Claude Skill

condense

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

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Download notque-vexjoy-agent-skills_code-quality_condense-8ad6845.zip · 1 KB
Part of notque/vexjoy-agent — 69 skills

Install

skills CLI npx skills add https://github.com/notque/vexjoy-agent/tree/main/skills/code-quality/condense
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install notque-vexjoy-agent@llmmart
Git git clone https://github.com/notque/vexjoy-agent.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole notque/vexjoy-agent collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

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.


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.

Files (vexjoy-agent)
  • SKILL.md 3.6 KB
    ---
    name: condense
    description: "Maximize information density: preserve all instructions, remove prose filler."
    user-invocable: true  # justification: users type "/condense <file>" directly to tighten
                          # specific files; /do dispatch adds unnecessary routing overhead
                          # for a targeted file-editing operation.
    argument-hint: "<file-or-glob>"
    allowed-tools:
      - Read
      - Edit
      - Write
      - Bash
      - Grep
      - Glob
    routing:
      triggers:
        - condense
        - reduce words
        - clarity pass
        - information density
        - remove prose
        - tighten
        - fewer words
      pairs_with:
        - toolkit
      complexity: Simple
      category: code-quality
    ---
    
    # 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" 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.
       ```bash
       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.
    

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