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Readme

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.

Caliber demo

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:

Score — read-only audit of your current setup
Propose — generate or improve configs, shown as a diff
Review — accept, refine via chat, or decline each change
Backup — originals saved to .caliber/backups/ before every write
Undo — caliber 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.

Category	Points	What it checks
Files & Setup	25	Config files exist, skills present, MCP servers, cross-platform parity
Quality	25	Code blocks, concise token budget, concrete instructions, structured headings
Grounding	20	Config references actual project directories and files
Accuracy	15	Referenced paths exist on disk, config freshness vs. git history
Freshness & Safety	10	Recently updated, no leaked secrets, permissions configured
Bonus	5	Auto-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:

Hook	Trigger	What it does
Git pre-commit	Before each commit	Refreshes docs and stages updated files
Claude Code session end	End of each session	Runs `caliber refresh` and updates docs
Learning hooks	During each session	Captures 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 undo — caliber undo restores everything to its previous state
Dry run — preview changes with --dry-run before applying

Commands

Command	Description
`caliber score`	Score config quality (deterministic, no LLM)
`caliber score --compare <ref>`	Compare current score against a git ref
`caliber init`	Full setup wizard — analyze, generate, review, install hooks
`caliber regenerate`	Re-analyze and regenerate configs (aliases: `regen`, `re`)
`caliber refresh`	Update docs based on recent code changes
`caliber skills`	Discover and install community skills
`caliber learn`	Session learning — install hooks, view status, finalize analysis
`caliber hooks`	Manage auto-refresh hooks
`caliber config`	Configure LLM provider, API key, and model
`caliber status`	Show current setup status
`caliber undo`	Revert 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:

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:

Provider	Setup	Default Model
Claude Code (your seat)	`caliber config` → Claude Code	Inherited from Claude Code
Cursor (your seat)	`caliber config` → Cursor	Inherited from Cursor
Anthropic	`export ANTHROPIC_API_KEY=sk-ant-...`	`claude-sonnet-4-6`
OpenAI	`export OPENAI_API_KEY=sk-...`	`gpt-4.1`
Vertex AI	`export VERTEX_PROJECT_ID=my-project`	`claude-sonnet-4-6`
Custom endpoint	`OPENAI_API_KEY` + `OPENAI_BASE_URL`	`gpt-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

Variable	Purpose
`ANTHROPIC_API_KEY`	Anthropic API key
`OPENAI_API_KEY`	OpenAI API key
`OPENAI_BASE_URL`	Custom OpenAI-compatible endpoint
`VERTEX_PROJECT_ID`	GCP project ID for Vertex AI
`VERTEX_REGION`	Vertex AI region (default: `us-east5`)
`VERTEX_SA_CREDENTIALS`	Service account JSON (inline)
`GOOGLE_APPLICATION_CREDENTIALS`	Service account JSON file path
`CALIBER_USE_CLAUDE_CLI`	Use Claude Code CLI (`1` to enable)
`CALIBER_USE_CURSOR_SEAT`	Use Cursor subscription (`1` to enable)
`CALIBER_MODEL`	Override model for any provider
`CALIBER_FAST_MODEL`	Override 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 commits — feat: for features, fix: for bug fixes.

License

MIT