3372 个 · 按相关度排序
Compare Google and Meta campaign results across mobile, desktop, and tablet. Identify device performance gaps, investigate whether ads, landing pages, or audiences explain them, and receive recommendations for bids, campaign splits, and page changes.
中文简介正在准备中。当前显示英语简介。
Analyze how long visitors take to convert and use those patterns to refine Meta retargeting, advertising to previous visitors. Provides recommended audience windows, confidence levels, spending breakdowns, estimated savings, and bid adjustments based on time since the visit.
中文简介正在准备中。当前显示英语简介。
Analyze Google and Meta campaign results by weekday and hour to recommend advertising schedules. Estimate spending reductions, conversions lost, and the impact of moving budget to better periods, while accounting for limited data.
中文简介正在准备中。当前显示英语简介。
Analyze successful Google and Meta ads to find useful patterns in headlines, body text, and calls to action. Generate new copy variants that preserve those patterns, with platform formatting, test priorities, and suggested pairings for comparison.
中文简介正在准备中。当前显示英语简介。
Review Google and Meta campaign structures against goals, budgets, and conversion data. Identify excessive segmentation, overlapping targeting, and fragmented budgets, then get consolidation recommendations, an explained account structure, and a migration plan.
中文简介正在准备中。当前显示英语简介。
Track Google and Meta ad spending against monthly budgets across accounts and campaigns. Project spending at month end, flag overuse or underuse, and calculate daily budget changes while accounting for weekday and weekend spending patterns.
中文简介正在准备中。当前显示英语简介。
Analyze spending, revenue, and conversions across advertising channels to recommend budget shifts. Compare overall efficiency with the revenue gained from additional spending, identify saturation, and produce a gradual reallocation plan with monitoring priorities.
中文简介正在准备中。当前显示英语简介。
Compare how different attribution models, or ways of assigning conversion credit, change campaign results across Google and Meta. The skill highlights shifts in credited conversions and return on ad spend, then suggests budget adjustments based on the comparison.
中文简介正在准备中。当前显示英语简介。
Detect unusual changes in Google and Meta advertising performance by comparing current metrics with recent averages. Get issues ranked by estimated financial impact, likely causes supported by related changes, and recommended immediate actions.
中文简介正在准备中。当前显示英语简介。
Find search terms, placements, audiences, and ads that spend money but generate few or no conversions in Google and Meta accounts. The skill prepares uploadable exclusion lists and groups spending by category to help prioritize account cleanup.
中文简介正在准备中。当前显示英语简介。
Compare current and previous Google and Meta campaign data to investigate rising cost per acquisition, the cost of each conversion. Receive contributing factors ranked by impact, affected campaigns or audiences, and prioritized fixes.
中文简介正在准备中。当前显示英语简介。
Capture the Unity Editor's Game View as a PNG image for an AI agent to inspect. The screenshot uses the current Game View resolution and corrects vertical flipping on DirectX and Metal to match the visible orientation.
中文简介正在准备中。当前显示英语简介。
Extract Markdown and metadata from PDFs, Office documents, emails, archives, and other formats using Apache Tika. Keep attachments and embedded items separate in structured output, detect parsing problems, and read selected portions without overwhelming the agent's context.
中文简介正在准备中。当前显示英语简介。
Connect paid advertising accounts in NotFair and establish reusable brand and campaign context. Prepare a new workspace for agent-assisted advertising work, or refresh existing context across Google, Meta, X, LinkedIn, Reddit, and TikTok Ads.
中文简介正在准备中。当前显示英语简介。
Create and edit Markdown or MDX documentation in Metabase's clear, conversational style. Put actions first, tailor explanations to readers, improve headings and links, check examples, and apply consistent formatting.
中文简介正在准备中。当前显示英语简介。
When the user wants a full-page metadata audit or implementation across discovery, search intent, title, description, canonical, robots, hreflang, Open Graph, Twitter Cards, code or CMS changes, build validation, rendered HTML, production verification, localization, or post-release observation. Also use for "metadata audit," "sitewide meta optimization," "metadata implementation," "meta robots," "hreflang," "viewport," or "charset." For an isolated title, description, heading, Open Graph, or Twitter Card task, use its specialist skill directly.
中文简介正在准备中。当前显示原文说明。
Review and adapt MetaX attention backends, MLA, sparse indexers, cache layouts, and their kernel wrappers against a target vLLM revision and the actually installed MetaX component APIs. Verify dispatch, supported configurations, and GPU numerical behavior. Use for attention adaptations outside vllm_metax/patch/; do not apply monkey-patch headers or patch audit requirements.
中文简介正在准备中。当前显示原文说明。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for cloud metadata services, instance identity, workload identity, link-local credential paths, role assumption, and metadata-to-privilege trust edges. Use when the user asks to inspect metadata-service access, instance credentials, pod or workload identity, link-local token paths, SSRF-to-metadata escalation, or explain how metadata-derived credentials turn into accepted cloud or control-plane privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
中文简介正在准备中。当前显示原文说明。
Fix silent metadata issues in RFDB node storage. Covers two traps: (1) metadata flattening — nested metadata fields become top-level after serialization, so node.metadata.field is undefined but node.field works. (2) reserved keys — fields named "type", "id", "name", "file", "exported" are silently stripped from metadata by _parseNode() to prevent overwriting top-level node fields.
中文简介正在准备中。当前显示原文说明。
Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types, including the Agent Script language for the new Agentforce Builder (hybrid-reasoning -> logic vs | prompt instructions, @actions/@subagent/@variables references, subagents). Use when creating agents in source control, writing or debugging Agent Script, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'write or debug Agent Script', 'how do I deploy an Agentforce agent using source control', 'convert an agent topic to a subagent', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship', 'Agent Script logic vs prompt instructions'. NOT for Apex-based agent actions — use agentforce/custom-agent-actions-apex. NOT for clicking the agent together in the Builder UI — use agentforce/agentforce-agent-creation.
中文简介正在准备中。当前显示原文说明。
Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types. Use when creating agents in source control, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship'. NOT for Apex-based agent actions (use custom-agent-actions-apex). NOT for UI-based agent creation (use agentforce-agent-creation).
中文简介正在准备中。当前显示原文说明。
Metabolomics pathway analysis — metabolite identification (HMDB, KEGG, ChEBI), pathway mapping (Reactome, KEGG, MetaCyc), disease associations, enzyme/gene linkage. Use for metabolite-to-pathway-to-disease connections, BridgeDb-based ID conversion, and integrating metabolomics with gene-level pathway analyses.
中文简介正在准备中。当前显示原文说明。
Designs and validates quantitative targeted metabolomics assays (MRM/SRM on triple-quadrupole, PRM on high-resolution instruments) to report absolute concentrations. Covers the internal-standard strategy (external cal -> global IS -> standard addition -> stable-isotope-labeled IS), weighted calibration judged by back-calculated %RE not R-squared, ion-ratio quantifier/qualifier confirmation, matrix-effect/recovery characterization, and ICH M10 method validation. Use when quantifying a closed panel of known metabolites with units, building or validating an LC-MS/MS assay, choosing an IS or calibration weighting, or judging whether a reported concentration is trustworthy. For untargeted feature detection see metabolomics/xcms-preprocessing; for group statistics see metabolomics/statistical-analysis; for flux/MID/tracing see metabolomics/isotope-tracing.
中文简介正在准备中。当前显示原文说明。
Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA/DIA (SWATH) raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MsdialConsoleApp console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics/xcms-preprocessing; for lipid annotation mode see metabolomics/lipidomics; for MSI-level confidence honesty see metabolomics/metabolite-annotation; for drift correction and QC see metabolomics/normalization-qc.
中文简介正在准备中。当前显示原文说明。