compress-prompt

📁 doodledood/claude-code-plugins 📅 Today
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总安装量
1
周安装量
#77009
全站排名
安装命令
npx skills add https://github.com/doodledood/claude-code-plugins --skill compress-prompt

Agent 安装分布

amp 1
cline 1
opencode 1
cursor 1
continue 1
kimi-cli 1

Skill 文档

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

  1. 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.

  2. Goal over process – State WHAT to achieve, not HOW. Let the model choose its approach.

  3. Training filter – “Would a competent person need to be told this?” If no → drop it. Models are trained on millions of examples.

  4. Maximize action space – Fewer constraints = more freedom = better results. Each constraint should earn its place.

  5. Inline-typable brevity – Short enough you could type it verbally to a capable colleague.

  6. 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.