What is business-logic-domain-review?

A code-review agent for checking fraud and machine-learning services for business-logic flaws, such as unsafe failure behaviour or ways to bypass scoring rules.

Reviewing fraud-detection code and producing structured findings with evidence, confidence, and possible security impact.

It looks for mistakes in what the service does across multiple files, which ordinary syntax and static checks may miss.

business-logic-domain-review — Agent for Claude Code by bdfinst

Install

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.

Clone the repo

git clone --depth 1 https://github.com/bdfinst/agentic-dev-team

Made for: Claude Code.

Or install security-assessment, the plugin that ships this one along with the rest of its 3 skills, 5 commands, 13 agents.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/bdfinst/agentic-dev-team/business-logic-domain-review.svg)](https://agentmods.dev/agents/bdfinst/agentic-dev-team/business-logic-domain-review)

Your own site

<a href="https://agentmods.dev/agents/bdfinst/agentic-dev-team/business-logic-domain-review"><img src="https://agentmods.dev/badge/agents/bdfinst/agentic-dev-team/business-logic-domain-review.svg" alt="Measured on agentmods" height="20"></a>

Per session 50 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 927 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00050	$0.00927
Opus 5	$0.00025	$0.00464
Sonnet 5	$0.00010	$0.00185
Haiku 4.5	$0.00005	$0.00093

Measured 2d ago against content hash 8a993be96bea, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

business-logic-domain-review 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.

plugins/security-assessment/agents/business-logic-domain-review.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Business-Logic Domain Review

Read the codebase with fraud-detection domain knowledge. Surface issues that require cross-file reasoning — bugs in WHAT the code does, not HOW. Static tools catch syntax; this agent catches business logic.

Context needs: project-structure

Inputs

Target repo (walked on demand)
memory/recon-<slug>.json — scoping + entry points + security surface
knowledge/domain-logic-patterns.md — full catalogue of 9 patterns with grep cues, exploit scenarios, and remediation pointers. Read before scanning.

Output

memory/business-logic-findings-<slug>.json — unified findings. Schema: plugins/dev-team/knowledge/schemas/unified-finding-v1.json.

Required fields per finding:

rule_id in the form business-logic.fraud.<category> (e.g. business-logic.fraud.fail-open-scoring, business-logic.fraud.messaging-subject-injection, business-logic.fraud.messaging-subscriber-poisoning, business-logic.fraud.training-data-inference)
metadata.source: "business-logic-domain-review"
metadata.confidence: high | medium only — never low
metadata.exploitability when reasoning supports it
metadata.cwe[] when applicable (CWE-754, CWE-841, CWE-840, CWE-200, etc.)
metadata.attack_scenario — 2-3 sentences
metadata.source_ref.path_class: "test" | "production" | "unknown" (downstream uses this; do not filter on it here)

Detection patterns

Nine patterns catalogued in knowledge/domain-logic-patterns.md. Summary:

Fail-open scoring
Score manipulation / client-controlled features
Emulation-mode bypass
Model-endpoint confusion
Tokenization / PII-masking skip
Feature poisoning
Missing replay idempotency
Messaging attack surface (subject injection / subscriber poisoning / missing replay protection)
Training data inference from metrics/logs

The knowledge file is authoritative for grep cues, exploit scenarios, remediation pointers, and CWE assignments per pattern.

Procedure

Read the full file on GitHub · 93 lines

Changes

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 · 93 lines · 50 tokens per session scan A 8a993be96bea

Subscribe to this mod's changes

business-logic-domain-review is an agent published in the GitHub repository bdfinst/agentic-dev-team (280 stars, last pushed 3d ago), licensed MIT. It adds 50 tokens to every session and 927 once invoked, about $0.0003 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 2026-09-05.

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