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Plan refactoring with dependency analysis, which shows how code parts rely on each other. Find candidates and unreferenced code, preview and apply renames, and compare the resulting impact with the original plan.
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Convert laboratory instrument files into Allotrope Simple Model (ASM) JSON, a standardized data format, or tabular CSV. Detect instrument types, validate converted data, and export Python parsing code for analysis workflows or handoff to data engineers.
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Analyze single-cell data with scvi-tools, using deep learning to combine datasets, correct differences between experiments, and transfer cell labels. Model guides and reusable scripts cover RNA, protein, chromatin accessibility, spatial data, and downstream analysis.
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Implement, fix, refactor, or remove repository code through a defined workflow. It connects each change to an acceptance criterion, checks effects on existing behavior, and guides focused testing, change review, and verification.
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When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.
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Process SARIF files, a standard format for static analysis results, to filter alerts, remove duplicates, combine findings from multiple tools, and produce reports. It also supports comparing runs and integrating result checks into CI/CD pipelines.
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Find classes left in parent folders or namespaces and identify nested component structures. Compare consolidation, splitting, and shared code extraction, then plan and carry out refactoring with updated references and tests.
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Review agent skills before installation or during use by inspecting their files without running code. Local checks identify risks such as credential theft, permission abuse, and differences between stated purpose and actual behavior.
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Add, repair, and strengthen tests for existing code using the project's testing framework. Covers regression cases, boundary conditions, flaky tests, and coverage gaps, with validation that links requested behaviors to specific tests.
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Interpret .NET coverage reports, which show code exercised by tests, to explain missing coverage and stalled progress. Calculate the gains needed to reach a target and, when requested, combine coverage with complexity to rank risk.
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Access TeamAI workflows for sharing skills, rules, documents, and environment settings across AI coding tools. This entry skill loads version-matched guidance for team setup, synchronization, codebase knowledge, and sharing lessons from a session.
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Add custom Go code checkers to gh-aw, with guidance on tests, registration, and running all checks. Handle equivalent syntax consistently and optionally limit performance rules using test coverage, which records which code tests execute.
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Develop and check research code in MATLAB/Octave or Python for signal processing, image analysis, statistics, and simulations. This skill supports reproducible workflows, checks units and assumptions, and prepares figures for papers and presentations.
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Write MATLAB or GNU Octave scripts for matrix calculations, data analysis, signal processing, and scientific plots. The skill also helps explain syntax and functions, convert code between MATLAB and Python, and run scripts when an interpreter is installed.
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Inspect scientific data files to understand their format, structure, and quality. Covering over 200 formats, this skill produces Markdown reports with statistical summaries, potential issues, and recommendations for preprocessing, visualization, and further analysis.
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Choose how to handle repository initialization, code review, and security review requests. Start with Claude’s built-in commands for ordinary tasks, and route requests for multiple opinions, adversarial analysis, or specific providers to Octopus.
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Guide for running, interpreting, and fixing code style and analysis violations in grails-core using GrailsCodeStylePlugin, GrailsCodeAnalysisPlugin, and GrailsViolationAggregationPlugin - covering CodeNarc, Checkstyle, PMD, SpotBugs, and JaCoCo
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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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Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
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Master binary analysis patterns including disassembly, decompilation, control flow analysis, and code pattern recognition. Use when analyzing executables, understanding compiled code, or performing static analysis on binaries.
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Clones and audits competitor repositories into evidence-based intelligence with file:line citations. Use to track competitors, add a competitor repo, review competitor code, compare capabilities, build a competitor landscape, or check for code updates: 竞品分析 / 竞品 / competitor scan / analyze competitor / compare with X / latest competitor code. Not for market research without code (use deep-research).
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Multi-path parallel product analysis with cross-model test-time compute scaling. Spawns parallel agents (Claude Code agent teams + Codex CLI) to explore product from multiple perspectives, then synthesizes findings into actionable optimization plans. Can invoke competitors-analysis for competitive benchmarking. Use when "product audit", "self-review", "发布前审查", "产品分析", "analyze our product", "UX audit", or "信息架构审计".
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Orchestrate Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.
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Automatic code quality and best practices analysis. Use proactively when files are modified, saved, or committed. Analyzes code style, patterns, potential bugs, and security basics. Triggers on file changes, git diff, code edits, quality mentions.
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