extract-business-rules
Skill4 starsChanged 3 months agoMITView on GitHub
WHEN/WHERE/WHO: [Scheduling: Business analysts or agents reviewing user stories or PRDs that contain embedded business logic.] HOW: [Structural: Use this SKILL to extract policies, laws, and calculations from text and categorize them into a strict Business Rules Taxonomy.] WHY: [Scheduling: Separating rules allows for dynamic configuration. Hardcoding a rule as a feature creates legacy debt.]
In this repository61 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
10 rules. About 0 are common, 10 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈10
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
When this skill is activated, first load skill-common and apply the shared conventions it defines: canonical section order, frontmatter …
- Distinctive: line 33
If a user story says, "The system shall charge 7% tax for state residents," the *functional requirement* is to calculate tax.
- Distinctive: line 47
Transfer Check: Verify the workflow still works for user stories, PRDs, and legacy-code reverse engineering.
- Distinctive: line 48
Stop Rule: If the input is purely technical or non-functional, stop and redirect before extracting business rules.
- Distinctive: line 52
Do not invent rules. If a calculation is implied but not stated, flag it as a question.
- Distinctive: line 55
If the input text is purely technical (e.g., "The API must respond in 200ms"), stop and inform the user that these are Non-Functional …
- Distinctive: line 59
Read the input text and identify any statements that represent corporate policies, government regulations, industry standards, or …
- Distinctive: line 67
If a rule implies a functional requirement (e.g., "A user must be 18 to register" implies the system needs to ask for a birthdate), note …
- Distinctive: line 82
Verify that no extracted rule dictates a specific software implementation (e.g., "Must use a dropdown" is invalid).
- Distinctive: line 83
Ensure every rule has a defined taxonomy type.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Extract Business Rules
Common Contract (Load First)
When this skill is activated, first load skill-common and apply the shared conventions it defines: canonical section order, frontmatter rules, anti-pattern guidance, and the Review In Mind loop in review-in-mind. This skill adds only domain-specific rules below.
Who
Business Analysts, Product Owners, and AI Agents. The agent uses this skill to decouple business logic from functional requirements.
What
This skill implements the Business Rules methodology from Software Requirements (3rd Edition). It scans requirements text, identifies statements that dictate business behavior (rather than software behavior), and classifies them into one of five categories:
- Facts: True statements about the business domain.
- Constraints: Rules that restrict actions.
- Action Enablers: Conditions that trigger actions.
- Inferences: Derived truths based on conditions.
- Computations: Mathematical formulas.
When
Invoke this skill during requirements analysis, when reviewing user stories, or when reverse-engineering legacy code. Trigger phrases include: "extract business rules," "find the rules in this text," "decouple the logic," or "what are the policies here."
Where
Applies to User Stories, PRDs, acceptance criteria, and interview transcripts.
Why
If a user story says, "The system shall charge 7% tax for state residents," the functional requirement is to calculate tax. The business rule is the 7% rate. If you don't separate them, the developer hardcodes 0.07, and when the law changes, the code breaks.
Inputs
- PRDs, User Stories, or transcripts containing business logic.
Output (Logical Evidence)
- A catalog of extracted Business Rules, tagged by taxonomy type.
Optimization Readiness
- Failure Signals: Software behavior is mistaken for a business rule, implied calculations stay unflagged, taxonomy labels are missing, or the extraction invents policy details.
- Evidence To Collect: Rule catalogs, taxonomy assignments, implied-functional-requirement notes, and examples where the extracted rule clearly separated business logic from software behavior.
- Safe Mutation Boundaries: Refine extraction prompts, taxonomy guidance, implied-requirement notes, and output formatting without changing the core rule-extraction workflow.
- Acceptance Criteria: Accept revisions only if each rule is categorized correctly, no software implementation details leak into the rule list, and uncertain calculations are surfaced as questions.
- Rejected Revision Handling: Record implementation-style statements, unsupported rule inferences, and ambiguous taxonomies so they are not repeated.
- Transfer Check: Verify the workflow still works for user stories, PRDs, and legacy-code reverse engineering.
- Stop Rule: If the input is purely technical or non-functional, stop and redirect before extracting business rules.
Constraints (Logical Boundaries)
- Business rules are NOT software functions. "The system shall display a warning" is a functional requirement, not a business rule.
- Do not invent rules. If a calculation is implied but not stated, flag it as a question.
One More Thing
If the input text is purely technical (e.g., "The API must respond in 200ms"), stop and inform the user that these are Non-Functional Requirements, not Business Rules.
How (Structural Workflow)
Phase 1: Rule Extraction
- Read the input text and identify any statements that represent corporate policies, government regulations, industry standards, or mathematical calculations.
- Extract these statements, separating them from the software features that might implement them.
Phase 2: Classification
- Classify each extracted rule into one of the five categories defined in the Business Rules Taxonomy (Fact, Constraint, Action Enabler, Inference, Computation).
Phase 3: Formatting
- Output the extracted rules in a structured list or table, including an ID, Type, and the Rule Definition.
- If a rule implies a functional requirement (e.g., "A user must be 18 to register" implies the system needs to ask for a birthdate), note the implied functional requirement.
Resources
- Business Rules Taxonomy
Review In Mind (ReviewInMindGenie)
Execute the common review loop in review-in-mind before delivering.
Review lens for this skill:
- Is each extracted rule classified correctly (fact/constraint/enabler/inference/computation)?
- Does the rule text preserve the original business intent and scope?
- Are software-implementation details excluded from business rules?
Validation
- Verify that no extracted rule dictates a specific software implementation (e.g., "Must use a dropdown" is invalid).
- Ensure every rule has a defined taxonomy type.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
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- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

