Agent = Model + Harness. The model isn't the bottleneck anymore — the harness is.
Your AGENTS.md, CLAUDE.md, CI config, hooks, and .gitignore are the harness. When they're wrong, Claude Code, Cursor, and Codex ship AI slop. When they're right, agents compound.
AgentLint scores your harness across 51 deterministic checks on 6 core dimensions, plus 7 opt-in extended checks (Deep + Session) that use AI sub-agents and local Claude Code session logs when available. Evidence-backed. Zero opinions.
📚 Full docs, 20+ long-form guides, and the complete check catalog live at agentlint.app. Highlights: Writing a Good CLAUDE.md · The 33-Check Catalog · AGENTS.md vs CLAUDE.md · 中文博客.
Install
npm install -g agentlint-ai # CLI only — no Claude plugin yet
npx agentlint-ai install # opt-in: register /al Claude Code plugin
The first command installs the agentlint CLI on $PATH and does not touch ~/.claude/. The second command (one-time, opt-in) detects Claude Code, copies the /al slash command into ~/.claude/commands/, and registers the marketplace plugin. Side-effect details and uninstall path in INSTALL.md.
Then in any git repo:
agentlint check
In Claude Code (after running npx agentlint-ai install): run /al for the interactive scan-fix-report flow.
Using an AI coding agent? Point it at INSTALL.md — it's written to be read once and acted on.
What you get
$ /al
AgentLint — Score: 72/100 (core)
Findability ██████████████░░░░░░ 7/10
Instructions ████████████████░░░░ 8/10
Workability ████████████░░░░░░░░ 6/10
Safety ██████████░░░░░░░░░░ 5/10
Continuity ██████████████░░░░░░ 7/10
Harness ████████████████████ 10/10
Deep ░░░░░░░░░░░░░░░░░░░░ n/a (opt-in)
Session ░░░░░░░░░░░░░░░░░░░░ n/a (opt-in)
Fix Plan (7 items):
[guided] Pin 8 GitHub Actions to SHA (supply chain risk)
[guided] Add .env to .gitignore (AI exposes secrets)
[assisted] Generate HANDOFF.md
[guided] Reduce IMPORTANT keywords (7 found, Anthropic uses 4)
Select items → AgentLint fixes → re-scores → saves HTML report
The harness problem
In February 2026, Mitchell Hashimoto (HashiCorp) coined the term. OpenAI's Ryan Lopopolo formalized it days later. LangChain's Vivek Trivedy gave it the cleanest definition:
Agent = Model + Harness. If you're not the model, you're the harness.
The harness is every piece of code, configuration, and instruction that wraps an LLM and turns it into an agent. For coding agents, your harness includes:
AGENTS.md / CLAUDE.md — the persistent rules injected at session start.cursor/rules/, .github/copilot-instructions.md — tool-specific instruction layers- CI, pre-commit hooks,
.gitignore — the deterministic constraints the agent can't override
SECURITY.md, changelogs, handoff notes — the context that survives across sessions
Harness engineering is the discipline of designing those pieces so the agent stays reliable across hundreds of tool calls, not just the first ten.
The research is blunt:
