condense

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.

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.

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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.