Installable skillMIT

Condense

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

BY notqueUPDATED Oct 27, 2026
INTENDED FOR✳Claude Code>_Codex◈OpenClaw↗CursorInstall in a listed agent.

instructions.md

---
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.
COMPATIBILITY & REFERENCES

Platform guidance

Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.