「data-collection」の検索結果

Trigger on-chain data collection (a "Commit") in Tres Finance for wallets that have already been onboarded. Use this skill whenever the user wants to collect data, pull balances, sync wallets, refresh on-chain data, run a commit, trigger a commit, or "collect" anything in Tres. This is the second step of the Tres onboarding flow — it sits between wallet upload (tres-wallets-upload) and balance validation (tres-asset-balance-validation). Always trigger this skill for any request that mentions "commit", "collect", "data collection", "pull data", "fetch on-chain data", "sync wallets", "refresh balances", or anytime the user just finished uploading wallets and is ready to bring in their on-chain data.

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。

End-to-end onboarding of a new entity, customer, or account into TRES Finance. Orchestrates 8 steps in order: wallet upload, data collection (commit), balance validation, reconciliation, cost basis, export unidentified addresses, import contacts, and rollup rules. Trigger ONLY when the user explicitly wants to onboard a new entity, customer, or account — e.g. "onboard a new entity", "onboard new customer", "start onboarding for [name]", "set up a new account in TRES", "run the full onboarding flow", "I need to onboard [company name]", "new entity onboarding". Do NOT trigger for individual tasks — those have their own dedicated skills. If the user asks to "add wallets" without mentioning onboarding, use tres-wallets-upload. "Validate balances" alone is tres-asset-balance-validation. "Run a commit" alone is tres-data-collection-commit. Only trigger when the user wants the full pipeline.

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。

Provides GDPR Article 13 information at the point of direct data collection, covering all required elements under Art. 13(1)(a)-(f) and Art. 13(2)(a)-(g), layered notice design, and timing requirements. Activate for Art. 13, direct collection notice, privacy notice at collection, data collection information queries.

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。

Writes or reviews a pre-analysis plan before data collection — registry choice (OSF, AEA RCT Registry, AsPredicted, registered reports), PAP structure, models and decision rules locked in advance, analysis code run on simulated data, contingencies for attrition, failed manipulations, and exclusions, deviation records, and timeline. Operationalizes the pre-data-collection side of DA-RT. Use when the user asks to draft or audit a pre-registration or PAP, choose a registry, decide what to lock versus leave exploratory, or handle a later deviation. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-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。

Run an end-to-end GEO visibility measurement through a real AI interface, persist the captures, and return a portable JSON run artifact plus optional PDF/dashboard outputs. Use automatically on an explicit request to measure a brand's AI-search visibility, and as a composable data-collection step inside another agent workflow; the user should not have to launch the pipeline or dashboard manually.

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。