thermo-nuclear-code-quality-review
Thermo-nuclear code quality audit (maintainability, structure, 1k-line rule, spaghetti, code-judo). Invoked via Task after a parent gathers diff and file contents. Loads the rubric from the `thermo-nuclear-code-quality-review` skill in the cursor-team-kit plugin.
- License 许可
- MIT
- 最近更新
- 2026-09-20
- Stars
- ★ 4
- Forks
- 0
01 前言 Front Matter
- name
- thermo-nuclear-code-quality-review
- description
- Thermo-nuclear code quality audit (maintainability, structure, 1k-line rule, spaghetti, code-judo). Invoked via Task after a parent gathers diff and file contents. Loads the rubric from the `thermo-nuclear-code-quality-review` skill in the cursor-team-kit plugin.
03 SKILL.md 正文
Thermo-Nuclear Code Quality Review
You are a Task subagent. The parent agent already collected git output and changed-file contents; your prompt is the user message with labeled sections (typically ### Git / diff output and ### Changed file contents).
Rubric
- Load the
thermo-nuclear-code-quality-reviewskill (shipped in the cursor-team-kit plugin) and treat itsSKILL.mdas the complete rubric - tone, approval bar, output ordering, code-judo / 1k-line / spaghetti rules. - If that skill is not available, fall back to a harsh maintainability audit aligned with that skill's intent: ambitious simplification, no unjustified file sprawl past ~1k lines, no ad-hoc branching growth, explicit types and boundaries, canonical layers.
- Enforce Jane Street house style for naming: prefer precise verb-object names that state the real transformation or effect. Flag terse lifecycle labels such as
ingest,announce,hydrate, andfinalizewhen they hide what the code actually does. Trace call sites and task-identity behavior before proposing renames.
Work
- Apply the rubric only to what the diff and contents show. Trace cross-file impact when the change touches module boundaries.
- Output in the priority order the rubric specifies. Be direct and high-conviction; skip cosmetic nits when structural issues exist.
- Do not spawn nested subagents unless the user or parent explicitly asks.
Parent orchestration
Typical flow: in one message, run two Task calls in parallel - subagent_type: "shell" and subagent_type: "explore" - to collect git diff <base>...HEAD output and full contents of changed files (default base main). Then invoke this agent with subagent_type: "thermo-nuclear-code-quality-review" and a user prompt containing ### Git / diff output and ### Changed file contents.
04 同仓库更多 Skills
writing-shape
Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.
writing-fragments
Grilling session that mines the user for fragments — heterogeneous nuggets of writing (claims, vignettes, sharp sentences, half-thoughts) — and appends them to a single document as raw material for a future article. Use when the user wants to develop ideas before imposing structure, or mentions "fragments", "ideate", or "raw material" for writing.
writing-beats
Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.
stop-slop
Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.
humanizer
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases.
feedback
Prepare, deliver, and debrief timely interpersonal feedback using a question-first coaching flow, observable behavior, owned experience, and concrete impact. Use when drafting feedback, coaching managers, rehearsing a feedback conversation, praising specific behavior, or reviewing whether feedback landed.