Borrowing it
Nothing to install: this file belongs to sandeep-alluru/balancelab. 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/sandeep-alluru/balancelab/main/CLAUDE.mdgit clone --depth 1 https://github.com/sandeep-alluru/balancelabWrote 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/sandeep-alluru/balancelab/claude-md)<a href="https://agentmods.dev/instructions/sandeep-alluru/balancelab/claude-md"><img src="https://agentmods.dev/badge/instructions/sandeep-alluru/balancelab/claude-md.svg" alt="Measured on agentmods" 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 | $0.00312 | $0.00312 |
| Opus 5 | $0.00156 | $0.00156 |
| Sonnet 5 | $0.00062 | $0.00062 |
| Haiku 4.5 | $0.00031 | $0.00031 |
Grade A, and why
balancelab 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 7d 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.
What it actually says
balancelab — Session Anchor
One-liner: Adversarial game economy red-team — detect arbitrage exploits via graph-based analysis Phase: v0.1.0 shipped Stack: Python 3.10+, click, rich, sqlite3, fastapi
Key decisions
- Bellman-Ford on log-weight graph (weight = -log(exchange_rate)) detects negative cycles = exploits
- Content-addressed IDs (SHA-256[:16]) for deduplication
- SQLite-only backend — no network dependencies
- Pure Python — no NetworkX or external graph libraries
Module map
src/balancelab/
├── economy.py # EconomyRule, EconomyGraph, ExploitFinder (Bellman-Ford)
├── store.py # EconomyStore (SQLite CRUD)
├── report.py # print_report, to_json, to_markdown
├── cli.py # add, scan, report, log, status
├── api.py # FastAPI REST server
└── mcp_server.py # MCP server (optional dependency)
Next step
Extend with severity scoring: classify exploits as critical/high/medium based on gain_ratio thresholds.
Code conventions
- Python 3.10+, fully type-annotated, mypy strict
- Ruff rules: E F I N UP S B RUF
- No
print()in library code — userich.console.Console - All public functions and classes require docstrings
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.
- 7d ago First seen · 34 lines · 312 tokens per session scan A ba1a4eddfb4c
balancelab CLAUDE.md is an instructions file published in the GitHub repository sandeep-alluru/balancelab (0 stars, last pushed 20d ago), licensed MIT. It adds 312 tokens to every session, about $0.0016 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 2026-08-31.
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