Borrowing it
Nothing to install: this file belongs to franzvill/lad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/franzvill/lad/main/CLAUDE.mdgit clone --depth 1 https://github.com/franzvill/ladWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/franzvill/lad/claude-md)<a href="https://agentmods.dev/instructions/franzvill/lad/claude-md"><img src="https://agentmods.dev/badge/instructions/franzvill/lad/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/franzvill/lad/claude-md"><img src="https://agentmods.dev/badge/instructions/franzvill/lad/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.01251 | $0.01251 |
| Opus 5.5 | $0.00500 | $0.00500 |
| Sonnet 5.5 | $0.00250 | $0.00250 |
| Haiku 4.5 | $0.00125 | $0.00125 |
Grade A, and why
lad CLAUDE.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 2mo 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
LAD-A2A (Local Agent Discovery for A2A) is an open protocol specification and reference implementation for discovering A2A-capable AI agents on local networks. It enables AI agents to find and connect to service agents when joining networks (hotels, offices, hospitals, etc.).
Key concepts:
- Discovery layer between physical networks and A2A protocol (MCP handles tools, A2A handles communication, LAD-A2A handles discovery)
- Zero-configuration discovery via mDNS/DNS-SD (
_lad-a2a._tcp.local), well-known endpoints (/.well-known/lad/agents), and DHCP - Security model assumes local networks are hostile by default
Common Commands
Reference Implementation
cd reference
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
# Run tests (conformance, security, and v0.2 hardening)
pytest tests/ -v
# Run single test
pytest tests/test_lad.py::TestDiscoveryEndpoint::test_discovery_required_fields -v
# Start server (development only - no TLS)
python -m server.lad_server --name "My Agent" --port 8080
# Start server with TLS (production)
python -m server.lad_server --name "My Agent" --port 443 \
--ssl-certfile /path/to/cert.pem --ssl-keyfile /path/to/key.pem
# Start server with AgentCard signing
python -m server.lad_server --name "My Agent" --signing-key keys/private.pem
# Run client
python -m client.lad_client --url http://localhost:8080
# Run client with verification
python -m client.lad_client --url https://agent.example.com \
--signing-public-key keys/public.pem --require-verified
# Run client with interactive consent
python -m client.lad_client --url http://localhost:8080 --with-consent
# Generate signing keys
python -c "from common.signing import generate_signing_keys; generate_signing_keys('keys/')"
Demo Application
cd demo
cp .env.example .env # Add OPENAI_API_KEY
./run_demo.sh # Opens http://localhost:8000
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.
- 2mo ago First seen · 127 lines · 1,251 tokens per session scan A 2fc0b5b57db1
lad CLAUDE.md is an instructions file published in the GitHub repository franzvill/lad (245 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 1,251 tokens to every session, about $0.0050 per session on Opus 5.5. 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.
lad CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| lad CLAUDE.mdfranzvill/lad | Instructions file | 245 | 4mo ago | Apache-2.0 | A | 1,251 | Claude Code |
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| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |