“reliability”的搜索结果

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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Your software-engineering brain trust. Named personas across 11 cells covering cloud architecture, reliability/SRE/observability, data and storage, security, FinOps, languages and runtimes, systems programming, web and frontend…

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Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed

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Operate managed Aeon instances from memory/instances.json - health-check, dispatch, and status snapshots (control), plus a fleet scorecard of runs, tokens, cost, and reliability (scorecard).

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Implements transactions, idempotency and interrupted-operation recovery; not schema migration or a mandatory background-job platform.

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Measures whether an automation suite builds release confidence via feedback-loop length, suite reliability, release cadence, and production escape rate, and emits a release_confidence verdict. Use when judging suite value, ROI, or pre-release trust; not for writing or healing tests.

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Write or repair Elixir tests with ExUnit, sandbox isolation, async reliability, Mox, ExMachina, and LiveViewTest. Use for test files, test setup, or failing/flaky tests. NOT for investigating an application bug outside the test suite.

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When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.

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Run and extend the repository's recommendation-system projects covering the ranking funnel, two-tower ANN, point-in-time feature consistency, and online serving reliability.

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Use when reviewing, designing, or modifying Java enterprise systems and need a structured, repeatable ISO/IEC 25010:2023 quality-attribute review covering Functional Suitability, Performance Efficiency, Compatibility, Interaction Capability, Reliability, Security, Maintainability, Flexibility, and Safety, producing an engineering review report rather than an interactive ADR discovery. Part of Plinth Toolkit

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Claude Skills meta-skill: extract domain material (docs/APIs/code/specs) into a reusable Skill (SKILL.md + references/scripts/assets), and refactor existing Skills for clarity, activation reliability, and quality gates.

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Expert payment integration specialist mastering payment gateway integration, PCI compliance, and financial transaction processing. Specializes in secure payment flows, multi-currency support, and fraud prevention with focus on reliability, compliance, and seamless user experience.

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Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

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Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications. Use when implementing error handling, designing APIs, or improving application reliability.

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Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

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Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting. Pick tests, verify assumptions, or format results for publication. Covers frequentist (t-test, ANOVA, chi-square, regression, correlation, survival, count, reliability) and Bayesian. Use statsmodels or pymc-bayesian-modeling to fit.

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Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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Use when designing a study that benchmarks AI systems against a human-expert panel, before data collection. Plans the arms, decoupled rubrics with anchors, planted calibration probes, reviewer panel, inter-rater reliability targets and LLM-as-judge versus human adjudication.

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Applies NASA Power of 10 rules for safety-critical verifiable code. Use when auditing financial, medical, or high-reliability system code.

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Position an AI product when everyone claims AI and skeptics call it "just a wrapper." Find the real wedge (data, workflow, trust, domain), make reliability the differentiator, answer "won't the big labs just build this," and stop leading with "AI-powered." Use when your AI or dev tool blends into a sea of similar demos, buyers doubt the accuracy, or you can't say why you win when the model is a commodity.

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Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.

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

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Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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