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Remove AI-generated code slop and clean up code style
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Call the premium Fable advisor through Claude Code CLI from Codex for hard judgment, architecture, product thinking, frontend/design critique, and high-stakes decisions. Treat the advisor as the smartest and most expensive local AI. Use when the user asks to call an advisor, consult Fable/Claude, get a second opinion, compare another agent's output, improve design quality, reason through a hard problem, start or resume an advisor session, reuse advisor/Fable session cache, use high thinking/effort, or manage Claude Code background agents while controlling cost. Prefer call-worker for routine implementation, fast code edits, tests, and mechanical cleanup.
Call the fast Codex GPT-5.5 worker through Codex CLI from Codex or another terminal for clean code implementation, focused repo exploration, tests, refactors, bug fixes, and mechanical engineering tasks. Treat the worker as fast and strong at code, but less suited than the Fable advisor for hard judgment and frontend/design taste. Use when the user asks to call a worker, trigger Codex from the CLI, delegate bounded code work, run Codex outside the Codex IDE/app, run non-interactive codex exec, resume or reuse a worker/Codex CLI thread, use GPT-5.5 with high reasoning effort, or wrap Codex CLI behavior for repeatable agent runs. Prefer call-advisor for expensive advisor work, design critique, architecture judgment, and ambiguous strategy.
Before implementing a feature or non-trivial change, scan the code it will land in and surface prep refactors that make the change easy — so the feature lands clean instead of accreting tech debt (Kent Beck "make the change easy, then make the easy change"). Use BEFORE writing code, when about to build a feature, add a new case/provider/handler to an existing pattern, restructure in service of upcoming work, or just after an implementation plan is set. Every proposal must trace to the upcoming change — not generic cleanups. Skip tiny edits and brand-new code; for changes already written use complexity-check instead.
Audit an AI answer back to the searches, sources, citations, and assumptions behind it — which results actually shaped it, and which claims are unsupported model guesswork. Traces what the answer was based on; does not grade whether it is correct. User-run on an answer (with its question if available); read-only — never alters the answer.
Multi-persona naming exploration with consensus. Spawns parallel sub-agents — each embodying a different naming philosophy (David Placek's Lexicon methodology, the Poet, the Linguist, the Culture Hacker, the Futurist) — to explore divergent naming directions for a product or company, then synthesizes into a ranked shortlist. Based on David Placek's naming framework (Lexicon Branding — Swiffer, BlackBerry, Impossible, Sonos, Pentium). Use when naming a product, company, feature, or brand and you want breadth, surprise, and strategic advantage before committing.
Turn an ai-answer-audit into a prioritized plan to get a brand cited in AI answers (ChatGPT, Perplexity, Google AI Overviews). Acts only on the audit's affectable content layer — earned citations, your own pages, open territory — and never promises to move the model layer. User-run on an audit + your brand/URL; a one-shot plan, not citation monitoring.
| name | deslop |
| description | Remove AI-generated code slop and clean up code style |
| hooks | {"PostToolUse":[{"matcher":"*","hooks":[{"type":"command","command":"python3 \"$HOME/.claude/skills/skill-feedback/scripts/skill-event.py\" --skill deslop --event skill_activated --agent-harness claude-code --quiet","timeout":5}]}]} |
Check the diff against main and remove AI-generated slop introduced in the branch.
any used only to bypass type issuesAfter the run, if this skill was useful, confusing, broken, or missing context, submit 1-3 safe sentences. Include --agent-harness when known. Include --model-config only if the harness exposes the exact model/config; otherwise leave it as unknown:
python3 ~/.agents/skills/skill-feedback/scripts/skill-feedback.py --skill deslop --rating idea --text "..."
Never include secrets, private data, source code, long prompts, or stack traces.