Results for “complexity”

Static code analysis and complexity metrics

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Use when an engineering plan needs pressure-testing for architecture, complexity, data, risk, and operability.

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Design tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing agent tool sets.

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Before committing any non-trivial change, dispatch the diff to a reviewer runtime via ATO (`ato dispatch <reviewer> --session <id>`), parse the numbered/severity-tagged findings, apply or defer each one with a recorded justification, then commit. Fights the "build passes therefore ship it" failure mode — what Garry Tan calls the AI agent complexity ratchet. Place in the v2.16 stack: this skill is the LAST gate. `ato-warroom` decides the design; `ato-mission` runs the multi-step work and produces the diff; `ato-review` checks the diff before commit. When the review is part of a Mission, dispatch the review with `--require-tools read_file,grep,git_diff,git_log` so the reviewer can walk the source itself instead of reasoning from a paraphrase (PR-1.5 tool surface). Receipts land in `execution_logs` and the Mission narrative. Fires automatically before commits touching public surface (CLI subcommands, Tauri commands, MCP tools, schema migrations, security boundaries) or whenever a diff exceeds ~50 LOC of behavior change.

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Before any material decision — code chunk, plan, strategy, design, scope cut, push to GitHub — convene a war-room. The session driver takes the CEO seat: frame the tradeoff, summon specialist seats from whatever agent roster the user has built, dispatch a cross-family voice via `ato dispatch` so priors actually disagree, decide. A failure-mode filter (wrong assumptions / overcomplexity / orthogonal edits / imperative-over-declarative — Karpathy's four are one good default, swap in your own) runs on every dispatch. Place in the v2.16 stack: war-rooms DECIDE before code starts; `ato-mission` EXECUTES the work between decisions (multi-step, goal-driven, persisted across days); `ato-review` VERIFIES the resulting commits. Use a war-room for the design verdict, hand the verdict to a Mission, review the merged result. Fires before: sending a code draft to the user as final, opening a PR, pushing to a remote-tracking branch, committing >50 LOC of behavior change, or delivering a plan or strategic recommendation as the final answer.

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Use SmokingGun to find complexity candidates and organize imported optimization evidence.

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Calculates CRAP (Change Risk Anti-Patterns) score for .NET methods, classes, or files. Use when the user asks to assess test quality, identify risky untested code, compute CRAP scores, or evaluate whether complex methods have sufficient test coverage. Requires code coverage data (Cobertura XML) and cyclomatic complexity analysis. DO NOT USE FOR: writing tests, general test execution unrelated to coverage/CRAP analysis, or general code coverage reporting without CRAP context.

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Pythinker Browser Extension lets AI control the user's real browser — navigate, click, type, read, screenshot, and interact with any website using the user's actual login sessions. Use this skill whenever the user wants to interact with websites, automate browser tasks, scrape web content, or perform any action requiring a real browser. Also use when the user mentions "browser", "webpage", "open URL", "screenshot", or asks to read/interact with any website. Use even for simple-sounding browser requests — the daemon handles all complexity.

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Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

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Review owned or authorized code for security using structure-first evidence: AST/structure maps, call graphs, complexity, Source/Sink flow, and defensive findings. Use when asked for security review, vulnerability review, AST structure map review, SAST triage, Source/Sink, taint flow, parser/scanner hardening, CI/CD security, LLM/agent tool boundary review, 脆弱性レビュー, 構造マップ, セキュリティレビュー.

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"Apply Bloom's revised taxonomy to classify learning objectives and design assessments across six cognitive levels. Use this skill when the user needs to write learning objectives at specific cognitive levels, align assessment with instructional goals, or evaluate curriculum for cognitive complexity distribution — even if they say 'how to write learning objectives', 'what level of thinking does this require', or 'higher-order thinking skills'.".

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Lightweight design smell review for modules and APIs—coupling, cohesion, naming, boundaries, and unnecessary complexity. Use before large refactors or when code feels hard to change. Complements gstack/review (diff-focused).

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Multi-language code quality validation (Python, JS/TS, Shell, SQL) — formatting, linting, type-checking, complexity analysis with auto language detection. Use when checking code quality or running linters. Trigger on "lint", "type-check", "quality check", "run the linters", "check code quality".

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Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. This skill should be used after implementing features or bug fixes to clean up code, when refactoring for readability, or when reducing unnecessary complexity. It launches the code-simplifier subagent provided by the code-simplifier@claude-plugins-official plugin.

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Estimates task effort by analyzing complexity, dependencies, historical velocity, and risk factors. Produces a structured estimate with confidence levels.

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Use when behavior-preserving frontend refactoring focuses on conditional complexity, repeated branches, guards, special cases, or internal invariants, and the selected technique needs family-specific mechanics, risks, and JavaScript/TypeScript/React/Vue adaptation.

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Transform reference photographs and visually dense images into sparse, recognizable, high-saturation flat-vector-style illustrations through semantic compression rather than literal tracing. Use when the user asks for photo-to-flat illustration, photo-to-vector-style redraw, 照片转扁平插画, 无渐变高饱和插画, iconification, visual simplification, composition-preserving abstraction, a consistent minimal illustration set, or revisions that remove gradients, glow, texture, blur, clutter, text, logos, numbers, or UI. Default to human-perceived semantic complexity 6 out of 10 or lower, fixed-HSB solid fills, crisp boundaries, and no gradients or light-halo effects.

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Install Tilt and scaffold or audit Tiltfile + tilt/ configurations with ecosystem-aware templates. Auto-detects Java/Gradle (Spring Boot), Next.js, Python/uv, and external infrastructure; recommends single-file or modular tilt/*.star + service-config.yaml + environments.yaml patterns based on project complexity. Bakes in production-context safety guards, PVC persistence toggles, JDWP debug ports, and optional Prometheus/Grafana/Traefik scaffolds. Use when user runs /devtools:tilt-setup, mentions "tilt setup", "setup tilt", "scaffold tiltfile", "audit tiltfile", "tilt configure", "tilt project setup", "Tiltfile", "local kubernetes dev", or "kubernetes development environment".

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Install Taskfile (task) and scaffold or audit Taskfile.yml configurations with ecosystem-aware templates. Auto-detects Node.js/pnpm, JVM/Gradle, Python/uv, Docker, and recommends single-file or multi-file (includes) patterns based on project complexity. Use when user runs /devtools:taskfile-setup, mentions "taskfile setup", "setup task runner", "audit taskfile", "scaffold taskfile", "add taskfile", "taskfile.yml", or "developer tasks".

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Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

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Code simplification suggestions — clarity, unnecessary complexity, redundant abstractions. Advisory only.

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Inventory and prioritize all technical debt in a codebase. Scans for TODO/FIXME/HACK markers and stale comments, outdated and deprecated dependencies with CVE detection, high-churn files and cyclomatic complexity hotspots, duplicated code blocks.

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Analyze game code for performance bottlenecks including draw call batching and overdraw, shader complexity and LOD strategy, per-frame GC allocation pressure, object pooling gaps, physics timestep and collision matrix tuning, spatial partitioning for entity queries.

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Score your codebase 0-100 across complexity, coupling, cohesion, test coverage, documentation, churn hotspots, dependency health, and lint/type safety. Triggers: 'how healthy is this codebase', 'check code quality', 'score my project', 'find tech debt hotspots'.

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