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Orchestrates `impeccable` for every design task inside a workflow phase. Captures intent in natural language, classifies it (design / development / copy), translates design intents into a sequence of impeccable subcommand invocations, runs them in main context with state persistence at `.claude/state/design/<slug>.json`, and returns a structured report. ALWAYS invokes `impeccable` under the hood for the underlying design move; never picks aesthetic direction itself; never writes product code directly. Per CLAUDE.md Article X.2, all design tasks in workflow phases route through this skill.

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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, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

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Bring documentation prose down to a high-school reading level without losing precision. Runs after the content is written and before humanizer. Targets grade 9 on four measures — sentence length, vocabulary, clause load and jargon density — and carries the rewriting moves that actually shift them.

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Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when deciding whether to maintain or sunset existing code.

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Audit and rewrite content to remove 21 categories of AI writing patterns with a 43-entry replacement table

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Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).

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Provide comprehensive techniques for attacking Microsoft Active Directory environments. Covers reconnaissance, credential harvesting, Kerberos attacks, lateral movement, privilege escalation, and domain dominance for red team operations and penetration testing.

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Rewrite AI-sounding prose so it reads as considered human writing — cut the tells (em-dash pile-ups, "it's not X, it's Y" antitheses, inflated vocabulary, uniform rhythm, empty uplift) while preserving every fact, claim, and the author's own voice. Use when the user says "make this not sound like AI", "de-AI this", "humanize/humanise this", "this reads like ChatGPT", "remove the AI tells", "it sounds robotic/generic", or shares a draft (post, email, essay, docs, cover letter) to sharpen into a more human voice. Ships a stdlib scanner that flags the mechanical tells.

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Analyze Amazon product, sales, Amazon Ads, retail-readiness, and Best Seller Rank data to find commercially actionable growth opportunities. Use when Codex needs to identify which ASINs, campaigns, keywords, search terms, product targets, placements, categories, or BSR movements deserve more investment, optimization, harvesting, bid/budget changes, retail-readiness fixes, investigation, protection, or controlled scaling across Sponsored Products, Sponsored Brands, Sponsored Display, search term reports, targeting reports, campaign reports, placement reports, advertised/purchased product reports, Business Reports, organic performance, and BSR history.

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Use for read-first Amazon Sponsored Products optimization, product-aware growth, preflight readiness, and post-change monitoring reviews that can run standalone from static exports or use Rocketcart MCP as an optional Amazon Ads plus product-intelligence layer. Guides Codex to inspect Rocketcart profiles, live SP campaigns, product ads/ASIN context, category rank/BSR movement, competitor signals, product readiness, budget and targeting drift, snapshots/changelogs, and proposed approval-gated action rows without executing writes by default.

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Forces every code-related claim to include file:line citations. No citation means the claim must be verified or removed. Use when building documentation, writing code reviews, or any output that others will rely on.

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Trace the purpose of suspicious legacy code using current callers, tests, and Git history when deciding whether it can be changed or removed.

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Models what a competitor will do next and what happens if you respond - best response, equilibrium, and the difference between a rival who optimises perfectly and one who does not. Use before a price change or price war, when a competitor launches something, when deciding whether to match a rival's move, when analysing a market with few players, or when someone asks "what will they do if we do this".

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Review changed code for YAGNI, removable over-complexity, reuse, code quality, and efficiency, then apply worthwhile simplifications. Use when the user asks to simplify, clean up, tighten, de-hack recent changes, identify logic that can be dropped, or review whether added safeguards and edge-case handling are actually needed.

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Export a NeatContext context saved from a conversation into a self-contained bundle folder that can be shared or moved to another machine. Use only when the user explicitly invokes this skill or asks to export a NeatContext context.

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Export a NeatContext context saved from a conversation into a self-contained bundle folder that can be shared or moved to another machine. Use only when the user explicitly invokes this skill or asks to export a NeatContext context.

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Draft the changelog entry for all unreleased commits in Keep a Changelog format. Groups commits into Added, Changed, Fixed, Removed, and Security. Use before releasing a new version or when updating CHANGELOG.md.

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Create, switch to, or delete a git worktree under ~/.worktrees/<path-key>/<branch>, then rebase the agent session onto that directory. Use when asked to make, enter, or remove a worktree for a branch, when work needs an isolated checkout, or when invoked as /git:worktree.

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This skill should be used when the user asks to "scrub JSON values", "mask config fields", "add a Git clean filter", "remove volatile values from tracked config", or "stop config churn".

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Build and manage trusted local Paseo plugins. Use when the user asks to create, edit, install, reload, enable, disable, remove, or troubleshoot a Paseo plugin; add lifecycle hooks; transform agent configuration, environment, MCP servers, or workspace creation; automate permissions or turn follow-ups; add a native surface, sidebar item, or workspace panel; add Command Center items or slash commands; add composer pills or attachment sources; transform, render, or append agent timeline items; contribute a theme; use Paseo from plugin code; or add plugin RPCs.

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Turn an idea into a Conventional Docs Decision, then a Plan: classify the request, explore options and risks through dialogue, then commit a numbered Decision that review, prototypes, and acceptance move through. Use PROACTIVELY before any creative work — new features, new subsystems, interface or dependency changes, or any open-ended design question ("what are the options", "how should I approach", "should I use X or Y", "help me decide", "write a decision", "ADR").

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Move issues and external PRs through a state machine of triage roles — categorise, verify, grill if needed, and write agent-ready briefs.

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Finalize code changes for production readiness by removing duplicate logic, auditing hardcoded values, verifying branch/worktree intent, verifying build integrity, structuring clean commits with Conventional Commits format, and forcing a post-publish branch/worktree cleanup decision.

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Use when removing AI-writing tells from deliverable prose — posts, articles, drafts. Triggers on 'make this sound less like AI', 'humanize this draft', 'this reads like ChatGPT wrote it'.

日本語の概要は準備中です。原文の説明を表示しています。