「metrics」の検索結果

Knowledge base from the DoD Digital Engineering Strategy (2018, OUSD/ODASD(SE)). Use for the Department of Defense's five digital engineering goals — (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; (5) transform the culture and workforce — plus their focus areas, the document-to-model and design-build-test→model-analyze-build shifts, model formalisms/provenance, governance and access control of the authoritative source of truth, and the coordinate→plan→pilot→sustain rollout. A vision-and-policy strategy, deliberately non-prescriptive. Does NOT provide a how-to method, tool tutorials, MBSE/SysML mechanics, or implementation-plan content; thin on metrics, acquisition-policy detail, and step-by-step procedure. Excludes the CAC-gated Digital Engineering Body of Knowledge (DEBoK).

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Knowledge base from the OUSD(R&E) guidebook Implementing a MOSA in DoD Programs (Feb 2025). Use for the Modular Open Systems Approach as a DoD acquisition + design discipline: the statutory/regulatory basis (10 U.S.C. 4401–4403, FY2017/FY2021 NDAA, DFARS), the five OUSD(R&E) pillars, the plan→modularize→identify→define→standardize interface lifecycle, technical levers (WBS/MIL-STD-881F taxonomy, standards profiling, API-first software, COTS/cybersecurity), MOSA assessment metrics and tools (PART/OAAT/KOSS/SEAM/MAUT), technology-change and DMSMS management, roadmaps, MOSA across the six AAF pathways, and contracting/IP/data-rights mechanics. SCOPE LIMITS: this is U.S. DoD weapon-system acquisition policy, not a general open-architecture or interface-standards reference — it names standards (FACE, OMS, DISR) without reproducing them, is light on detailed engineering of any specific standard, and does not cover the underlying SE processes (use dau-se-guidebook / nasa-npr-7123) or general systems-engineering theory (use sebok).

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Knowledge base from NASA Human Systems Integration (HSI) Practitioner's Guide (NASA/SP-2015-3709). Use for HSI process implementation, lifecycle phase activities, function allocation, ConOps development, HSI Plan writing, HSI metrics (MOE/MOP/TPM/KPP), HITL testing strategy, HSI team organisation, scaling/tailoring for program size, and V&V planning. Does not cover the specific human engineering design standards (NASA-STD-3001 content) or detailed HFE methods beyond what the guide itself provides.

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Summarises one sprint from the board export, sprint report and review or stand-up notes the user provides into a one-page DRAFT sprint review summary: the sprint goal as written, items delivered, items not delivered with the reasons the sources state, carry-over, impediments, metrics exactly as given, and numbered questions for the retrospective, every row referenced to a work item or note fragment and every missing fact marked UNKNOWN. Use when the user asks to "summarise the sprint", "write the sprint review", "what did we deliver this sprint", "list the carry-over and blockers" or "draft the sprint recap". Do not use for the retrospective write-up or themed lessons across several sprints, use lessons-learned-synthesis instead; for a weekly project status report, use project-status-tracker. Drafts for human review; never approves, authorises or signs off.

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Prepares the trend update for a recurring emailed report: identifies report emails not yet processed, extracts the configured metrics from their spreadsheet, CSV or PDF attachments, returns new rows for an append-only trends sheet and writes a DRAFT summary with period-on-period deltas and flagged anomalies. Use when the user asks to "update the trends for the weekly report", "analyse the latest monthly report", "what changed in the latest report", "do the latest numbers look unusual" or "set up tracking for a report that arrives by email". Do not use for a single file with no trend state, use dataset-insight-pack instead. Drafts for human review; never approves, authorises or signs off.

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Drafts a DRAFT self-assessment, peer feedback note or manager review narrative from the evidence the writer supplies (goals, examples, metrics, notes), in the review template where given: every sentence tied to a numbered evidence item, unsupported claims removed and logged, and a gaps list to complete before submitting. Never assigns, suggests or implies a rating, score, ranking, pay or promotion outcome. Use when the user asks to "write my self-assessment", "draft peer feedback for a colleague", "turn these notes into a performance review", "help me write the year-end review for my report", "summarise my achievements against my goals" or "draft the mid-year review". Do not use for a job description or role profile, use job-description-drafter instead; for a new hire's first 90 days, use onboarding-plan-builder. Drafts for human review; never approves, authorises or signs off.

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Drafts one anonymised DRAFT case study (situation, approach, outcome as evidenced, optional lessons) from engagement notes, status reports, closing reports, emails and metrics extracts the user supplies, with a fact trace to the notes, an evidence grade on every result, quotes only where the user supplied them with speaker role and permission, an anonymisation log and a confidentiality checklist that must be completed before publication. Use when the user asks to "write a case study from these notes", "turn this engagement into a customer story", "draft a success story for the website", "anonymise this project write-up" or "prepare a reference case for the proposal". Do not use for redacting identifiers from an existing document, use document-deidentification-pass instead; for checking whether a document's claims are substantiated, use claims-evidence-map. Drafts for human review; never approves, authorises or signs off.

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Prepares a DRAFT leadership-meeting briefing pack from the reports, meeting notes, emails and dashboard exports the user provides: one headline, metrics exactly as stated with period and source, decisions the meeting must take, risks as reported, talking points with anticipated questions, a source reference on every line and UNKNOWN where the material is silent. Use when the user asks to "brief the leadership team", "prepare the exec pack", "pull together a briefing for the steering committee", "summarise these reports for the CEO", "what do I tell the board on Monday" or "build the pre-read for the management meeting". Do not use for a formal board or committee paper seeking a resolution, use board-paper-skeleton instead; for a personal one-page prep drawn from the user's own calendar and inbox, use meeting-prep-onepager. Drafts for human review; never approves, authorises or signs off.

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Use for a focused high-craft polish pass after structure and art direction are stable; improves typography metrics, spacing rhythm, imagery crop, surfaces, optical alignment, micro-interactions, and restrained detail without redesigning the whole page.

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Produces marketing performance reports and data-driven recommendations from WooCommerce sales data, social media metrics, and campaign analytics. Use when the task asks for "campaign performance", "ROI analysis", "monthly report", "sales trends", "product velocity", "audience insights", or "KPI tracking". Covers metric definition, reporting templates, slow-mover detection, and insight-to-action frameworks.

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Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.

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Automate SEO research with Ahrefs -- analyze backlink profiles, research keywords, track domain metrics history, audit organic rankings, and perform batch URL analysis through the Composio Ahrefs inte

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Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.

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Use when designing an A/B or split test: define the hypothesis, control and variants, estimate sample size, verify tracking, and predeclare metrics and stopping rules.

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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or

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Design valid marketing experiments with hypotheses, primary metrics, guardrails, sample/traffic assumptions, implementation QA and decision rules.

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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.

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Ensure code changes keep logs/metrics/traces useful for debugging failures and regressions.

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Designs and reviews production observability for mobile apps on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers crash and error reporting with usable context, breadcrumbs, structured logs, traces and custom metrics, release and cohort segmentation, correlation with backend requests, alerting thresholds, sampling and cost, and what must never be collected. Use when production problems cannot be explained from the available data, when setting up crash reporting or telemetry, or when defining release health alerts.

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Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

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Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.

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Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Appli...

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Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.

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Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.

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。