'correctness' 검색 결과

Generate Isabelle or Coq proofs establishing partial or total correctness of imperative programs from code and formal specifications. Use when users need to: (1) Prove program correctness using Hoare logic, (2) Generate verification conditions from pre/postconditions, (3) Construct loop invariants and termination arguments, (4) Verify imperative programs with assignments, conditionals, and loops. Supports both partial correctness (if terminates, postcondition holds) and total correctness (terminates and postcondition holds) for both Isabelle/HOL and Coq.

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Approach engineering problems by checking assumptions, defining performance constraints, and choosing suitable tools before implementation. The skill guides algorithm analysis, complete code, performance optimization, and correctness checks through tests, assertions, or formal verification, meaning mathematical checks of correctness.

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• Checking algorithm complexity and design assumptions
• Choosing languages for numerical or distributed systems
• Implementing concurrent code with explicit error handling

Review Apache Pinot changes for correctness in null handling, numeric types, type-based processing, and resource lifecycles. Use it to examine changes to calculations, aggregations, window functions, and segment cleanup or persistence.

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• Reviewing changes to null handling
• Checking branches based on data or index types
• Reviewing numeric processing in aggregations and window functions

await 漏れ・floating promise・並行競合など、非同期処理の correctness バグを検出する。並列化の効率提案（SIMPLIFY Efficiency）や配線断点（e2e-wiring）ではなく、「await を忘れて結果・順序・エラー伝播が壊れる」実装バグに限定する

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Review code changes or a repository for actionable bugs by tracing how components interact. Findings explain reachable triggers, incorrect behavior, consequences, and checked counterarguments. Correctness is the default focus; performance and security can also be selected.

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• Reviewing changes for bugs before integration
• Investigating state mismatches across components
• Checking whether a fix introduces another defect

Review Apache ShardingSphere pull requests or authorized downstream changes using repository evidence. Assess code correctness, merge readiness, or CI failures, then produce consolidated findings or a ready-to-copy reply to a review discussion.

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• Review code changes for correctness and regression risks
• Assess whether a pull request is ready to merge
• Investigate CI failures

Automatically infer loop invariants for code verification and correctness proofs. Use when analyzing loops to identify properties that hold throughout execution, generating assertions for verification, proving loop correctness, or documenting loop behavior. Supports Python, Java, C/C++, and language-agnostic analysis. Generates invariants as code assertions (assert statements). Triggers when users ask to infer invariants, find loop properties, generate loop assertions, prove loop correctness, or verify loop behavior.

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Reviews audio DSP code for numerical correctness. Use when the user asks to review, audit, or check DSP code for correctness issues — filters, feedback loops, fixed-point arithmetic, accumulation loops, or any numeric computation in audio code. Trigger on "check this filter for NaN", "why does my reverb blow up?", "is this numerically stable?", "why do I hear DC in my output?". Pairs with audio-dsp-review (realtime safety); this skill covers numeric correctness. Flag issues proactively.

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현재 성능 측정, 가설별 변경안 비교, 정확성 검증으로 최적화를 진행합니다. 시간과 비용 한도 안에서 가장 빠르고 안전한 안을 고르고 재실행 절차를 남깁니다.

• 느린 작업을 측정하며 속도 개선
• 같은 입력 조건에서 구현안 비교
• 응답 시간, 처리량, 실행 비용 평가

Prepare GRPO reinforcement learning configurations for tasks with programmatically checkable answers, such as math, code, and structured output. Design and inspect reward functions, then select training variants when runs show instability or exploit flaws in scoring.

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• Preparing GRPO training for tasks with automatic correctness checks
• Designing rewards for output format and correctness
• Inspecting reward scores before training

Create and run evaluations in Arize using an LLM as the judge. Define scoring criteria, connect them to actual data fields, evaluate recorded operations or experiment results, and set up continuous scoring for new data.

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• Check answers for unsupported claims
• Score experiment results for correctness
• Continuously evaluate new production records

Review code changes for security, performance, correctness, and maintainability. Get findings tied to files and lines, severity ratings, practical suggestions with code examples, and a recommendation to approve, request changes, or discuss further.

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• Reviewing a pull request before merging
• Checking authentication and injection risks
• Finding excessive database queries and resource leaks

Verify a local OpenSEO MCP server, which exposes tools to AI agents, through real DataForSEO calls and a headless Claude trial. Check protocol correctness alongside tool discovery, input rules, response size, errors, and asynchronous task recovery.

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• Checking an MCP tool after adding or changing it
• Testing whether an agent can choose tools from their descriptions
• Verifying queued tasks and recovery using taskId

Review code changes for correctness, readable names, clear interfaces, and design problems. Examine duplication, hidden assumptions, and unnecessary abstractions, then report actionable findings with file locations, explanations, and concrete suggestions for fixes.

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• Review pull request changes
• Check names and function interfaces
• Find duplication and unclear responsibilities

Improve MNN's Vulkan backend by separating CPU scheduling bottlenecks from GPU computation costs. It guides operator and shader changes, new feature integration with compatibility fallbacks, and device testing to verify both correctness and end-to-end performance.

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• Finding CPU or GPU bottlenecks in Vulkan inference
• Optimizing a specific operator or shader
• Integrating cooperative matrix or subgroup features

Develop or port CPU kernels, the low-level calculation routines used by MNN. Follow staged implementation guidance, check data layout agreements, register functions, and verify correctness across processor instruction sets and numerical precision settings.

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• Implementing or porting an MNN CPU kernel
• Adding support for a processor instruction set
• Changing packed data layouts or tile sizes

Run TVM's TIRx compiler tests alongside tirx-kernels import and correctness checks. It verifies the local build, selects GPUs, runs parallel tests, saves full logs, and guides failure investigation and result reporting.

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• Testing TIRx compiler and kernel changes
• Checking tirx-kernels imports and registry entries
• Running parallel tests across selected GPUs

Fine-tune language models with TRL using GRPO, a method that compares scored responses to the same prompt. Covers training data, scoring functions, GPU-conscious settings, and monitoring for tasks with measurable correctness or required output formats.

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• Train models to follow JSON or XML output formats
• Reward correct answers in math or coding tasks
• Balance correctness, format, and style during training

Review code changes for security, performance, correctness, and maintainability. Get findings tied to files and lines, severity ratings, practical suggestions with code examples, and a recommendation to approve, request changes, or discuss further.

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• Reviewing a pull request before merging
• Checking authentication and injection risks
• Finding excessive database queries and resource leaks

Review a pull request using repository checks, verified code references, and current official documentation. Assess correctness, security, structure, and unnecessary code, then post one consolidated GitHub review with inline findings and an evidence-based verdict.

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• Review a pull request before merging
• Check code structure and unnecessary comments
• Verify dependency changes against official documentation

Use ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.

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Comprehensive adversarial audit of a theory, proof, math/econ paper, codebase, or set of claims — decompose into components, fan out independent skeptics that must return CONCRETE defects, adjudicate every finding with a separate judge, fix all confirmed defects, then re-verify. Use when correctness must be bulletproof and single-pass or round-by-round review is too slow and too shallow. Invoke for "audit this rigorously", "find ALL the bugs/gaps", "make this rock solid", "converge faster on correctness".

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Audit code for correctness and rigor — logic errors, silent error swallowing, wishful thinking, and unjustified fallbacks. This is not a style review; it's a logic review. Use when you want a focused correctness audit separate from the full code-police pass.

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Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — **ingestion must be idempotent: a document's content hash decides insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but the re-ingest-correctness contract is not surfaced.

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