tldr-deep

tldr-deep is a skill for Claude Code from parcadei/Continuous-Claude-v3. It costs 23 tokens per session (782 once invoked), scanned A, original, MIT.

A deep code-analysis guide for examining one function through its structure, call relationships, control flow, data flow, and dependencies. Control flow describes possible execution paths; data flow traces how values move through code.

In plain words
What is it for?
Use it to locate a function, inspect its syntax and callers, measure its branching complexity, trace its values, and identify the code affected by a line.
Why use it?
It provides a systematic way to understand complicated functions and investigate bugs or risky changes.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

not rated 3.9krepo +2 7mo ago A scan Socket: passSnyk: passSkillSpector: pass 23 tokens original MIT

Good fit Use it to locate a function, inspect its syntax and callers, measure its branching complexity, trace its values, and identify the code affected by a line.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/parcadei/continuous-claude-v3/tldr-deep
About the project

Continuous-Claude-v3 is a Claude Code development environment that preserves working context between sessions, coordinates specialized agents, and stores project knowledge through ledgers, handoffs, and analysis tools. It is for people using Claude Code on ongoing or complex software work. Its catalogue entries are the skills, agents, hooks, plugin, and setting that provide its workflows and orchestration.

parcadei/Continuous-Claude-v3 · 3,938 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add parcadei/Continuous-Claude-v3 --skill tldr-deep
Clone the repo
git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for tldr-deep

README.md
[![agentmods](https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/tldr-deep/github.svg)](https://agentmods.dev/skills/parcadei/continuous-claude-v3/tldr-deep)
Your own site
<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/tldr-deep"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/tldr-deep/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for tldr-deep

Your own site · 80×15
<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/tldr-deep"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/tldr-deep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 782 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 18 Mar 2026
  • Snyk pass 15 Feb 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00023 $0.00782
Opus 5 $0.00012 $0.00391
Sonnet 5 $0.00005 $0.00156
Haiku 4.5 $0.00002 $0.00078

Measured 9d ago against content hash 906d221db52d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

tldr-deep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/tldr-deep/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TLDR Deep Analysis

Full 5-layer analysis of a specific function. Use when debugging or deeply understanding code.

Trigger

  • /tldr-deep <function_name>
  • "analyze function X in detail"
  • "I need to deeply understand how Y works"
  • Debugging complex functions

Layers

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>

Execution

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

Output Format

## 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)

When to Use

  1. Debugging - Need to understand all paths through a function
  2. Refactoring - Need to know what depends on what
  3. Code review - Analyzing complex functions
  4. Performance - Finding hot spots (high cyclomatic complexity)

Read the full file on GitHub · 108 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 108 lines · 23 tokens per session scan A 906d221db52d

Subscribe to this mod's changes

tldr-deep is a skill published in the GitHub repository parcadei/Continuous-Claude-v3 (3,938 stars, last pushed 7mo ago), licensed MIT. It adds 23 tokens to every session and 782 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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