AI-SETUP(1)

NAME

ai-setupContinuously sync your AI setups with one command. Codebase tailor suited agent skills, MCPs and config files for…

SYNOPSIS

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DESCRIPTION

Continuously sync your AI setups with one command. Codebase tailor suited agent skills, MCPs and config files for Claude Code, Cursor, and Codex.

README

Caliber product demo

npm version license node Caliber Score Claude Code Cursor Codex


Try it — zero install, zero commitment

npx @rely-ai/caliber score

Score your AI agent config in 3 seconds. No API key. No changes to your code. Just a score.

Your code stays on your machine. Scoring is 100% local — no LLM calls, no code sent anywhere. Generation uses your own AI subscription (Claude Code, Cursor) or your own API key (Anthropic, OpenAI, Vertex AI). Caliber never sees your code.


Caliber scores, generates, and keeps your AI agent configs in sync with your codebase. It fingerprints your project — languages, frameworks, dependencies, architecture — and produces tailored configs for Claude Code, Cursor, and OpenAI Codex. When your code evolves, Caliber detects the drift and updates your configs to match.

Before / After

Most repos start with a hand-written CLAUDE.md and nothing else. Here's what Caliber finds — and fixes:

  Before                                    After caliber init
  ──────────────────────────────            ──────────────────────────────

Agent Config Score 35 / 100 Agent Config Score 94 / 100 Grade D Grade A

FILES & SETUP 6 / 25 FILES & SETUP 24 / 25 QUALITY 12 / 25 QUALITY 22 / 25 GROUNDING 7 / 20 GROUNDING 19 / 20 ACCURACY 5 / 15 ACCURACY 13 / 15 FRESHNESS 5 / 10 FRESHNESS 10 / 10 BONUS 0 / 5 BONUS 5 / 5

Scoring is deterministic — no LLM, no API calls. It cross-references your config files against your actual project filesystem: do referenced paths exist? Are code blocks present? Is there config drift since your last commit?

caliber score --compare main    # See how your branch changed the score

Audits first, writes second

Caliber never overwrites your existing configs without asking. The workflow mirrors code review:

  1. Score — read-only audit of your current setup
  2. Propose — generate or improve configs, shown as a diff
  3. Review — accept, refine via chat, or decline each change
  4. Backup — originals saved to .caliber/backups/ before every write
  5. Undocaliber undo restores everything to its previous state

If your existing config scores 95+, Caliber skips full regeneration and applies targeted fixes to the specific checks that are failing.

How It Works

Caliber is not a one-time setup tool. It's a loop:

          caliber score
              │
              ▼
  ┌──── caliber init ◄────────────────┐
  │     (generate / fix)              │
  │           │                       │
  │           ▼                       │
  │     your code evolves             │
  │     (new deps, renamed files,     │
  │      changed architecture)        │
  │           │                       │
  │           ▼                       │
  └──► caliber refresh ──────────────►┘
       (detect drift, update configs)

Auto-refresh hooks run this loop automatically — on every commit or at the end of each AI coding session.

What It Generates

Claude Code

  • CLAUDE.md — Project context, build/test commands, architecture, conventions
  • CALIBER_LEARNINGS.md — Patterns learned from your AI coding sessions
  • .claude/skills/*/SKILL.md — Reusable skills (OpenSkills format)
  • .mcp.json — Auto-discovered MCP server configurations
  • .claude/settings.json — Permissions and hooks

Cursor

  • .cursor/rules/*.mdc — Modern rules with frontmatter (description, globs, alwaysApply)
  • .cursor/skills/*/SKILL.md — Skills for Cursor
  • .cursor/mcp.json — MCP server configurations

OpenAI Codex

  • AGENTS.md — Project context for Codex
  • .agents/skills/*/SKILL.md — Skills for Codex

Key Features

Any Codebase

TypeScript, Python, Go, Rust, Java, Ruby, Terraform, and more. Language and framework detection is fully LLM-driven — no hardcoded mappings. Caliber works on any project.

Any AI Tool

Target a single platform or all three at once:

caliber init --agent claude        # Claude Code only
caliber init --agent cursor        # Cursor only
caliber init --agent codex         # Codex only
caliber init --agent all           # All three
caliber init --agent claude,cursor # Comma-separated
Chat-Based Refinement

Not happy with the generated output? During review, refine via natural language — describe what you want changed and Caliber iterates until you're satisfied.

MCP Server Discovery

Caliber detects the tools your project uses (databases, APIs, services) and auto-configures matching MCP servers for Claude Code and Cursor.

Deterministic Scoring

caliber score evaluates your config quality without any LLM calls — purely by cross-referencing config files against your actual project filesystem.

CategoryPointsWhat it checks
Files & Setup25Config files exist, skills present, MCP servers, cross-platform parity
Quality25Code blocks, concise token budget, concrete instructions, structured headings
Grounding20Config references actual project directories and files
Accuracy15Referenced paths exist on disk, config freshness vs. git history
Freshness & Safety10Recently updated, no leaked secrets, permissions configured
Bonus5Auto-refresh hooks, AGENTS.md, OpenSkills format

Every failing check includes structured fix data — when caliber init runs, the LLM receives exactly what's wrong and how to fix it.

Session Learning

Caliber watches your AI coding sessions and learns from them. Hooks capture tool usage, failures, and your corrections — then an LLM distills operational patterns into CALIBER_LEARNINGS.md.

caliber learn install      # Install hooks for Claude Code and Cursor
caliber learn status       # View hook status, event count, and ROI summary
caliber learn finalize     # Manually trigger analysis (auto-runs on session end)
caliber learn remove       # Remove hooks

Learned items are categorized by type — [correction], [gotcha], [fix], [pattern], [env], [convention] — and automatically deduplicated.

Auto-Refresh

Keep configs in sync with your codebase automatically:

HookTriggerWhat it does
Git pre-commitBefore each commitRefreshes docs and stages updated files
Claude Code session endEnd of each sessionRuns caliber refresh and updates docs
Learning hooksDuring each sessionCaptures events for session learning
caliber hooks --install    # Enable refresh hooks
caliber hooks --remove     # Disable refresh hooks

The refresh command analyzes your git diff (committed, staged, and unstaged changes) and updates config files to reflect what changed.

Fully Reversible
  • Automatic backups — originals saved to .caliber/backups/ before every write
  • Score regression guard — if a regeneration produces a lower score, changes are auto-reverted
  • Full undocaliber undo restores everything to its previous state
  • Dry run — preview changes with --dry-run before applying

Commands

CommandDescription
caliber scoreScore config quality (deterministic, no LLM)
caliber score --compare <ref>Compare current score against a git ref
caliber initFull setup wizard — analyze, generate, review, install hooks
caliber regenerateRe-analyze and regenerate configs (aliases: regen, re)
caliber refreshUpdate docs based on recent code changes
caliber skillsDiscover and install community skills
caliber learnSession learning — install hooks, view status, finalize analysis
caliber hooksManage auto-refresh hooks
caliber configConfigure LLM provider, API key, and model
caliber statusShow current setup status
caliber undoRevert all changes made by Caliber

FAQ

Does it overwrite my existing configs?

No. Caliber shows you a diff of every proposed change. You accept, refine, or decline each one. Originals are backed up automatically.

Does it need an API key?

Scoring: No. caliber score runs 100% locally with no LLM.

Generation: Uses your existing Claude Code or Cursor subscription (no API key needed), or bring your own key for Anthropic, OpenAI, or Vertex AI.

What if I don't like what it generates?

Refine it via chat during review, or decline the changes entirely. If you already accepted, caliber undo restores everything. You can also preview with --dry-run.

Does it work with monorepos?

Yes. Run caliber init from any directory. caliber refresh can update configs across multiple repos when run from a parent directory.

Does it send my code anywhere?

Scoring is fully local. Generation sends your project fingerprint (not source code) to whatever LLM provider you configure — the same provider your AI editor already uses. Anonymous usage analytics (no code, no file contents) can be disabled via caliber config.

Add a Caliber badge to your repo

After scoring your project, add a badge to your README:

Caliber Score

Copy this markdown and replace 94 with your actual score:

![Caliber Score](https://img.shields.io/badge/caliber-SCORE%2F100-COLOR)

Color guide: brightgreen (90+), green (70-89), yellow (40-69), red (<40).

LLM Providers

No API key? No problem. Caliber works with your existing AI tool subscription:

ProviderSetupDefault Model
Claude Code (your seat)caliber config → Claude CodeInherited from Claude Code
Cursor (your seat)caliber config → CursorInherited from Cursor
Anthropicexport ANTHROPIC_API_KEY=sk-ant-...claude-sonnet-4-6
OpenAIexport OPENAI_API_KEY=sk-...gpt-4.1
Vertex AIexport VERTEX_PROJECT_ID=my-projectclaude-sonnet-4-6
Custom endpointOPENAI_API_KEY + OPENAI_BASE_URLgpt-4.1

Override the model for any provider: export CALIBER_MODEL=<model-name> or use caliber config.

Caliber uses a two-tier model system — lightweight tasks (classification, scoring) auto-use a faster model, while heavy tasks (generation, refinement) use the default. This keeps costs low and speed high.

Configuration is stored in ~/.caliber/config.json with restricted permissions (0600). API keys are never written to project files.

Vertex AI advanced setup
# Custom region
export VERTEX_PROJECT_ID=my-gcp-project
export VERTEX_REGION=europe-west1

# Service account credentials (inline JSON)
export VERTEX_PROJECT_ID=my-gcp-project
export VERTEX_SA_CREDENTIALS='{"type":"service_account",...}'

# Service account credentials (file path)
export VERTEX_PROJECT_ID=my-gcp-project
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
Environment variables reference
VariablePurpose
ANTHROPIC_API_KEYAnthropic API key
OPENAI_API_KEYOpenAI API key
OPENAI_BASE_URLCustom OpenAI-compatible endpoint
VERTEX_PROJECT_IDGCP project ID for Vertex AI
VERTEX_REGIONVertex AI region (default: us-east5)
VERTEX_SA_CREDENTIALSService account JSON (inline)
GOOGLE_APPLICATION_CREDENTIALSService account JSON file path
CALIBER_USE_CLAUDE_CLIUse Claude Code CLI (1 to enable)
CALIBER_USE_CURSOR_SEATUse Cursor subscription (1 to enable)
CALIBER_MODELOverride model for any provider
CALIBER_FAST_MODELOverride fast model for any provider

Requirements

  • Node.js >= 20
  • One LLM provider: your Claude Code or Cursor subscription (no API key), or an API key for Anthropic / OpenAI / Vertex AI

Contributing

See CONTRIBUTING.md for detailed guidelines.

git clone https://github.com/rely-ai-org/caliber.git
cd caliber
npm install
npm run dev      # Watch mode
npm run test     # Run tests
npm run build    # Compile

Uses conventional commitsfeat: for features, fix: for bug fixes.

License

MIT

This Project is awesome !

SEE ALSO

clihub3/26/2026AI-SETUP(1)