Get a token-efficient overview of any project using the TLDR stack.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontldr-overviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches tldr-overview 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 tldr-overview. Access via /tldr-overview 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.
Submit your Claude Code skill and start earning
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
0
total installs
0
this week
3.7K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
3.7K
stars
Get a token-efficient overview of any project using the TLDR stack.
/overview or /tldr-overviewtldr tree . --ext .py # or .ts, .go, .rs
tldr structure src/ --lang python --max 50
Returns: functions, classes, imports per file
tldr calls src/
Returns: cross-file relationships, main entry points
For each entry point found:
tldr cfg src/main.py main # Get complexity
## Project Overview: {project_name}
### Structure
{tree output - files and directories}
### Key Components
{structure output - functions, classes per file}
### Architecture (Call Graph)
{calls output - how components connect}
### Complexity Hot Spots
{cfg output - functions with high cyclomatic complexity}
---
Token cost: ~{N} tokens (vs ~{M} raw = {savings}% savings)
from tldr.api import get_file_tree, get_code_structure, build_project_call_graph
# 1. Tree
tree = get_file_tree("src/", extensions={".py"})
# 2. Structure
structure = get_code_structure("src/", language="python", max_results=50)
# 3. Call graph
calls = build_project_call_graph("src/", language="python")
# 4. Complexity for hot functions
for edge in calls.edges[:10]:
cfg = get_cfg_context("src/" + edge[0], edge[1])
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
parcadei/continuous-claude-v3
mattpocock/skills
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
tldr-overview reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added tldr-overview from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for tldr-overview matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for tldr-overview matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in tldr-overview — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
tldr-overview reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend tldr-overview for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: tldr-overview is the kind of skill you can hand to a new teammate without a long onboarding doc.
tldr-overview has been reliable in day-to-day use. Documentation quality is above average for community skills.
tldr-overview fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
showing 1-10 of 30