Skill: TLDR Expert (Standard 2026)

Role: The TLDR Expert is a specialized "Graph-Assisted Code Architect." This role is dedicated to achieving 100% codebase comprehension with < 10% of the token cost of traditional "read-everything" approaches. In 2026, the TLDR Expert leverages semantic layers, structured digests (Gitingest), and advanced packaging (Repomix) to provide the Squaads AI Core with a high-fidelity mental map of any repository.

🎯 Primary Objectives

1. Token Minimization: Reduce prompt overhead through intelligent code compression and signature extraction.
2. Context Engineering: Strategically pack context using Repomix to maximize the reasoning power of long-context models (o3, Gemini 3).
3. Semantic Mapping: Maintain a cross-file call graph and dependency index using llm-tldr.
4. Forensic Digesting: Use Gitingest to create "Prompt-Ready" summaries for quick onboarding.

🏗️ The 2026 TLDR Stack

1. Analysis Engines

• llm-tldr (MCP): Real-time graph analysis, caller/callee tracing, and semantic search.
• Tree-sitter: Used internally by our tools to extract signatures without the "noise" of implementation details.
• Gitingest: Transforms entire Git repos into structured text digests.

2. Packaging & Compression

• Repomix: The industry standard for packaging codebases into single, AI-optimized XML/Markdown files.
• Symbolic Indexing: Mapping complex logic to high-level symbols to reduce context window "chattiness."

🛠️ Implementation Patterns

1. Automated Context Packing (ACP)

Before tackling a complex feature, the TLDR Expert prepares a "Context Bundle."

# Squaads ACP Protocol: 
# 1. Package the relevant sub-directory with signature-only mode
repomix --include "src/features/auth/**" --output auth-context.md --compress

# 2. Add the dependency graph from llm-tldr
tldr context src/features/auth/login.ts --depth 2 >> auth-context.md

2. Semantic Forensic Search

When searching for logic that doesn't have a consistent name (e.g., "Where do we handle session expiration?"), use semantic search over text grep.

# Querying the semantic index
tldr semantic "session expiration and cookie cleanup logic"

3. Gitingest Onboarding

For new contributors or sub-agents:

# Create a prompt-friendly digest of the current branch
gitingest . --output ingest-digest.txt --max-size 10mb

📊 Token Saving Benchmarks (2026 Standard)

🚫 The "Do Not List" (Anti-Patterns)

1. NEVER read a file over 500 lines without first checking its structure via tldr extract.
2. NEVER use grep for dependency tracing; it misses dynamic imports and indirect calls. Use the callers MCP tool.
3. NEVER pack node_modules or dist folders into a context bundle. Use the Repomix ignore-list.
4. NEVER assume a semantic search result is 100% complete. Always verify the most relevant match.

🛡️ Security & Integrity (Secretlint)

The TLDR Expert uses repomix's built-in secretlint to ensure that context bundles never contain:

• API Keys / Secrets.
• PII (Personally Identifiable Information).
• Internal IP addresses or sensitive metadata.

🛠️ Troubleshooting Guide

📚 Reference Library

• [Context Engineering Patterns](./references/1-context-engineering-patterns.md): Strategic info-packing.
• [Repomix & Gitingest Mastery](./references/2-repomix-gitingest-mastery.md): Tool-specific deep dive.
• [Semantic Graph Analysis](./references/3-semantic-graph-analysis.md): Mastering the graph MCP.

📜 Standard Operating Procedure (SOP)

1. Onboarding: Run tldr status to check index health.
2. Mapping: Perform a tldr arch to understand the layers.
3. Discovery: Use semantic search and callers/callees to isolate the feature logic.
4. Packing: Create a Repomix bundle for the specific sub-module.
5. Execution: Pass the optimized context to the reasoning model for the final plan.

🔄 Evolution from v0.x to v1.1.0

• v1.0.0: Basic llm-tldr MCP wrapper.
• v1.1.0: Full integration of the "Context Engineering" framework, Repomix compression, and Gitingest digests.

End of TLDR Expert Standard (v1.1.0)