Full 5-layer analysis of a specific function. Use when debugging or deeply understanding code.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontldr-deepExecute the skills CLI command in your project's root directory to begin installation:
Fetches tldr-deep 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-deep. Access via /tldr-deep 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
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Full 5-layer analysis of a specific function. Use when debugging or deeply understanding code.
/tldr-deep <function_name>| Layer | Purpose | Command |
|---|---|---|
| L1: AST | Structure | tldr extract <file> |
| L2: Call Graph | Navigation | tldr context <func> --depth 2 |
| L3: CFG | Complexity | tldr cfg <file> <func> |
| L4: DFG | Data flow | tldr dfg <file> <func> |
| L5: Slice | Dependencies | tldr slice <file> <func> <line> |
Given a function name, run all layers:
# First find the file
tldr search "def <function_name>" .
# Then run each layer
tldr extract <found_file> # L1: Full file structure
tldr context <function_name> --project . --depth 2 # L2: Call graph
tldr cfg <found_file> <function_name> # L3: Control flow
tldr dfg <found_file> <function_name> # L4: Data flow
tldr slice <found_file> <function_name> <target_line> # L5: Slice
## Deep Analysis: {function_name}
### L1: Structure (AST)
File: {file_path}
Signature: {signature}
Docstring: {docstring}
### L2: Call Graph
Calls: {list of functions this calls}
Called by: {list of functions that call this}
### L3: Control Flow (CFG)
Blocks: {N}
Cyclomatic Complexity: {M}
[Hot if M > 10]
Branches:
- if: line X
- for: line Y
- ...
### L4: Data Flow (DFG)
Variables defined:
- {var1} @ line X
- {var2} @ line Y
Variables used:
- {var1} @ lines [A, B, C]
- {var2} @ lines [D, E]
### L5: Program Slice (affecting line {target})
Lines in slice: {N}
Key dependencies:
- line X → line Y (data)
- line A → line B (control)
---
Total: ~{tokens} tokens (95% savings vs raw file)
from tldr.api import (
extract_file,
get_relevant_context,
get_cfg_context,
get_dfg_context,
get_slice
)
# All layers for one function
file_info = extract_file("src/processor.py")
context = get_relevant_context("src/", "process_data", depth=2)
cfg = get_cfg_context("src/processor.py", "process_data")
dfg = get_dfg_context("src/processor.py", "process_data")
slice_lines = get_slice("src/processor.py", "process_data", target_line=42)
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
Keeps context tight: tldr-deep is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in tldr-deep — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
tldr-deep fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
tldr-deep is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for tldr-deep matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in tldr-deep — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend tldr-deep for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: tldr-deep is focused, and the summary matches what you get after install.
tldr-deep has been reliable in day-to-day use. Documentation quality is above average for community skills.
tldr-deep is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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