compress-prompt
GitHub将提示词压缩为最小化、目标导向的指令，去除模型已知的冗余信息，保留核心目标与关键约束。适用于需要精简Prompt以提升效率的场景。
Trigger Scenarios
Install
npx skills add doodledood/manifest-dev --skill compress-prompt -g -y
SKILL.md
Frontmatter
{
"name": "compress-prompt",
"metadata": {
"internal": true
},
"description": "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."
}
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.
Version History
- 2ac487e Current 2026-08-20 08:55
