Condense

SkillAI & models

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

Available today. Use it from your connected AI after setup.

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.

  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.

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" 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:

  1. YAML check: Confirm frontmatter parses.
    python3 -c "import yaml; yaml.safe_load(open('<file>').read().split('---')[1])"
    
  2. Report: Show | File | Before | After | Reduction | table with word counts.
  3. 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