Condense
SkillAI & modelsMaximize information density: preserve all instructions, remove prose filler.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Condense skill
What this skill tells your AI
The instructions your AI receives, as published by notque/vexjoy-agent in skills/code-quality/condense/SKILL.md and read by ahel’s review.
Strip prose filler from .md files. Preserve every instruction. This skill practices what it preaches.
Phase 1: SCOPE
Identify targets.
- Single file: User names a path. Read it.
- Glob: User gives a pattern (
agents/*.md). Expand, list matches, confirm with user. - 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.
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:
- Read the full file. Record word count.
- Rewrite in place applying the rules below.
- 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" on obvious rules
- Motivational framing ("this will help you", "it is important to note")
- Filler phrases: "in order to", "it should be noted that", "it is worth mentioning"
- Examples that repeat what the phase already says
- Paragraphs saying the same thing from different angles -- merge to one
STYLE: Short sentences. Active voice. Concrete words. If you can cut a word without losing an instruction, cut it.
DELETE TEST
Before cutting any sentence: "If I remove this, does the reader lose an instruction, rule, or decision?" No = cut. Yes = keep.
Boundaries
Do not reorganize sections, change meaning, add ideas, alter paths/commands, drop tables or code blocks, or modify YAML frontmatter values.
Phase 3: VERIFY
For each condensed file:
- YAML check: Confirm frontmatter parses.
python3 -c "import yaml; yaml.safe_load(open('<file>').read().split('---')[1])" - Report: Show
| File | Before | After | Reduction |table with word counts. - Instruction check: Grep original for key terms (phase names, gate names, commands). Confirm each appears in condensed version. If any missing, restore from original.
Gate: YAML parses. No instructions lost. Reduction reported.
Error Handling
No prose to cut: Report 0% reduction, move to next file.
Instruction removed: Re-read original, restore missing instruction, re-verify.
YAML broken: Restore original frontmatter verbatim, re-condense body only.
Non-.md file: Skip with warning.
Signals
- GitHub stars
- 419
- Forks
- 44
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
condense- Source
- github.com/notque/vexjoy-agent