condense

Solid

Maximize information density: preserve all instructions, remove prose filler.

AI & Automation 393 stars 36 forks Updated today MIT

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Quality Score: 95/100

Stars 20%
86
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Condense Strip prose filler from .md files. Preserve every instruction. This skill practices what it preaches. ## Phase 1: SCOPE Identify targets. 1. **Single file**: User names a path. Read it. 2. **Glob**: User gives a pattern (`agents/*.md`). Expand, list matches, confirm with user. 3. **Batch (10+ files)**: Dispatch parallel agents, one per file. **Mechanical pre-pass** (deterministic, run before LLM condensing): strip trailing whitespace and consecutive blank lines that inflate Opus token counts. The script handles the mechanical reduction so the LLM phase focuses on prose density. ```bash python3 scripts/check-whitespace.py --fix <target-file-or-dir> # 0=clean, 1=violations fixed ``` Run on the scoped targets (defaults to `agents/**/*.md` and `skills/**/*.md` when no path given). Then proceed to the LLM pass on the same files. **Gate**: At least one target file identified and readable; mechanical pre-pass run. --- ## Phase 2: CONDENSE For each file: 1. Read the full file. Record word count. 2. Rewrite in place applying the rules below. 3. Record new word count. ### Rules **KEEP** (never cut): - Every instruction, rule, gate, phase, step - Tables, code blocks, commands, paths - YAML frontmatter (do not alter) - Structure: headers, numbered lists, phase ordering - Technical terms naming specific things - Reference loading tables - Error handling sections - Non-obvious "because X" reasoning **CUT**: - Redundant restatements of the same rule - "Because X"...

Details

Author
notque
Repository
notque/vexjoy-agent
Created
2 months ago
Last Updated
today
Language
Python
License
MIT

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