reprompt-cli 2.4.1

Discover, analyze, and optimize your prompts from AI coding sessions

re:prompt

Score, rewrite, and optimize your AI prompts -- the only CLI that improves your prompts automatically. No LLM needed.

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See it in action

$ pip install reprompt-cli

# Rewrite a weak prompt into a better one (no LLM, rule-based)
$ reprompt rewrite "I was wondering if you could maybe help me fix the auth bug"
  34 → 52 (+18)

  ╭─ Rewritten ────────────────────────────────────────────────╮
  │ Help me fix the auth bug.                                  │
  ╰────────────────────────────────────────────────────────────╯

  Changes
  ✓ Removed filler (24% shorter)
  ✓ Removed hedging language

  You should also
  → Add actual code snippets or error messages for context
  → Reference specific files or functions by name
  → Add constraints (e.g., "Do not modify existing tests")

# Full diagnostic in one command
$ reprompt check "Fix the auth bug in src/login.ts where JWT expires"
  GOOD · 58

  Clarity     ████████████░░░░░░░░ 15/25
  Context     ████████████████░░░░ 20/25
  Position    ████████████████████ 20/20
  Structure   ░░░░░░░░░░░░░░░░░░░░  0/15
  Repetition  ███░░░░░░░░░░░░░░░░░  3/15

  Strengths
  ✓ Key instruction at the start — optimal placement
  ✓ References specific files

  Improve
  → Add the actual error message (+6 pts)
  → Add constraints like "Don't modify tests" (+5 pts)

What it does

Analyze

Command	Description
reprompt	Instant dashboard -- prompts, sessions, avg score, top categories
reprompt scan	Auto-discover prompts from 9 AI tools
reprompt check "prompt"	Full diagnostic -- score + lint + rewrite preview in one command
reprompt score "prompt"	Research-backed 0-100 scoring with 30+ features
reprompt compare "a" "b"	Side-by-side prompt analysis (or --best-worst for auto-selection)
reprompt insights	Personal patterns vs research-optimal benchmarks
reprompt style	Prompting fingerprint with --trends for evolution tracking
reprompt agent	Agent workflow analysis -- error loops, tool patterns, session efficiency
reprompt sessions	Session quality scores with frustration signal detection
reprompt repetition	Cross-session repetition detection -- spot recurring prompts
reprompt projects	Per-project quality breakdown -- sessions, scores, frustration signals

Optimize

Command	Description
reprompt build "task"	Build prompts from components -- task, context, files, errors, constraints. Model-aware (Claude/GPT/Gemini)
reprompt rewrite "prompt"	Rewrite prompts to score higher -- filler removal, restructuring, hedging cleanup
reprompt compress "prompt"	4-layer prompt compression (40-60% token savings typical)
reprompt distill	Extract important turns from conversations with 6-signal scoring
reprompt distill --export	Recover context when a session runs out -- paste into new session
reprompt lint	Configurable prompt quality linter with CI/GitHub Action support
reprompt init	Generate .reprompt.toml config for your project

Manage

Command	Description
reprompt privacy	See what data you sent where -- file paths, errors, PII exposure
reprompt privacy --deep	Scan for sensitive content: API keys, tokens, passwords, PII
reprompt report	Full analytics: hot phrases, clusters, patterns (--html for dashboard)
reprompt digest	Weekly summary comparing current vs previous period
reprompt wrapped	Prompt DNA report -- persona, scores, shareable card
reprompt template save|list|use	Save and reuse your best prompts

Prompt Science

Scoring is calibrated against 10 peer-reviewed papers covering 30+ features across 5 dimensions:

Dimension	What it measures	Key papers
Structure	Markdown, code blocks, explicit constraints	Prompt Report (2406.06608)
Context	File paths, error messages, I/O specs, edge cases	Zi+ (2508.03678), Google (2512.14982)
Position	Instruction placement relative to context	Stanford (2307.03172), Veseli+ (2508.07479), Chowdhury (2603.10123)
Repetition	Redundancy that degrades model attention	Google (2512.14982)
Clarity	Readability, sentence length, ambiguity	SPELL (EMNLP 2023), PEEM (2603.10477)

Cross-validated findings that inform our engine:

• Position bias is architectural — present at initialization, not learned. Front-loading instructions is effective for prompts under 50% of context window (3 papers agree)
• Moderate compression improves output — rule-based filler removal doesn't just save tokens, it enhances LLM performance (2505.00019)
• Prompt quality is independently measurable — prompt-only scoring predicts output quality without seeing the response (ACL 2025, 2503.10084)

All analysis runs locally in <1ms per prompt. No LLM calls, no network requests.

Conversation Distillation

reprompt distill scores every turn in a conversation using 6 signals:

• Position -- first/last turns carry framing and conclusions
• Length -- substantial turns contain more information
• Tool trigger -- turns that cause tool calls are action-driving
• Error recovery -- turns that follow errors show problem-solving
• Semantic shift -- topic changes mark conversation boundaries
• Uniqueness -- novel phrasing vs repetitive follow-ups

Session type (debugging, feature-dev, exploration, refactoring) is auto-detected and signal weights adapt accordingly.

Supported AI tools

Tool	Format	Auto-discovered by scan
Claude Code	JSONL	Yes
Codex CLI	JSONL	Yes
Cursor	.vscdb	Yes
Aider	Markdown	Yes
Gemini CLI	JSON	Yes
Cline (VS Code)	JSON	Yes
OpenClaw / OpenCode	JSON	Yes
ChatGPT	JSON	Via reprompt import
Claude.ai	JSON/ZIP	Via reprompt import

Installation

pip install reprompt-cli            # core (all features, zero config)
pip install reprompt-cli[chinese]   # + Chinese prompt analysis (jieba)
pip install reprompt-cli[mcp]       # + MCP server for Claude Code / Continue.dev / Zed

Quick start

reprompt check "your prompt here"   # full diagnostic — score + lint + rewrite
reprompt scan                       # discover prompts from installed AI tools
reprompt                            # see your dashboard

Auto-scan after every session

reprompt install-hook               # adds post-session hook to Claude Code

Browser extension

Capture prompts from ChatGPT, Claude.ai, and Gemini directly in your browser. Live score badge shows prompt quality as you type.

1. Install the extension from Chrome Web Store or Firefox Add-ons
2. Connect to the CLI: reprompt install-extension
3. Verify: reprompt extension-status

Captured prompts sync locally via Native Messaging -- nothing leaves your machine.

CI integration

GitHub Action

# .github/workflows/prompt-lint.yml
- uses: reprompt-dev/reprompt@main
  with:
    score-threshold: 50   # fail if avg prompt score < 50
    strict: true          # fail on warnings too
    comment-on-pr: true   # post quality report as PR comment

pre-commit

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/reprompt-dev/reprompt
    rev: v2.2.2
    hooks:
      - id: reprompt-lint

Direct CLI

reprompt lint --score-threshold 50  # exit 1 if avg score < 50
reprompt lint --strict              # exit 1 on warnings
reprompt lint --json                # machine-readable output

Project configuration

reprompt init   # generates .reprompt.toml with all rules documented

# .reprompt.toml (or [tool.reprompt.lint] in pyproject.toml)
[lint]
score-threshold = 50       # fail if avg score < 50

[lint.rules]
min-length = 20            # error if prompt < 20 chars (0 = off)
short-prompt = 40          # warning if < 40 chars (0 = off)
vague-prompt = true        # error on "fix it" etc (false = off)
debug-needs-reference = true

Privacy

• All analysis runs locally. No prompts leave your machine.
• reprompt privacy shows exactly what you've sent to which AI tool.
• Optional telemetry sends only anonymous 26-dimension feature vectors -- never prompt text.
• Open source: audit exactly what's collected.

Privacy policy

Links

• Website: getreprompt.dev
• Chrome Extension: Chrome Web Store
• Firefox Add-on: Firefox Add-ons
• PyPI: reprompt-cli
• Changelog: CHANGELOG.md
• Privacy: getreprompt.dev/privacy

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT