localsend-analysis
Analyze LocalSend repos with tree-sitter tags, gh GraphQL contributor snapshots, and protocol safety notes.
Best use case
localsend-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyze LocalSend repos with tree-sitter tags, gh GraphQL contributor snapshots, and protocol safety notes.
Teams using localsend-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/localsend-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How localsend-analysis Compares
| Feature / Agent | localsend-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyze LocalSend repos with tree-sitter tags, gh GraphQL contributor snapshots, and protocol safety notes.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Localsend Analysis ## Quick Start [Provide ONE minimal working example - the most common use case] ```typescript // Keep this concise - show essential code only // Move detailed examples to references/ for Level 3 loading ``` ## Core Principles - Principle 1: [Key concept] - Principle 2: [Key concept] - Principle 3: [Key concept] ## Common Patterns ### [Most Frequent Pattern] [Brief description - keep under 100 words] ## Reference Files For detailed documentation, see: - [references/](references/) - Add detailed guides here ## Notes - Important note 1 - Important note 2 <!-- PROGRESSIVE DISCLOSURE GUIDELINES: - Keep this file ~50 lines total (max ~150 lines) - Use 1-2 code blocks only (recommend 1) - Keep description <200 chars for Level 1 efficiency - Move detailed docs to references/ for Level 3 loading - This is Level 2 - quick reference ONLY, not a manual LLM WORKFLOW (when editing this file): 1. Write/edit SKILL.md 2. Format (if formatter available) 3. Run: claude-skills-cli validate <path> 4. If multi-line description warning: run claude-skills-cli doctor <path> 5. Validate again to confirm --> ## Scientific Skill Interleaving This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem: ### Eda - **exploratory-data-analysis** [○] via bicomodule ### Bibliography References - `general`: 734 citations in bib.duckdb ## Cat# Integration This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure: ``` Trit: 0 (ERGODIC) Home: Prof Poly Op: ⊗ Kan Role: Adj Color: #26D826 ``` ### GF(3) Naturality The skill participates in triads satisfying: ``` (-1) + (0) + (+1) ≡ 0 (mod 3) ``` This ensures compositional coherence in the Cat# equipment structure.
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