Data & AI
llm-communication - Claude MCP Skill
Write effective LLM prompts, commands, and agent instructions. Goal-oriented over step-prescriptive. Role + Objective + Latitude pattern. Use when writing prompts, designing agents, building Claude Code commands, or reviewing LLM instructions. Keywords: prompt engineering, agent design, command writing.
SEO Guide: Enhance your AI agent with the llm-communication tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to write effective llm prompts, commands, and agent instructions. goal-oriented over step-prescriptive.... Download and configure this skill to unlock new capabilities for your AI workflow.
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SKILL.md# Talking to LLMs This skill helps you write effective prompts, commands, and agent instructions. ## Core Principle LLMs are intelligent agents, not script executors. Talk to them like senior engineers. ## Anti-Patterns ### Over-Prescriptive Instructions Bad: ``` Step 1: Run `sentry-cli issues list --status unresolved` Step 2: Parse the JSON output Step 3: For each issue, calculate priority score using formula... Step 4: Select highest priority issue Step 5: Run `git log --since="24 hours ago"` ...700 more lines ``` This treats the LLM like a bash script executor. It's brittle, verbose, and removes the LLM's ability to adapt. ### Excessive Hand-Holding Bad: ``` If the user says X, do Y. If the user says Z, do W. Handle edge case A by doing B. Handle edge case C by doing D. ``` You can't enumerate every case. Trust the LLM to generalize. ### Defensive Over-Specification Bad: ``` IMPORTANT: Do NOT do X. WARNING: Never do Y. CRITICAL: Always remember to Z. ``` If you need 10 warnings, your instruction is probably wrong. ## Good Patterns ### State the Goal, Not the Steps Good: ``` Investigate production errors. Check all available observability (Sentry, Vercel, logs). Correlate with git history. Find root cause. Propose fix. ``` Let the LLM figure out how. ### Provide Context, Not Constraints Good: ``` You're a senior SRE investigating an incident. The user indicated something broke around 14:57. ``` Frame the situation, don't micromanage the response. ### Trust Recovery Good: ``` Trust your judgment. If something doesn't work, try another approach. ``` LLMs can recover from errors. Let them. ### Role + Objective + Latitude The best prompts follow this pattern: 1. **Role**: Who is the LLM in this context? 2. **Objective**: What's the end goal? 3. **Latitude**: How much freedom do they have? Example: ``` You're a senior engineer reviewing this PR. # Role Find bugs, security issues, and code smells. # Objective Be direct. If it's fine, say so briefly. # Latitude ``` ## When Writing Claude Code Commands Commands are prompts. The same rules apply: **Bad command (700 lines):** - Exhaustive decision trees - Exact CLI commands to copy - Every edge case enumerated - No room for judgment **Good command (20 lines):** - Clear objective - Context about what tools exist - Permission to figure it out - Trust in agent judgment ## When Building Agentic Systems Same principles scale up: **Bad agent design:** - Rigid state machines - Exhaustive action lists - No error recovery - Brittle integrations **Good agent design:** - Goal-oriented - Self-correcting - Minimal constraints - Natural language interfaces ## The Test Before finalizing any LLM instruction, ask: > "Would I give these instructions to a senior engineer?" If you'd be embarrassed to hand a colleague a 700-line runbook for a simple task, don't give it to the LLM either. ## Remember The L in LLM stands for Language. Use it.
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- Repository
- phrazzld/claude-config
- Author
- phrazzld
- Last Sync
- 3/2/2026
- Repo Updated
- 3/1/2026
- Created
- 1/18/2026
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