UnifAPI DocsSkills
How UnifAPI Skills turn task prompts into MCP calls over live public-data APIs.
Skills are task-specific workflows an agent can run with UnifAPI MCP. They start from an outcome — a KOL pricing brief, creator shortlist, social listening summary, or competitor launch analysis — then let the agent discover and call public-data operations as needed.
The installable Skill packages will come later. The docs here describe the contract that makes those Skills work today: MCP discovery, public-data boundaries, output expectations, and the HTTP API layer behind the workflow.
Skill model
Start with the result. A Skill prompt names the artifact the agent should produce, such as a ranked creator table or competitive brief.
Discover operations through MCP. The agent uses list_operations and get_operation to
find public-data APIs that match the task.
Call live public data. The agent uses call_api only when it needs evidence, then returns
the result with assumptions, confidence, and follow-up questions.
First benchmark Skill
The first public benchmark is KOL Pricing:
Analyze these Twitter/X KOLs for an AI developer-tool campaign: @vercel, @shadcn, @rauchg.
Use UnifAPI public data, compare recent engagement, audience fit, posting cadence, and collaboration risk.
Return a ranked table with estimated sponsored-post price ranges, confidence, evidence, and follow-up questions.This runs well in Codex, Claude Code, Cursor, Claude Desktop, or any MCP-capable client. The user keeps their existing agent plan; UnifAPI bills only the public-data records returned by call_api.
Data boundary
UnifAPI Skills use public data. OAuth authorizes the UnifAPI MCP workspace and credit balance; it does not grant access to a user's private Twitter/X, Google, CRM, or SaaS account.
Use a connector platform when a workflow needs user-authorized SaaS data. Use UnifAPI when the workflow needs public social records, posts, comments, profiles, videos, trends, communities, or company signals.
Output expectations
A Skill should return a decision artifact, not raw API dumps:
- evidence-backed tables or briefs
- assumptions and confidence notes
- source operations used
- follow-up searches the agent should run
- risks, missing evidence, and next actions