Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/harbor-framework/harbor/agents-mdgit clone --depth 1 https://github.com/harbor-framework/harborWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.04064 | $0.04064 |
| Opus 5 | $0.02032 | $0.02032 |
| Sonnet 5 | $0.00813 | $0.00813 |
| Haiku 4.5 | $0.00406 | $0.00406 |
Grade A, and why
harbor AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 385 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md - Harbor Framework
Breaking changes: See CHANGELOG.md for recent breaking changes to the agent and environment APIs and migration guidance. Do not add anything that is temporary or not a major change.
Contributing
Refer to the CONTRIBUTING.md file for guidelines on how to contribute.
Project Overview
Harbor is a framework for evaluating and optimizing AI agents and language models. It provides:
- Agent Evaluation: Run evaluations of arbitrary agents (Claude Code, OpenHands, Codex CLI, Aider, etc.) against benchmark tasks
- Benchmark Support: Interface with standard benchmarks (SWE-Bench, Terminal-Bench, Aider Polyglot, etc.)
- Parallel Execution: Conduct experiments in thousands of environments in parallel via providers like Daytona and Modal
- RL Optimization: Generate rollouts for reinforcement learning optimization
Quick Start Commands
# Install
uv tool install harbor
# Run a benchmark
harbor run --dataset [email protected] --agent claude-code --model anthropic/claude-opus-4-1 --n-concurrent 4
# Pass environment variables to the agent
harbor run --dataset [email protected] --agent claude-code --model anthropic/claude-opus-4-1 \
--ae AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
--ae AWS_REGION=us-east-1
# List available datasets
harbor datasets list
# Get help
harbor run --help
Repository Structure
This is a monorepo containing the Harbor CLI, documentation website, and results viewer.
harbor/
├── src/harbor/ # Main CLI source code
│ ├── agents/ # Agent implementations
│ │ ├── base.py # BaseAgent abstract class
│ │ ├── factory.py # Agent factory for instantiation
│ │ ├── installed/ # Built-in agent implementations
│ │ ├── terminus_2/ # Terminus agent implementation
│ │ ├── oracle.py # Oracle agent (for testing)
│ │ └── nop.py # No-op agent
│ ├── cli/ # Command-line interface (Typer-based)
│ │ ├── main.py # Main CLI entry point
│ │ ├── jobs.py # Job management commands
│ │ ├── datasets.py # Dataset commands
│ │ ├── trials.py # Trial management
│ │ ├── tasks.py # Task management
│ │ ├── traces.py # Trace viewing
│ │ ├── sweeps.py # Parameter sweeps
│ │ ├── adapters.py # Adapter commands
│ │ ├── adapter_wizard.py # Interactive adapter creation
│ │ ├── publish.py # Package publishing
│ │ ├── analyze.py # Analysis commands
│ │ ├── cache.py # Cache management
│ │ ├── view.py # Results viewing
│ │ ├── admin/ # Admin commands
│ │ ├── annotator/ # Annotation tools
│ │ ├── quality_checker/ # Quality verification
│ │ ├── template-adapter/ # Adapter templates
│ │ ├── template-metric/ # Metric templates
│ │ └── template-task/ # Task templates
│ ├── environments/ # Execution environments
│ │ ├── base.py # BaseEnvironment abstract class
│ │ ├── factory.py # Environment factory
│ │ ├── docker/ # Local Docker environment
│ │ ├── daytona.py # Daytona cloud environment
│ │ ├── e2b.py # E2B environment
│ │ ├── modal.py # Modal environment
│ │ ├── runloop.py # Runloop environment
│ │ ├── apple_container.py # Apple container environment
│ │ ├── gke.py # Google Kubernetes Engine
│ │ ├── openshift.py # Red Hat Openshift environment
│ │ └── novita.py # Novita AI Sandbox environment
│ ├── models/ # Pydantic data models
│ │ ├── agent/ # Agent context and metadata
│ │ ├── job/ # Job configuration and results
│ │ ├── task/ # Task configuration
│ │ ├── trial/ # Trial configuration and results
│ │ ├── metric/ # Metric definitions
│ │ ├── package/ # Package registry models
│ │ ├── trajectories/ # ATIF trajectory format
│ │ ├── verifier/ # Verification results
│ │ └── registry.py # Dataset registry models
│ ├── orchestrators/ # Trial orchestration
│ ├── verifier/ # Test verification system
│ ├── inspect/ # Inspection utilities
│ ├── analyze/ # Analysis backend (LLM-powered)
│ ├── auth/ # Authentication (OAuth callback server)
│ ├── publisher/ # Package publishing and registry DB
│ ├── storage/ # Storage backends (Supabase)
│ ├── db/ # Database types
│ ├── llms/ # LLM integrations (LiteLLM)
│ ├── dataset/ # Dataset handling
│ ├── registry/ # Dataset registry
│ ├── tasks/ # Task utilities
│ ├── trial/ # Trial utilities
│ ├── metrics/ # Metrics collection
│ ├── mappers/ # Data mappers
│ ├── viewer/ # Results viewer UI
│ └── utils/ # Utility functions
├── adapters/ # Benchmark adapters (convert external datasets)
├── apps/
│ └── viewer/ # Results viewer web app (React Router, Vite)
├── docs/ # Documentation website (Next.js, Fumadocs)
├── examples/ # Example configurations and tasks
│ ├── tasks/ # Example task definitions
│ ├── agents/ # Agent configuration examples
│ ├── configs/ # Job configuration examples
│ ├── datasets/ # Dataset examples
│ ├── metrics/ # Custom metrics examples
│ ├── prompts/ # Prompt templates
│ └── training/ # Training examples
├── rfcs/ # RFC specifications
├── scripts/ # Utility scripts
├── skills/ # Claude Code skills
├── tests/ # Test suite
│ ├── unit/ # Unit tests
│ ├── integration/ # Integration tests
│ ├── runtime/ # Runtime tests (may need Docker)
│ └── golden/ # Golden file tests
├── dataset/ # Local dataset storage (jobs/)
├── jobs/ # Job output storage
└── trials/ # Trial output storage
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 385 lines · 4,064 tokens per session scan A 0fef49bc61d6
harbor AGENTS.md is an instructions file published in the GitHub repository harbor-framework/harbor (4,854 stars, last pushed today), licensed Apache-2.0. It adds 4,064 tokens to every session, about $0.0203 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
waku-agent CLAUDE.md
Instructions for ShenSeanChen/waku-agent, covering waku-agent — working conventions, architecture map (file ↔ diagram box), rules and commands.
Tracely-ai CLAUDE.md
Claude Code instructions for Jwuthri/Tracely-ai, covering claude.md, commands, architecture, hard rules and gotchas.
mastra AGENTS.md
AGENTS.md instructions for mastra-ai/mastra: Unless asked, don't inspect reference or modify examples. Use the most-specific AGENTS.md; for package work, read packages/ /AGENTS.md first.
AgentEval copilot-instructions.md
Instructions for AgentEvalHQ/AgentEval, covering agenteval - ai coding agent instructions, architecture overview, environment setup, optional: secondary models for comparison and build & test commands.
AgentEval maf-upgrade-preparation.instructions.md
Instructions for analyzing a new MAF version and producing an upgrade plan BEFORE updating the NuGet package.
AgentEval tracing.instructions.md
Guidelines for implementing trace recording and replay.