Meta-skill for exploring an internal codebase at varying depths. READ-ONLY workflow - no code changes.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionexploreExecute the skills CLI command in your project's root directory to begin installation:
Fetches explore from parcadei/continuous-claude-v3 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate explore. Access via /explore in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Meta-skill for exploring an internal codebase at varying depths. READ-ONLY workflow - no code changes.
/explore <depth> [options]
If the user types just /explore with no or partial arguments, guide them through this question flow. Use AskUserQuestion for each phase.
question: "How would you like to explore?"
header: "Explore"
options:
- label: "Help me choose (Recommended)"
description: "I'll ask questions to pick the right exploration depth"
- label: "Quick - fast overview"
description: "Chain: tldr tree → tldr structure (~1 min)"
- label: "Deep - comprehensive analysis"
description: "Chain: onboard → tldr → research → document (~5 min)"
- label: "Architecture - layers & dependencies"
description: "Chain: tldr arch → call graph → layer mapping (~3 min)"
Mapping:
If Answer is Unclear (via "Other"):
question: "I want to understand how deep you want to explore. Did you mean..."
header: "Clarify"
options:
- label: "Help me choose"
description: "Not sure - guide me through questions"
- label: "Quick - fast overview"
description: "Just want to see what's here"
- label: "Deep - comprehensive analysis"
description: "Need thorough understanding"
- label: "Neither - let me explain differently"
description: "I'll describe what I need"
question: "What are you trying to understand?"
header: "Goal"
options:
- label: "Get oriented in the codebase"
description: "Quick overview of structure"
- label: "Understand how something works"
description: "Deep dive into specific area"
- label: "Map the architecture"
description: "Layers, dependencies, patterns"
- label: "Find where something is"
description: "Locate specific code/functionality"
Mapping:
question: "What area should I focus on?"
header: "Focus"
options:
- label: "Entire codebase"
description: "Explore everything"
- label: "Specific directory or module"
description: "I'll specify the path"
- label: "Specific concept/feature"
description: "e.g., 'authentication', 'API routes'"
If "Specific directory" or "Specific concept" → ask follow-up for the path/keyword.
question: "What should I produce?"
header: "Output"
options:
- label: "Just tell me what you find"
description: "Interactive summary in chat"
- label: "Create a documentation file"
description: "Write to thoughts/shared/docs/"
- label: "Create handoff for implementation"
description: "Prepare context for coding agent"
Mapping:
If architecture depth selected:
question: "Where should I start the analysis?"
header: "Entry point"
options:
- label: "Auto-detect (main, cli, app)"
description: "Find common entry points"
- label: "Specific function/file"
description: "I'll specify the entry point"
Based on your answers, I'll run:
**Depth:** deep
**Focus:** "authentication"
**Output:** handoff
**Path:** src/
Proceed? [Yes / Adjust settings]
| Depth | Time | What it does |
|---|---|---|
quick |
~1 min | tldr-explorer only - fast structure overview |
deep |
~5 min | onboard + tldr-explorer + research-codebase + write doc |
architecture |
~3 min | tldr arch + call graph + layer mapping + circular dep detection |
| Option | Description | Example |
|---|---|---|
--focus "area" |
Focus on specific area | --focus "auth", --focus "api" |
--output handoff |
Create handoff for next agent | --output handoff |
--output doc |
Create documentation file | --output doc |
--entry "func" |
Start from specific entry point | --entry "main", --entry "process_request" |
# Quick structure overview
/explore quick
# Deep exploration focused on auth
/explore deep --focus "auth" --output doc
# Architecture analysis from specific entry
/explore architecture --entry "cli" --output handoff
# Quick focused exploration
/explore quick --focus "hooks"
Fast structure overview using tldr-explorer. Best for:
Steps:
tldr tree for file structuretldr structure for codemaps--focus provided, run tldr search for targeted resultsCommands:
# 1. File tree
tldr tree ${PATH:-src/} --ext .py
# 2. Code structure
tldr structure ${PATH:-src/} --lang python
# 3. Focused search (if --focus provided)
tldr search "${FOCUS}" ${PATH:-src/}
Comprehensive exploration with documentation output. Best for:
Steps:
.claude/cache/tldr/), if not run onboardSubprocess:
# 1. Onboard check
if [ ! -f .claude/cache/tldr/arch.json ]; then
# Spawn onboard agent
fi
# 2. Structure analysis
tldr structure src/ --lang python
tldr calls src/
# 3. Research patterns (via scout agent)
Task: research-codebase → "Document existing patterns in ${FOCUS:-codebase}"
# 4. Write output
→ thoughts/shared/research/YYYY-MM-DD-explore-{focus}.md
→ OR thoughts/shared/handoffs/{session}/explore-{focus}.yaml
Architecture-focused analysis with layer detection. Best for:
Steps:
tldr arch for layer detectiontldr calls for cross-file call graphCommands:
# 1. Architecture detection
tldr arch ${PATH:-src/}
# Returns: entry_layer, middle_layer, leaf_layer, circular_deps
# 2. Call graph
tldr calls ${PATH:-src/}
# Returns: edges, nodes
# 3. Impact analysis from entry point (if --entry provided)
tldr impact ${ENTRY} ${PATH:-src/} --depth 3
Output Structure:
layers:
entry: [routes.py, cli.py, main.py] # Controllers/handlers
middle: [services.py, auth.py] # Business logic
leaf: [utils.py, helpers.py] # Utilities
call_graph:
total_edges: 142
hot_paths: [process_request → validate → authorize]
circular_deps:
- [module_a, module_b] # A imports B, B imports A
boundaries:
- name: API layer
files: [src/api/*✓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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.8★★★★★68 reviews- MMeera Torres★★★★★Dec 20, 2024
I recommend explore for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- MMeera Abbas★★★★★Dec 20, 2024
Solid pick for teams standardizing on skills: explore is focused, and the summary matches what you get after install.
- CChaitanya Patil★★★★★Dec 4, 2024
explore has been reliable in day-to-day use. Documentation quality is above average for community skills.
- SSoo Sethi★★★★★Nov 27, 2024
Registry listing for explore matched our evaluation — installs cleanly and behaves as described in the markdown.
- PPiyush G★★★★★Nov 23, 2024
Solid pick for teams standardizing on skills: explore is focused, and the summary matches what you get after install.
- HHana Harris★★★★★Nov 11, 2024
Useful defaults in explore — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- SSoo Rao★★★★★Nov 11, 2024
explore has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AAmelia Bhatia★★★★★Oct 18, 2024
explore fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- SShikha Mishra★★★★★Oct 14, 2024
We added explore from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- IIra Ndlovu★★★★★Oct 2, 2024
explore is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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