AgentLint

Your AI agent is only as good as your repo. AgentLint finds what's broken — file structure, instruction quality, build setup, session continuity, security posture — and fixes it. 33 checks, every one backed by data.

We analyzed 265 versions of Anthropic's Claude Code system prompt, documented the hard limits, audited thousands of real repos, and reviewed the academic research. The result: a single command that tells you exactly what your AI agent is struggling with and why.

Install

curl -fsSL https://raw.githubusercontent.com/0xmariowu/agent-lint/main/scripts/install.sh | bash

Then start a new Claude Code session:

/al

That's it. AgentLint scans your projects, scores them, shows what's wrong, and fixes what it can.

What you get

$ /al

AgentLint — Score: 68/100

Findability      ██████████████░░░░░░  7/10
Instructions     ████████████████░░░░  8/10
Workability      ████████████░░░░░░░░  6/10
Safety           ██████████░░░░░░░░░░  5/10
Continuity       ██████████████░░░░░░  7/10

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 HTML report shows a segmented gauge, expandable dimension breakdowns with per-check detail, and a prioritized issues list. Before/after comparison when fixes are applied.

Why this matters

AI coding agents read your repo structure, docs, CI config, and handoff notes. They git push, trigger pipelines, and write files. A well-structured repo gets dramatically better AI output. A poorly structured one wastes tokens, ignores rules, repeats mistakes, and may expose secrets.

AgentLint is built on data most developers never see:

• 265 versions of Anthropic's Claude Code system prompt — every word added, deleted, and rewritten
• Claude Code internals — hard limits (40K char max, 256KB file read limit, pre-commit hook behavior) that silently break your setup
• Production security audits across open-source codebases — the gaps AI agents walk into
• 6 academic papers on instruction-following, context files, and documentation decay

What it checks

Findability — can AI find what it needs?

Instructions — are your rules well-written?

Workability — can AI build and test?

Continuity — can next session pick up?

Safety — is AI working securely?

Optional: AI Deep Analysis

Spawns AI subagents to find what mechanical checks can't:

• Contradictory rules that confuse the model
• Dead-weight rules the model would follow without being told
• Vague rules without decision boundaries

Optional: Session Analysis

Reads your Claude Code session logs to find:

• Instructions you repeat across sessions (should be in CLAUDE.md)
• Rules AI keeps ignoring (need rewriting)
• Friction hotspots by project

How scoring works

Each check produces a 0-1 score, weighted by dimension, scaled to 100.

Scores are measurements, not judgments. Reference values come from Anthropic's own data. You decide what to fix.

Update

claude plugin update agent-lint@agent-lint

Evidence

Every check cites its source. Full citations in standards/evidence.json.

Requirements

• Claude Code
• jq and node 20+

License

MIT