
attio-mcp-usage
Provides universal workflow patterns and best practices for using the Attio MCP server. Complements the attio-workspace-schema skill with HOW-TO guidance for MCP tools and error prevention.
- Installs
- 688
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/kesslerio/attio-mcp-server --skill attio-mcp-usage- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads attio-mcp-usage automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Attio MCP Usage Guide
Universal workflow patterns and best practices for the Attio MCP server.
Purpose
Teaches universal patterns for:
- Structuring multi-step workflows
- Preventing common API errors
- Using MCP tools correctly
- Building integrations
Related Skills
attio-workspace-schema - Reference for YOUR workspace-specific:
- Attribute slugs and types
- Select/status option values
- List IDs and names
- Read-only fields
Always check the schema skill for workspace-specific data
Quick Links
- Workflows - Universal workflow patterns
- Golden Rules - Error prevention system
- Tool Reference - MCP tool signatures
- Integration Patterns - Pattern index
Pattern Selection
See Pattern Selection Guide for keyword-based routing to the right pattern for your workflow.
When to Use This Skill
- Planning multi-step workflows
- Troubleshooting API errors
- Learning MCP tool syntax
- Building integrations
- Understanding best practices
Key Principles
- Schema skill = WHAT (your specific fields/lists)
- Usage skill = HOW (universal patterns)
- Cross-reference when you need workspace specifics
FAQ
What does the attio-mcp-usage skill do?
Provides universal workflow patterns and best practices for using the Attio MCP server. Complements the attio-workspace-schema skill with HOW-TO guidance for MCP tools and error prevention.
How do I install attio-mcp-usage?
Run npx skills add https://github.com/kesslerio/attio-mcp-server --skill attio-mcp-usage in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/attio-mcp-usage/ (personal) or .claude/skills/attio-mcp-usage/ (project).
Which AI agents can use attio-mcp-usage?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is attio-mcp-usage safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains attio-mcp-usage?
It is published by kesslerio in kesslerio/attio-mcp-server. It has 688 recorded installs.
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available