“complexity”的搜索结果

Use when you need CRAP score and coverage risk analysis to find high-risk code before refactoring or release. Keywords: CRAP score, risk hotspots, cyclomatic complexity, test coverage gaps, coverage threshold.

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Review+fix protocol with optional pre-core git diff review and safety guardrails (unsoundness, invariants, footguns, incidental complexity). Use when prompts say "/tw-fix this PR", "fix current branch", "fix this diff", "repair CI red", or "apply a minimal patch", and when crash/corruption/invariant-break issues need correction with a validation signal. Stop only after self-review exhausts actionable changes, each full cycle reaches post-self-review rerun plus terminal diff-review closure, and two consecutive zero-edit full cycles are clean.

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Audit over-engineered codebases by factoring layers into live obligations, quotienting redundant distinctions, ablating unearned surface, and normalizing survivors while preserving required behavior. Use when change latency or agent difficulty comes from frameworks, plugins, DI, codegen, task runners, config indirection, ORMs, GraphQL, monorepo/infra tooling, or web stacks, or when asked to remove layers. Produces an evidence-backed Reduction Certificate, cuts, migration phases, proof signals, and rollback. Not for one local readability cleanup (use tw-complexity-mitigator).

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Modelo de espaço de estados com complexidade O(n) versus O(n²) dos Transformers. Inferência 5× mais rápida, sequências de milhão de tokens, sem cache KV. SSM seletivo com design aware de hardware. Mamba-1 (d_state=16) e Mamba-2 (d_state=128, multi-head). Modelos 130M-2.8B no HuggingFace.

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Híbrido RNN+Transformer com inferência O(n). Tempo linear, contexto infinito, sem cache KV. Treina como GPT (paralelo), infere como RNN (sequencial). Projeto Linux Foundation AI. Produção em Windows, Office, NeMo. RWKV-7 (março de 2025). Modelos até 14B parâmetros.

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Analyze a screen recording of a manual process and produce targeted, working automation scripts. Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation at multiple complexity levels using tools already installed on the user machine. Use when someone shares a screen recording of a repetitive task they want automated.

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Refactors code surgically without changing behavior: extract functions, rename, break up large functions and classes, reduce complexity, and remove code smells. Use when code is hard to understand or extend, or the user asks to clean up, refactor, or improve existing code.

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This skill should be used when the user asks to "design agent tools", "create tool descriptions", "reduce tool complexity", "implement MCP tools", or mentions tool consolidation, architectural reduction, tool naming conventions, or agent-tool interfaces.

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Analyze one GitHub issue for root cause, complexity, and risk into .idd/analysis-N.json. Use when you need to analyze or scope issue #N. Don't use for creating issues (/issue-creator), triaging (/issue-triage), or resolving (/issue-resolver).

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Use when a concept is too abstract for the learner's current level and needs analogy-first teaching before complexity re-injection.

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Namespace router → quality and complexity skills. Use to pick the quality-check intent before invoking the matched concrete skill directly.

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Strip designs to their essence by removing unnecessary complexity. Great design is simple, powerful, and clean. Use when the user asks to simplify, declutter, reduce noise, remove elements, or make a UI cleaner and more focused.

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Strip designs to their essence by removing unnecessary complexity. Great design is simple, powerful, and clean. Use when the user asks to simplify, declutter, reduce noise, remove elements, or make a UI cleaner and more focused.

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A six-year-old code reviewer who asks "why?" about everything. Catches over-engineering, confusing names, and unnecessary complexity. Output is a 1-2 line summary of the request as a whole: how is the file? Is the class nice? Is the function correct?

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A six-year-old code reviewer who asks "why?" about everything. Catches over-engineering, confusing names, and unnecessary complexity. To invoke, say `6yo`.

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A six-year-old code reviewer who asks "why?" about everything. Catches over-engineering, confusing names, and unnecessary complexity, then corrects the code according to the language spec, but through kids eyes, maintaining simplicity and readability.

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Review someone else's PR through an architectural/strategic lens — does it shift design, raise complexity, or quietly change precedent? Drives a brief reflective discussion with the user, then posts a concise review comment via `gh`. Defers line-level and bug-hunting concerns to AI review bots and the PR author.

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Use for AST/dep graph/complexity analysis of Python code.

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Check code for standardization, readability, maintenance complexity, and defensive coding practices

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Plain language directive. Cuts verbosity, corporate jargon, and overused AI vocabulary. Use when you want clear, direct, short writing. No fluff, no hedging, no unnecessary complexity.

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Create an Estuary derivation that continuously aggregates metrics (daily totals, running counts, min/max, lifetime revenue) using reduction annotations. Use for real-time dashboards, continuous materialised views, and replacing batch ETL aggregations with streaming. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "aggregation derivation", "derivation to sum daily totals", "running-count derivation", "transformation for min/max per group", "derive lifetime metrics", "aggregate-by-date derivation", or "real-time-dashboard derivation".

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Write Estuary derivations in Python — async-generator transforms, Pydantic-typed source/output, optional persistent state, and dependency installation via uv. Use for ML/embeddings, async API calls, or any transform where SQL and TypeScript are awkward. Derivations add complexity and cost (and Python derivations require a private/BYOC data plane) — confirm the user wants a Python derivation before reaching for this. Use when user says "python derivation", "write a derivation in python", "embed with a python derivation", "ML derivation", "call an API from a derivation", or anything mentioning model2vec / huggingface / pydantic in a derivation context.

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Create an Estuary derivation that flattens a nested array into one output document per array element. Use for normalising nested JSON, exploding line items, unpacking tags, or producing one-row-per-item tables from arrays. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (some destinations can `UNNEST` server-side without one). Use when user says "flatten-array derivation", "derivation to explode an array", "transformation to unnest", "derive one row per item", or "flatten nested data with a derivation".

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Create an Estuary derivation that joins two or more collections on a shared key, merging their fields into a single enriched collection. Use for data enrichment, denormalisation, building wide tables for BI, and real-time CDC joins. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (a destination-side SQL join or BI tool view is often simpler). Use when user says "join-collections derivation", "derivation to join two collections", "transformation to enrich with customer data", "derive a wide table", "lookup-join derivation", or "build a join derivation".

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