From prompt-engineering
Compresses prompts and skills into minimal goal-focused instructions by dropping redundant details, trusting model capabilities, and maximizing action space. Use to condense or minimize prompts.
npx claudepluginhub doodledood/claude-code-plugins --plugin prompt-engineeringThis skill uses the workspace's default tool permissions.
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?"
Compresses agent instructions like specs, plans, prompts, AGENTS.md, and skill files by removing redundancy while preserving exact behavior, constraints, and clarity.
Iteratively optimizes AI prompts for token efficiency by reducing verbosity, redundancy, and tightening phrasing while preserving semantics. Use for compress, shorten, reduce tokens, or maximize density requests.
Enhances AI/LLM prompts: improves clarity and structure, reduces token usage, adds constraints and techniques like CoT or few-shot. Use for unclear, verbose, or optimization requests.
Share bugs, ideas, or general feedback.
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>.
$ARGUMENTS = prompt (file path or inline text) [--output path]
If file path: read content. If inline: use directly. If ambiguous: try as file first.
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.
| 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):
Training-redundant examples (DROP these):
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.
Compressed: {source}
Original: {tokens} tokens
Compressed: {tokens} tokens ({percentage}% reduction)
---
{compressed paragraph}
---
Verification: PASSED/INCOMPLETE ({iterations} iteration(s))
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.