🎯

tldr-expert

🎯Skill

from yuniorglez/gemini-elite-core

VibeIndex|
What it does

tldr-expert skill from yuniorglez/gemini-elite-core

tldr-expert

Installation

git cloneClone repository
git clone https://github.com/YuniorGlez/gemini-elite-core.git
Shell ScriptRun shell script
./setup.sh
πŸ“– Extracted from docs: yuniorglez/gemini-elite-core
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Last UpdatedJan 29, 2026

Skill Details

SKILL.md

"Master of Semantic Code Intelligence and Token Optimization, specialized in Context Engineering and Automated Context Packing (ACP)."

Overview

# 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.

---

πŸ—οΈ 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."

---

πŸ› οΈ Implementation Patterns

1. Automated Context Packing (ACP)

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

```bash

# 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.

```bash

# Querying the semantic index

tldr semantic "session expiration and cookie cleanup logic"

```

3. Gitingest Onboarding

For new contributors or sub-agents:

```bash

# Create a prompt-friendly digest of the current branch

gitingest . --output ingest-digest.txt --max-size 10mb

```

---

πŸ“Š Token Saving Benchmarks (2026 Standard)

| Method | Token Usage | Fidelity | Best For |

| :--- | :--- | :--- | :--- |

| Raw read_file | 100% | 100% | Final implementation/debugging. |

| Gitingest Digest | 25% | 85% | Initial onboarding and planning. |

| Repomix (Compressed) | 15% | 90% | Context packing for reasoning models. |

| llm-tldr Query | 2% | 95% (Structural) | Architectural mapping and tracing. |

---

🚫 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.

---

πŸ›‘οΈ 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.

---

πŸ› οΈ Troubleshooting Guide

| Issue | Likely Cause | 2026 Corrective Action |

| :--- | :--- | :--- |

| llm-tldr Index Stale | Significant refactor performed | Run tldr warm . immediately. |

| Context Bundle too large | Too many implementation details | Re-run Repomix with --top-level-only or --signatures-only. |

| Semantic Search "No Match" | Query too specific or index cold | Use rg for keywords, then tldr context on the results. |

| Gitingest Output Messy | Missing .gitignore configuration | Ensure a valid .gitignore exists at the root. |

---

πŸ“š 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.

---

πŸ“œ 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.

---

πŸ”„ 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)