Results for “measured-boot”

Guides test-driven development for Spring Boot services using JUnit 5, Mockito, MockMvc, Testcontainers, and JaCoCo to achieve 80%+ coverage.

Track and visualize ML training experiments with Trackio, including logging metrics, firing alerts, and retrieving data via CLI. Supports real-time dashboards, webhook alerts, and HF Space syncing.

Test-driven development for Spring Boot using JUnit 5, Mockito, MockMvc, Testcontainers, and JaCoCo. Use when adding features, fixing bugs, or refactoring.

Detect bootkits such as BlackLotus and Bootkitty and verify Secure Boot bypass via DBX and binary checks.

Guides test-driven development for Spring Boot services with JUnit 5, Mockito, MockMvc, Testcontainers, and JaCoCo, covering unit, web, integration, and persistence tests.

Track elapsed time from a set epoch with tamper-evident locking, supporting count-up, count-down, and count-between modes, milestones, and email notifications.

Read-only audit of product-analytics instrumentation: event taxonomy, funnel completeness, consent-gated firing, dead/duplicate/phantom events. Use when "audit our analytics", "are we tracking the right events", "funnel instrumentation", or before iterate-post-launch. PostHog/Amplitude/Mixpanel/GA4.

Test-driven development for Spring Boot using JUnit 5, Mockito, MockMvc, Testcontainers, and JaCoCo. Use when adding features, fixing bugs, or refactoring.

Builds SOC performance metrics and KPI tracking dashboards measuring Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), alert quality ratios, analyst productivity, and detection coverage using SIEM data.

Spring Boot 3.x - Java framework for production-ready applications with dependency injection, REST APIs, data access, security, and actuator monitoring

使用JUnit 5、Mockito、MockMvc、Testcontainers和JaCoCo进行Spring Boot的测试驱动开发。适用于添加功能、修复错误或重构时。

Build monitoring dashboards that answer real operator questions for Grafana, SigNoz, and similar platforms, turning metrics into actionable operational views.

Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.

Measure pleasur.ai's AI-citation share across ChatGPT / Perplexity / Gemini / Google AI Overviews for the tracked query list, using SemRush (data API for AIO-presence screening + AI Toolkit via browser for share-of-voice / mention + citation gaps) and direct engine queries. Scores each query into the GEO ledger and surfaces the highest-leverage gaps to fix. The GEO Lead's core measurement loop.

Use to audit a customer-facing support bot or AI agent for harm rather than for volume — manipulation and prompt-injection attempts, customers stranded without a human, fabricated answers, and unsafe commitments or disclosures. Trigger for "are people trying to jailbreak our bot", "show me attempts to manipulate the assistant", "is our bot giving wrong answers", "customers stuck with the bot and never got a human", or reviewing an AI agent before or after launch.

Gitリポジトリのコード行数・テストケース数を正確に計測しSSOTに記録。「本当にファイルを読んで計測した」ことを3層で証明（推測禁止）。 「/code-metrics」「コード行数を計測して」「テストケース数を計測して」「ポートフォリオの数字を確認して」「実績数値を実測して」「自己PRの数字を確認して」等で発火。経歴書の数字確認や過去計測の更新にも。

This skill should be used when the user asks to "create an index", "build a basket of top assets", "buy a weighted basket", "make a portfolio of assets", "equal-weight basket", "rebalance my portfolio", "track the top N tokens", or wants an automated, weighted multi-asset basket that buys in one pass and rebalances on a cadence. Builds the basket spec, delegates each buy and rebalance swap to the swap-integration Trading API flow, and records target weights in state.

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

Use to measure whether a support bot, AI agent, or self-service channel actually reduces contact volume, and to audit a vendor's containment or deflection number. Trigger for "what's our real deflection rate", "is the bot working", "our containment rate is 70% but tickets haven't dropped", automation ROI, self-service savings, AI agent resolution rate, or designing a holdout test for a support bot.

Test-driven development for Spring Boot using JUnit 5, Mockito, MockMvc, Testcontainers, and JaCoCo. Use when adding features, fixing bugs, or refactoring.

Read-only audit of a codemod or bulk mechanical transform for behavior-preservation — compiles/lints is not same-behavior. Use when "did this codemod break anything", "audit this bulk refactor", or before merging a mass find-replace. Diff quality → audit-code-review. SQL → plan-data-integrity.

Implement bounded remaining work for an issue-driven repository change after an authoritative verifier reports concrete gaps. Use when a workflow supplies an issue brief or assessment plus materialized gate-result and remaining-work evidence, and the existing candidate must be corrected without creating a MoonSpec feature packet.

Edit skeleton bone transforms/hierarchy, retargeting modes, curve metadata, blend profiles, bone constraints, and learn constraints from animations (VibeUE SkeletonService). Socket CRUD, bone enumeration, physics-asset, and material/LOD inspection are owned by the engine SkeletalMeshTools. Use when the user asks to add/transform bones, set retargeting, manage blend profiles or curves, or set bone constraints. For animation bone edits, also load animation-editing.

Validate monorepo lane specs before dispatch and verify integrated changes stay inside declared lane boundaries

为 Grafana、SigNoz 等平台构建能够回答实际运维人员问题的监控仪表板。适用于将指标转化为可用的仪表板，而非华而不实的展示板。

Train, evaluate, export, and run inference for monocular depth estimation models using Metric Depth Anything v2 or Relative Depth Anything architectures via the TAO toolkit.

Configure fault tolerance, straggler detection, preemption, in-process restart, and re-run state machine for Megatron Bridge training jobs.

Clade goal-driven autonomous improvement loop (Blueprint architecture — deterministic pre/post phases + LLM supervisor/worker nodes, converges when goal met or max-iter hit). NOT the Claude Code built-in /loop (which polls a prompt on an interval like `/loop 5m /foo`) — if the user wants interval polling, route to the built-in.

Operate and extend barretlee/agent-pulse, the evidence-backed AI industry intelligence system: inspect source catalog and lifecycle, collect and normalize signals, bind evidence, cluster Events, evaluate system health, generate Scout hypotheses, export the privacy-safe public site, and verify release gates. Use when the user asks to run, configure, debug, extend, or explain Agent Pulse, its collectors, Control Room, narratives, Scout, or GitHub Pages output. Triggers on: agent-pulse, Agent Pulse, evidence-backed intelligence, source catalog, signal collection, Event clustering, source audit, Scout opportunity, public export, weekly brief, or AI industry intelligence pipeline.

Measurement-driven model router for Claude Code. Routes Haiku/Sonnet/Opus per task class, logs every routed task with real $ numbers, and A/B tests cheaper tiers before you trust the savings.

Audit server-side paid-media measurement, including tag management, conversion APIs, event taxonomy, deduplication, consent, hashing, data quality, and observability.

Provides a local-first agent framework for observable execution, composable tool use, and multi-agent orchestration with policy-guarded execution.

Executes Atomic Red Team tests mapped to MITRE ATT&CK techniques, performs coverage gap analysis, and runs detection validation loops to measure blue team visibility.

Score the experience a new customer actually has, not just whether they activated. Grades the first 90 days on the moments that make someone feel taken care of, finds where the welcome goes cold, and hands back a scored audit with the specific fixes. Built for any post-sale team, customizable to your product and lifecycle. Trigger on "audit my onboarding", "score our onboarding experience", "why do new customers churn early", "grade our first 90 days", "where does onboarding go cold", "is our welcome any good", or any onboarding-quality question.

Run metric-driven iterative optimization loops -- define a measurable goal, run parallel experiments, measure each against hard gates or LLM-as-judge scores, keep improvements, and converge on the best solution. Use when optimizing clustering quality, search relevance, build performance, prompt quality, or any measurable outcome that benefits from systematic experimentation.