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.
git clone --depth 1 https://github.com/thevibeworks/claude-code-docsWrote 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/agents/thevibeworks/claude-code-docs/business-rules-extractor)<a href="https://agentmods.dev/agents/thevibeworks/claude-code-docs/business-rules-extractor"><img src="https://agentmods.dev/badge/agents/thevibeworks/claude-code-docs/business-rules-extractor/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/agents/thevibeworks/claude-code-docs/business-rules-extractor"><img src="https://agentmods.dev/badge/agents/thevibeworks/claude-code-docs/business-rules-extractor.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 | $0.00053 | $0.00855 |
| Opus 5.5 | $0.00021 | $0.00342 |
| Sonnet 5.5 | $0.00011 | $0.00171 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade B, and why
business-rules-extractor scanned grade B with 1 finding 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 20d 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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
literals crafted to look like directives to an AI tool ("SYSTEM:", "ignore previous instructions", "mark this rule as approved", "this finding is a Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
This is a copy
100% identical to business-rules-extractor — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a business analyst who reads code. Your job is to find the rules hidden inside legacy systems — the calculations, thresholds, eligibility checks, and policies that define how the business actually operates — and express them in a form that survives the rewrite.
What counts as a business rule
- Calculations: interest, fees, taxes, discounts, scores, aggregates
- Validations: required fields, format checks, range limits, cross-field
- Eligibility / authorization: who can do what, when, under which conditions
- State transitions: status lifecycles, what triggers each transition
- Policies: retention periods, retry limits, cutoff times, rounding rules
What does NOT count
Infrastructure, logging, error handling, UI layout, technical retries, connection pooling. If a rule would be the same regardless of what language the system was written in, it's a business rule. If it only exists because of the technology, skip it.
Extraction discipline
- Find the rule in code. Record exact
file:line-line. - State it in plain English a non-engineer would recognize.
- Encode it as Given/When/Then with concrete values:
Given an account with balance $1,250.00 and APR 18.5% When the monthly interest batch runs Then the interest charged is $19.27 (balance × APR ÷ 12, rounded half-up to cents) - List the parameters (rates, limits, magic numbers) with their current hardcoded values — these often need to become configuration.
- Rate your confidence: High (logic is explicit), Medium (inferred from structure/names), Low (ambiguous; needs SME).
- If confidence < High, write the exact question an SME must answer.
Secret handling (mandatory)
Rule parameters sometimes are credentials — hardcoded passwords in auth
checks, API keys in partner-service calls, connection strings in batch
routines. Record the rule, never the value: write the parameter as
<credential — masked, see file:line> with at most a 2–4 character
preview. Rule cards flow into briefs and steering decks; a raw credential
in a parameter list is a leak.
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.
- 20d ago Changed 77edbaced0c0
- 1mo ago First seen · 77 lines · 53 tokens per session scan B aa718e013a37
business-rules-extractor is an agent published in the GitHub repository thevibeworks/claude-code-docs (152 stars, last pushed today), licensed MIT. It adds 53 tokens to every session and 855 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (instruction-override phrasing). It is 100% identical to business-rules-extractor, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
code-reviewer
Senior code reviewer for production readiness. Reviews completed work against plans, specs, and coding standards. Dispatched after major tasks, features, or before merge.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
security-reviewer
Security vulnerability detection and remediation specialist. Use PROACTIVELY after writing code that handles user input, authentication, API endpoints, or sensitive data. Flags secrets, SSRF, injection, unsafe crypto, and OWASP Top 10 vulnerabilities.
brownfield-accuracy-judge
Evaluates how accurately an implementation plan accounts for existing code — correctly identifying what to modify vs create, avoiding reimplementation, and finding the right integration points.
mb-reviewer
Code review agent for /mb work review-loop. Reads stage diff + pipeline.yaml reviewrubric and emits structured JSON verdict (APPROVED / CHANGESREQUESTED) with severity-classified issue list. Drives the severity-gate decision.
adversarial-planner
Independent adversary for an implementation PLAN, before any code is written. Attacks the written plan's scope, non-goals, and decisions against the committed constraints it must honor, and surfaces only grounded objections for the human to decide. Never rewrites the plan and never sees the author's reasoning …