repoprompt

parcadei/continuous-claude-v3 · updated Apr 8, 2026

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$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill repoprompt
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summary

RepoPrompt is more token-efficient than raw file reads:

skill.md

RepoPrompt Skill

When to Use

  • Explore codebase structure (tree, codemaps)
  • Search code with context lines
  • Get code signatures without full file content (token-efficient)
  • Read file slices (specific line ranges)
  • Build context for tasks

Token Optimization

RepoPrompt is more token-efficient than raw file reads:

  • structure → signatures only (not full content)
  • read --start-line --limit → slices instead of full files
  • search --context-lines → relevant matches with context

CLI Usage

# If installed to PATH (Settings → MCP Server → Install CLI to PATH)
rp-cli -e 'command'

# Or use the alias (configure in your shell)
repoprompt_cli -e 'command'

Commands Reference

File Tree

# Full tree
rp-cli -e 'tree'

# Folders only
rp-cli -e 'tree --mode folders'

# Selected files only
rp-cli -e 'tree --mode selected'

Code Structure (Codemaps) - TOKEN EFFICIENT

# Structure of specific paths
rp-cli -e 'structure src/auth/'

# Structure of selected files
rp-cli -e 'structure --scope selected'

# Limit results
rp-cli -e 'structure src/ --max-results 10'

Search

# Basic search
rp-cli -e 'search "pattern"'

# With context lines
rp-cli -e 'search "error" --context-lines 3'

# Filter by extension
rp-cli -e 'search "TODO" --extensions .ts,.tsx'

# Limit results
rp-cli -e 'search "function" --max-results 20'

Read Files - TOKEN EFFICIENT

# Full file
rp-cli -e 'read path/to/file.ts'

# Line range (slice)
rp-cli -e 'read path/to/file.ts --start-line 50 --limit 30'

# Last N lines (tail)
rp-cli -e 'read path/to/file.ts --start-line -20'

Selection Management

# Add files to selection
rp-cli -e 'select add src/auth/'

# Set selection (replace)
rp-cli -e 'select set src/api/ src/types/'

# Clear selection
rp-cli -e 'select clear'

# View current selection
rp-cli -e 'select get'

Workspace Context

# Get full context
rp-cli -e 'context'

# Specific includes
rp-cli -e 'context --include prompt,selection,tree'

Chain Commands

# Multiple operations
rp-cli -e 'select set src/auth/ && structure --scope selected && context'

Workspaces

# List workspaces
rp-cli -e 'workspace list'

# List tabs
rp-cli -e 'workspace tabs'

# Switch workspace
rp-cli -e 'workspace switch "ProjectName"'

AI Chat (uses RepoPrompt's models)

# Send to chat
rp-cli -e 'chat "How does the auth system work?"'

# Plan mode
rp-cli -e 'chat "Design a new feature" --mode plan'

Context Builder (AI-powered file selection)

# Auto-select relevant files for a task
rp-cli -e 'builder "implement user authentication"'

Workflow Shorthand Flags

# Quick operations without -e syntax
rp-cli --workspace MyProject --select-set src/ --export-context ~/out.md
rp-cli --chat "How does auth work?"
rp-cli --builder "implement user authentication"

Script Files (.rp)

For repeatable workflows, save commands to a script:

# daily-export.rp
workspace switch Frontend
select set src/components/
context --all > ~/exports/frontend.md

Run with:

rp-cli --exec-file ~/scripts/daily-export.rp

CLI Flags

Flag Purpose
-e 'cmd' Execute command(s)
-w <id> Target window ID
-q Quiet mode
-d <cmd> Detailed help for command
--wait-for-server 5 Wait for connection (scripts)

Async Operations (tmux)

For long-running operations like builder, use the async script:

# Start context builder async
uv run python -m runtime.harness scripts/repoprompt_async.py \
    --action start --task "understand the auth system"

# With workspace switch
uv run python -m runtime.harness scripts/repoprompt_async.py \
    --action start --workspace "MyProject" --task "explore API patterns"

# Check status
uv run python -m runtime.harness scripts/repoprompt_async.py --action status

# Get result when done
uv run python -m runtime.harness scripts/repoprompt_async.py --action result

# Kill if needed
uv run python -m runtime.harness scripts/repoprompt_async.py --action kill

Note

Requires RepoPrompt app running with MCP Server enabled.

how to use repoprompt

How to use repoprompt on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add repoprompt
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill repoprompt

The skills CLI fetches repoprompt from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/repoprompt

Reload or restart Cursor to activate repoprompt. Access the skill through slash commands (e.g., /repoprompt) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.736 reviews
  • Amelia Bhatia· Dec 24, 2024

    Solid pick for teams standardizing on skills: repoprompt is focused, and the summary matches what you get after install.

  • Charlotte Garcia· Dec 4, 2024

    I recommend repoprompt for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chinedu Gupta· Nov 15, 2024

    We added repoprompt from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sakshi Patil· Nov 7, 2024

    repoprompt is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chaitanya Patil· Oct 26, 2024

    Keeps context tight: repoprompt is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chinedu Patel· Oct 6, 2024

    repoprompt fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Tariq Brown· Sep 25, 2024

    Registry listing for repoprompt matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Oshnikdeep· Sep 13, 2024

    repoprompt has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Charlotte Martinez· Sep 9, 2024

    repoprompt reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Diego Patel· Aug 28, 2024

    Registry listing for repoprompt matched our evaluation — installs cleanly and behaves as described in the markdown.

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