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LLM Skill (SKILL.md)

ZebraTruth can be integrated into any LLM agent by injecting a single markdown file into the agent’s context. No SDK, no code generation, no tool schemas — the LLM reads the documentation and makes HTTP calls.

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How It Works

  1. The SKILL.md file is injected into the LLM’s system prompt
  2. The LLM learns how to authenticate, run checks, and interpret results
  3. When the user asks to check content, the LLM calls the ZebraTruth API via HTTP
  4. Results are interpreted and presented to the user with actionable recommendations

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What the LLM Can Do

  • Validate API keys via GET /whoami
  • Run compliance checks (sync, stream, or async)
  • Invoke individual agents for targeted checks
  • Interpret scores, decisions, and annotations
  • Check credit balance before expensive operations
  • Handle errors (401, 402, 429) with appropriate user guidance

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Why This Approach

Inspired by Mosaic AI Labs, the SKILL.md pattern is:
  • Platform-agnostic — works with Claude Code, OpenClaw, or any LLM that supports markdown injection
  • No SDK to maintain — the integration IS the documentation
  • Always up to date — update the SKILL.md, all integrations update automatically
  • Lazy-loaded — workflow docs are loaded on demand, keeping base context small

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SKILL.md Structure

The skill file has two parts:
  1. YAML frontmatter — machine-readable metadata (name, API base URL, auth method, capabilities)
  2. Markdown body — the LLM system prompt (auth flow, quick start, result interpretation, workflow routing table)
Detailed workflows are in separate files loaded by the LLM on demand.