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 skills add SkillMedev/legacy-modernization --skill business-rule-extractorgit clone --depth 1 https://github.com/SkillMedev/legacy-modernizationWrote 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/skills/skillmedev/legacy-modernization/business-rule-extractor)<a href="https://agentmods.dev/skills/skillmedev/legacy-modernization/business-rule-extractor"><img src="https://agentmods.dev/badge/skills/skillmedev/legacy-modernization/business-rule-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/skills/skillmedev/legacy-modernization/business-rule-extractor"><img src="https://agentmods.dev/badge/skills/skillmedev/legacy-modernization/business-rule-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 | $0.00129 | $0.01005 |
| Opus 5 | $0.00064 | $0.00502 |
| Sonnet 5 | $0.00026 | $0.00201 |
| Haiku 4.5 | $0.00013 | $0.00101 |
Grade A, and why
Business Rule Extractor 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 10d 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.
This is a copy
100% identical to Business Rule 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Business Rule Extractor
Turn undocumented legacy logic into a written rule inventory so a rewrite preserves behavior instead of silently changing it. The source code is the spec, and most of it is accidental - your job is to surface what it actually does, not what it should do.
Workflow
- Scope the extraction target. Identify the exact module, files, and entry points to be rewritten. List every external system, shared table, global, and session state the logic touches - a rule split across files is the one most likely to break on rewrite.
- Mine rules from conditionals and constants. For each branch, guard, magic number, string literal, and validation, ask "what business reality does this encode?"
amount > 10000is a large-transaction threshold; a hardcoded date is a regulation start; a branch on one customer ID is a negotiated exception. Magic numbers are almost always undocumented policy. Treat any constant that appears in two or more places as a single rule with duplication risk - note every occurrence, because a rewrite that updates one copy and misses the other creates a divergence bug. - Record order and interaction. Capture precedence (which discount wins when two apply), short-circuits, fall-through defaults, and exact boundary handling of null, empty, negative, and
>vs>=. Evaluation order is itself a rule; note every place an input path forks. - Separate intended rules from bug-as-features. Code alone cannot tell you which behaviors are deliberate. Flag each questionable behavior and confirm against domain experts, support tickets, or production data - aim for at least two independent sources (e.g. one expert plus production evidence) before labeling a behavior intended. Document bug-as-features explicitly so the rewrite team decides consciously, not in an incident.
- Capture each rule as input-to-output examples. For every rule, record concrete cases from real data, especially at boundaries - a minimum of 3 cases per rule (below, at, and above each boundary), and 10+ for rules with interacting conditions. A table of cases removes the ambiguity that "applies a tiered discount" leaves behind.
- Hand off open questions. Before declaring a path dead, check production logs or traffic over a window that covers the business cycle - 90 days is a practical floor, a full year for anything with annual/seasonal triggers. Mark provably dead paths as dead rather than reverse-engineering intent. List behaviors you could not classify as explicit questions for domain owners.
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.
- 10d ago First seen · 37 lines · 129 tokens per session scan A f3f96779eb81
Business Rule Extractor is a skill published in the GitHub repository SkillMedev/legacy-modernization (2 stars, last pushed 2mo ago), licensed MIT. It adds 129 tokens to every session and 1,005 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Business Rule Extractor, differing in 0 lines, and is treated as a copy.
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