Loading...
Loading...
Found 1,194 Skills
Suggests code simplification opportunities. Identifies extract method candidates, complex expressions, redundant code, refactoring opportunities.
Run an independent code review using the OpenAI Codex CLI in headless mode. Gets a second opinion from a different model family (GPT-5/o3) on recent changes, a PR, a commit, or the whole app — covering bugs, regressions, security, data consistency, UX/state bugs, performance risks, and testing gaps. Saves a severity-prioritised report to .jez/reviews/. Triggers: 'codex review', 'review with codex', 'second opinion on this code', 'independent code review', 'what does codex think', 'get codex to review'.
Self-directed iterative improvement system for Codex that cycles through modify, verify, retain/discard indefinitely
Perform SGLang code review in the style of human maintainers by consulting the 2024-2025 non-agent PR review corpus, including inline code snippets, original multilingual comments, and discussion threads. Use when reviewing SGLang PRs, diffs, patches, or local changes for correctness, tests, performance, GPU/runtime risks, API compatibility, and maintainability.
Use when drafting, rewriting, classifying, or improving code review comments as a reviewer. TRIGGER on rough review notes, requests to make comments clearer or kinder, severity labels, "how should I phrase this review comment?", "write review comments", author pushback, or turning findings into PR comments. If role is unclear, ask reviewer or author. DO NOT TRIGGER for full code review, PR descriptions, or author responses unless wording is requested.
Detect software architecture bad smells, algorithmic complexity hotspots, and anti-patterns in a codebase. Produces a detailed markdown report identifying violations of architectural principles, design patterns, code quality, and performance complexity. Triggers on: smell, code smell, architecture smell, find anti-patterns, detect bad smells, complexity analysis, 代码坏味道, 架构坏味道, 反模式, 找出坏味道, 复杂度分析.
Review GitHub pull requests with detailed, multi-perspective code analysis using parallel subagents. Use this skill whenever the user wants to review a PR, asks for code review on a pull request, mentions "review PR", "check this PR", "look at pull request", or references a PR number or GitHub PR URL. Also trigger when the user wants feedback on code changes, wants to approve or request changes on a PR, or asks to review someone's contribution.
Eight-axis judgment code review for the current diff — Correctness, Simplification, Tests, Documentation, Style, Intent, Design/API, Performance (+ Coherence on metadata changes). Five-phase pipeline scope → deterministic tool battery (npx/uvx-preferred, zero-install for the JS + Python majority) → 8 parallel LLM axis reviewers → Haiku validators on sub-80 findings (verbatim rubric, ≥80 threshold) → synthesis with no-silent-drop + Conventional Comments JSONL. Every report closes with "What I did NOT check" (security → /security-review, runtime perf, flaky detection). Opt-in flags `--verify-build`, `--mutation-test`, `--reconcile`, `--apply-safe`. Public-skill posture — zero auto-install, graceful skip on missing native tools.
Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.
Complete Python gotchas reference. PROACTIVELY activate for: (1) Mutable default arguments, (2) Mutating lists while iterating, (3) is vs == comparison, (4) Late binding in closures, (5) Variable scope (LEGB), (6) Floating point precision, (7) Exception handling pitfalls, (8) Dict mutation during iteration, (9) Circular imports, (10) Class vs instance attributes. Provides: Problem explanations, code examples, fixes for each gotcha. Ensures bug-free Python code.
Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Four LLM reviewers challenge changes from independent angles.