'Compresses prompts/skills into minimal goal-focused instructions. Trusts the model, drops what it already knows, maximizes action space. Use when asked to compress, condense, or minimize a prompt.'
Install
npx skillscat add doodledood/claude-code-plugins/compress-prompt Install via the SkillsCat registry.
Compresses prompts into minimal goal-focused instructions by removing capability explanations while keeping discipline guardrails. Use when you need to condense verbose instructions into shorter forms that still enforce quality and verification, or when prompt length is a concern.
Compress Prompt
Goal
Transform a prompt into the minimal instruction needed for the model to succeed. Not "preserve everything densely"—instead, "what's the least I need to say?"
Output: Display compressed result + stats. Optionally write to file with --output <path>.
Input
$ARGUMENTS = prompt (file path or inline text) [--output path]
If file path: read content. If inline: use directly. If ambiguous: try as file first.
Principles
Trust capability, enforce discipline - Models know HOW to do tasks. But they cut corners, forget context, skip verification, declare victory early. Drop capability instructions, keep discipline guardrails.
Goal over process - State WHAT to achieve, not HOW. Let the model choose its approach.
Training filter - "Would a competent person need to be told this?" If no → drop it. Models are trained on millions of examples.
Maximize action space - Fewer constraints = more freedom = better results. Each constraint should earn its place.
Inline-typable brevity - Short enough you could type it verbally to a capable colleague.
Avoid arbitrary values - "Max 4 rounds" or "2-3 examples" become rigid rules. State the principle, not the number. Constrain productively while giving flexibility.
What to Keep vs Drop
| KEEP | DROP |
|---|---|
| Core goal/purpose | Process/phases (capability) |
| Acceptance criteria (success conditions) | Examples the model knows |
| Novel constraints (counter-intuitive rules) | Obvious constraints (model defaults) |
| Execution discipline (write before proceeding, verify before finalizing) | Edge case handling (model trained on these) |
| Output format if non-standard | Explanations and rationale |
Execution discipline examples (KEEP these):
- "Write findings to file BEFORE proceeding" — prevents context rot
- "Don't finalize until X confirmed" — prevents premature completion
- "Read full log before synthesis" — restores lost context
Training-redundant examples (DROP these):
- "Be thorough", "Handle errors gracefully", "Ask clarifying questions"
- "Consider edge cases", "Use professional tone"
Constraints
Create todo list - Track: input validation, compression, verification iterations, output.
Verify with agent - Launch prompt-compression-verifier to check goal clarity, novel constraints preserved, no over-specification. Iterate until verification passes.
Single paragraph output - The compressed prompt must be one dense paragraph, not reformatted sections or bullets.
Non-destructive - Original file untouched. Display output + optional file save.
Output Format
Compressed: {source}
Original: {tokens} tokens
Compressed: {tokens} tokens ({percentage}% reduction)
---
{compressed paragraph}
---
Verification: PASSED/INCOMPLETE ({iterations} iteration(s))Example
Before (1,247 tokens): Full code reviewer prompt with phases, edge cases, examples...
After (67 tokens):
Review code for bugs, security issues, performance problems; success = all critical issues identified with actionable fixes. Output JSON {file, line, issue, severity, fix}. Never approve code with critical issues.Kept: Goal, acceptance criteria, output format, novel constraint (never approve with critical issues).
Dropped: Process phases, edge case handling, examples, obvious constraints.