reprompt-cli 2.5.0

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 patterns	Personal prompt weaknesses -- recurring gaps by task type
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 — click "Rewrite & Apply" to improve your prompt and replace the text directly in the input box.

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
name: Prompt Quality
on: pull_request

jobs:
  lint:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write    # needed for PR comments
    steps:
      - uses: actions/checkout@v4
      - 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

When comment-on-pr: true, every PR gets a quality report:

## reprompt lint 🟢 Passed

| Metric          | Value          |
|-----------------|----------------|
| Prompts checked | 12             |
| Errors          | 0              |
| Warnings        | 2              |
| Avg Score       | 62/100 ✅ (threshold: 50) |

📋 2 violation(s) [click to expand]

The comment updates on each push — no duplicates. Uses GITHUB_TOKEN (no extra secrets needed).

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