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ashlr-plugin
ashlr-plugin에는 ashlrai에서 수집한 skills 11개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.
이 저장소의 skills
Check Ashlr's Codex plugin packaging, MCP manifest, hooks, skills, and workspace allow-list. Use when Codex setup, plugin loading, or MCP registration looks wrong.
Codex-native efficient tool workflow. Prefer Ashlr MCP read, grep, tree, bash, savings, and genome tools before native scans when they reduce context.
Refresh or maintain Ashlr project genome state from Codex. Use after architectural changes, schema/routing changes, or substantial refactors.
Efficient parallel exploration habits for Codex. Use when a task needs several independent codebase questions answered before implementation.
Inspect Ashlr token savings from Codex using host-neutral CLI and MCP surfaces.
Token-efficient refactoring discipline for Claude Code. Diff-first before editing, structural edits for renames, find-all-refs then one multi_edit per file, no re-reads between edits, one genome proposal at the end. Reduces refactor session cost 40–65%.
Output structure reshaper for Claude Code. Complements ashlr-brief's prose trimming by enforcing inverted-pyramid order, inline code for identifiers, tables for multi-item comparisons, and no transitional filler. Works standalone or alongside ashlr-brief.
Guidelines for when and how to call ashlr__genome_propose to keep the project genome accurate. Covers post-architectural-decision proposals, correct summary format, and what NOT to propose. Keeps the genome signal-rich without noise.
Highest-impact token-saving habits for Claude Code tool use. Read-once, batch edits, orient-before-search, skip verification reads, grep-before-read, concise output at high spend. Reduces session token cost 30–60%.
Genome-aware search habits for Claude Code. Prefer ashlr__grep and ashlr__glob over native scans; compose multi-term queries; trust genome summaries; orient once before searching. Reduces redundant reads and search token cost 40–70%.
Tunable response-shortening skill for Claude Code. Trims filler from prose to reduce output tokens 30–55% while preserving grammar and code fidelity. Three intensity levels (lite / standard / concise). Auto-clarity exceptions for security, destructive actions, errors, and code blocks.