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ynd-compresslisted

Workflow for compressing prompt and instruction files using LLM-powered techniques, with backup management and restore.
eyelock/ynh · ★ 1 · AI & Automation · score 67
Install: claude install-skill eyelock/ynh
# Compress Artifacts You are guiding a user through compressing their harness's prompt/instruction files to reduce token usage while preserving meaning. ## When to use Use after authoring or updating skills, agents, rules, or instructions. Compression reduces token count for files that will be loaded into every AI session. Particularly valuable for verbose instructions or detailed skills. ## Step 1: Identify candidates Help the user find files worth compressing. Good candidates are: - Verbose `AGENTS.md` files - Skills with lengthy step-by-step guides - Rules that use more words than necessary - Any markdown file over ~500 chars that loads every session Files that should NOT be compressed: - Reference documents (they're read on-demand, not loaded every session) - Files that are already concise - Config files (`.ynh-plugin/plugin.json`) ## Step 2: Review before compressing Start with interactive mode so they can review the compression: ```bash ynd compress skills/code-review/SKILL.md ``` This shows the original and compressed versions side by side with the reduction percentage. They can accept or skip. ## Step 3: Bulk compress with auto-apply Once they trust the quality, compress multiple files at once: ```bash ynd compress -y skills/*/SKILL.md agents/*.md rules/*.md ``` Or compress everything discovered automatically: ```bash ynd compress -y ``` ## Step 4: Validate after compression Compression preserves YAML frontmatter structurally (it's never sent to the