AgentLint

The linter for your agent harness.
ESLint was for the code humans wrote. AgentLint is for the context agents read.
🌐 Site · Blog · Install · Demo · Harness 101 · Checks · Evidence · FAQ · 中文
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> **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
> 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:
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
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:
- Anthropic's **2026 Agentic Coding Trends Report** found that teams maintaining a good context file report **40% fewer "bad suggestion" sessions**
- **DORA 2025 State of AI-Assisted Software Development** reached the same conclusion: AI is an amplifier — it accelerates teams with good harnesses and amplifies dysfunction in teams without them
- An **ETH Zurich study** found that *auto-generated* context files actually **reduce** agent success rates in 5 of 8 tested settings, and increase inference cost by **20–23%**
- A randomized controlled trial found developers using AI were **19% slower** on complex tasks — while believing they were 20% faster
- LangChain's February 2026 report: **70% of agent performance lives outside the model**. Same weights, different harness, different results.
Translation: a bad harness is worse than no harness. And almost nobody knows what a good one looks like.
**AgentLint is the first linter for the harness itself.**
What makes AgentLint different
Every check is backed by data, not opinions. The data comes from places most developers never look — and it's what lets us measure harness health rigorously:
- **265 versions** of Anthropic's own Claude Code system prompt — we tracked every single word they added, deleted, and rewrote. When they cut IMPORTANT from 12 uses to 4, we knew. When they removed every "You are a helpful assistant..." identity section, we knew.