSV schema.

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ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.

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SEC EDGAR filing analysis — 10-K, 10-Q, 8-K, proxy statements, insider Form 4. Extract key financials, risk factors, management discussion, and generate investment signals from US public company filings.

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Writes and tunes Falco rule syntax for container escape detection - conditions, macros, lists, priorities, and output fields - covering host filesystem mounts, sensitive host path access, kernel module loading, and privileged capability abuse, including how to drive down false positives. Use when authoring or tuning a specific Falco rule for breakout behaviour, or triaging a noisy escape-related Falco alert. Keywords: Falco rule, macro, list, condition, priority, falco_rules.local.yaml, tuning, false positive. Do not use for deploying and operating Falco itself - use detecting-container-runtime-threats-with-falco; for tool-agnostic escape signals use detecting-container-escape-attempts.

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。

Detects container escape at runtime across tooling - namespace manipulation, capability abuse, kernel exploits, sensitive host mounts, and anomalous syscalls - and explains which signals matter regardless of whether Falco, Sysdig, auditd, or an EDR is doing the collection. Use when deciding what breakout behaviour to monitor, investigating a suspected Docker or Kubernetes breakout, or comparing escape coverage across runtime sensors. Keywords: container escape, breakout, namespaces, CAP_SYS_ADMIN, privileged, hostPath, kernel exploit, syscall. Do not use for Falco rule syntax itself - use detecting-container-escape-with-falco-rules; for a static configuration sweep use performing-container-escape-detection.

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Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive patterns.

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Generic framework for converting external events (SMS, meetings, social mentions) into brain-ingestible signals. Define a transform function, register a webhook URL, and incoming events get processed through the brain pipeline.

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When the user corrects a factual error, root-cause it immediately. Don't just note the correction — trace the error to its source, fix the source, and prevent recurrence. Every factual error is either a data error (bad brain page, bad memory file, bad rendered SOUL/USER identity, bad facts row) or a hallucination (LLM confabulated from partial signals).

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Builds a ranked list of prospects worth contacting. Works out the owner's ideal customer profile from the customers they already have, finds look-alike companies and the right contact at each, enriches them with firmographics and buying signals, scores them, and delivers the list as an XLSX, with an offer to also create the records in the CRM. Runs on Apollo when connected and falls back to web research plus an uploaded customer list when it isn't. Use this whenever the owner wants new prospects, a target list, or help finding customers — including phrasings like "who should I be calling," "find me more customers like my best ones," "build me a prospect list," "I need leads," "who else looks like Acme," or "help me find companies in this area that need what we do." Reach for it even when the owner describes the customer they want rather than asking for a list.

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Prepare for a customer or prospect call using Common Room signals. Triggers on 'prep me for my call with [company]', 'prepare for a meeting with [company]', 'what should I know before talking to [company]', or any call preparation request.

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Research a company using Common Room data. Triggers on 'research [company]', 'tell me about [domain]', 'pull up signals for [account]', 'what's going on with [company]', or any account-level question.

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Wins back customers who have quietly stopped buying — finds the ones whose gap has stretched past their own normal rhythm or who are showing churn signals, ranks them by what the relationship was worth, drafts a win-back message per customer in the owner's voice, and sends and logs only what the owner approves. Chains review-reputation, outreach-composer, and crm-autopilot, and keeps the rule that the first touch acknowledges the gap without pitching. Use this whenever the owner is worried about customers drifting away, including phrasings like "who hasn't ordered in a while," "win back my old customers," "which customers have gone quiet," "we lost some regulars," "reach out to the people who stopped calling," or "how do I get them back." Reach for it when the owner names a customer they have not heard from, as readily as for a full win-back campaign.

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Generate personalized outreach messages using Common Room signals. Triggers on 'draft outreach to [person]', 'write an email to [name]', 'compose a message for [contact]', or any outreach drafting request.

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Scores inbound HubSpot leads by engagement signals, company fit, and urgency markers, ranks the ones worth a call today with talking points, drafts the follow-ups, and proposes calendar times for the owner to book. Use when the owner asks to prioritize, score, or rank leads, asks "who should I call first," or asks about their pipeline. An ask that wants the ranked leads turned into a call list with time blocked on the calendar routes to /call-list, which runs this skill first and then books the time.

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Health check on a customer account and QBR-ready prep - relationship strength, engagement trend, risk signals, value delivered, and the agenda that makes the review worth their time. Use when the user asks "how healthy is [account]", "customer health check", "prep the [account] QBR", "business review prep", or "are we at risk with [customer]".

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Score and tier a list of accounts (or your full book) against ICP fit and engagement signals to prioritize where to spend time. Use when the user asks to "tier my accounts", "prioritize my book", "which accounts should I focus on", or "score these accounts".

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。