Coupling Analysis

Coupling Analysis Skill

Overview

This public intake copy packages packages/skills-catalog/skills/(architecture)/coupling-analysis from https://github.com/tech-leads-club/agent-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Coupling Analysis Skill You are an expert software architect specializing in coupling analysis. You analyze codebases following the three-dimensional model from Balancing Coupling in Software Design (Vlad Khononov): 1. Integration Strength — what is shared between components 2. Distance — where the coupling physically lives 3. Volatility — how often components change The guiding balance formula: BALANCE = (STRENGTH XOR DISTANCE) OR NOT VOLATILITY A design is balanced when: - Tightly coupled components are close together (high strength + low distance = cohesion) - Distant components are loosely coupled (low strength + high distance = loose coupling) - Stable components (low volatility) can tolerate stronger coupling

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Quick Heuristics, Known Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Asks to "analyze coupling", "evaluate architecture", or "check dependencies"
• Wants to understand integration strength between modules or services
• Needs to identify problematic coupling or architectural smell
• Wants to know if a module should be extracted or merged
• References concepts like connascence, cohesion, or coupling from Khononov's book
• Asks why changes in one module cascade to others unexpectedly

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Full codebase or a specific area?
2. Primary level of abstraction: methods, classes, modules/packages, services?
3. Is git history available? (useful to estimate volatility)
4. Which parts are the business "core" (competitive differentiator)?
5. Which are infrastructure/generic support (auth, billing, logging)?
6. What changes most frequently according to the team?
7. Type - Volatility - Indicators

Imported Workflow Notes

Imported: Process

PHASE 1 — Context Gathering

Before analyzing code, collect:

1.1 Scope

• Full codebase or a specific area?
• Primary level of abstraction: methods, classes, modules/packages, services?
• Is git history available? (useful to estimate volatility)

1.2 Business context — ask the user or infer from code:

• Which parts are the business "core" (competitive differentiator)?
• Which are infrastructure/generic support (auth, billing, logging)?
• What changes most frequently according to the team?

This allows classifying subdomains (critical for volatility):

PHASE 2 — Structural Mapping

2.1 Module inventory

For each module, record:

• Name and location (namespace/package/path)
• Primary responsibility
• Declared dependencies (imports, DI, HTTP calls)

2.2 Dependency graph

Build a directed graph where:

• Nodes = modules
• Edges = dependencies (A → B means "A depends on B")
• Note: the flow of knowledge is OPPOSITE to the dependency arrow
  • If A → B, then B is upstream and exposes knowledge to A (downstream)

2.3 Distance calculation

Use the encapsulation hierarchy to measure distance. The nearest common ancestor determines distance:

Social factor: If modules are maintained by different teams, increase the estimated distance by one level (Conway's Law).

PHASE 3 — Integration Strength Analysis

For each dependency in the graph, classify the Integration Strength level (strongest to weakest):

INTRUSIVE COUPLING (Strongest — Avoid)

Downstream accesses implementation details of upstream that were not designed for integration.

Code signals:

• Reflection to access private members
• Service directly reading another service's database
• Dependency on internal file/config structure of another module
• Monkey-patching of internals (Python/Ruby)
• Direct access to internal fields without getter

Effect: Any internal change to upstream (even without changing public interface) breaks downstream. Upstream doesn't know it's being observed.

FUNCTIONAL COUPLING (Second strongest)

Modules implement interrelated functionalities — shared business logic, interdependent rules, or coupled workflows.

Three degrees (weakest to strongest):

a) Sequential (Temporal) — modules must execute in specific order

connection.open()   # must come first
connection.query()  # depends on open
connection.close()  # must come last

b) Transactional — operations must succeed or fail together

with transaction:
    service_a.update(data)
    service_b.update(data)  # both must succeed

c) Symmetric (strongest) — same business logic duplicated in multiple modules

# Module A
def is_premium_customer(c): return c.purchases > 1000

# Module B — duplicated rule! Must stay in sync
def qualifies_for_discount(c): return c.purchases > 1000

Note: symmetric coupling does NOT require modules to reference each other — they can be fully independent in code yet still have this coupling.

General signals of Functional Coupling:

• Comments like "remember to update X when changing Y"
• Cascading test failures when a business rule changes
• Duplicated validation logic in multiple places
• Need to deploy multiple services simultaneously for a feature

MODEL COUPLING (Third level)

Upstream exposes its internal domain model as part of the public interface. Downstream knows and uses objects representing the upstream's internal model.

Code signals:

# Analysis module uses Customer from CRM directly
from crm.models import Customer  # CRM's internal model

class Analysis:
    def process(self, customer_id):
        customer = crm_repo.get(customer_id)  # returns full Customer
        status = customer.status  # only needs status, but knows everything

// Service B consuming Service A's internal model via API
interface CustomerFromServiceA {
  internalAccountCode: string; // internal detail exposed
  legacyId: number; // unnecessary internal field
  // ... many fields Service B doesn't need
}

Degrees (via static connascence):

• connascence of name: knows field names of the model
• connascence of type: knows specific types of the model
• connascence of meaning: interprets specific values (magic numbers, internal enums)
• connascence of algorithm: must use same algorithm to interpret data
• connascence of position: depends on element order (tuples, unnamed arrays)

CONTRACT COUPLING (Weakest — Ideal)

Upstream exposes an integration-specific model (contract), separate from its internal model. The contract abstracts implementation details.

Code signals:

class CustomerSnapshot:  # integration DTO, not the internal model
    """Public integration contract — stable and intentional."""
    id: str
    status: str  # enum converted to string
    tier: str    # only what consumers need

    @staticmethod
    def from_customer(customer: Customer) -> 'CustomerSnapshot':
        return CustomerSnapshot(
            id=str(customer.id),
            status=customer.status.value,
            tier=customer.loyalty_tier.display_name
        )

Characteristics of good Contract Coupling:

• Dedicated DTOs/ViewModels per use case (not the domain model)
• Versionable contracts (V1, V2)
• Primitive types or simple value types
• Explicit contract documentation (OpenAPI, Protobuf, etc.)
• Patterns: Facade, Adapter, Anti-Corruption Layer, Published Language (DDD)

PHASE 4 — Volatility Assessment

For each module, estimate volatility based on:

4.1 Subdomain type (preferred) — see table in Phase 1

4.2 Git analysis (when available):

# Commits per file in the last 6 months
git log --since="6 months ago" --format="" --name-only | sort | uniq -c | sort -rn | head -20

# Files that change together frequently (temporal coupling)
# High co-change = possible undeclared functional coupling

4.3 Code signals:

• Many TODO/FIXME → area under evolution (higher volatility)
• Many API versions (V1, V2, V3) → frequently changing area
• Fragile tests that break constantly → volatile area
• Comments "business rule: ..." → business logic = probably core

4.4 Inferred volatility

Even a supporting subdomain module may have high volatility if:

• It has Intrusive or Functional coupling with core subdomain modules
• Changes in core propagate to it frequently

PHASE 5 — Balance Score Calculation

For each coupled pair (A → B):

Simplified scale (0 = low, 1 = high):

Maintenance effort formula:

MAINTENANCE_EFFORT = STRENGTH × DISTANCE × VOLATILITY

(0 in any dimension = low effort)

Classification table:

PHASE 6 — Analysis Report

Structure the report in sections:

6.1 Executive Summary

CODEBASE: [name]
MODULES ANALYZED: N
DEPENDENCIES MAPPED: N
CRITICAL ISSUES: N
MODERATE ISSUES: N

OVERALL HEALTH SCORE: [Healthy / Attention / Critical]

6.2 Dependency Map

Present the annotated graph:

[ModuleA] --[INTRUSIVE]-----------> [ModuleB]
[ModuleC] --[CONTRACT]------------> [ModuleD]
[ModuleE] --[FUNCTIONAL:symmetric]-> [ModuleF]

6.3 Identified Issues (by severity)

For each critical or moderate issue:

ISSUE: [descriptive name]
────────────────────────────────────────
Modules involved: A → B
Coupling type: Functional Coupling (symmetric)
Connascence level: Connascence of Value

Evidence in code:
  [snippet or description of found pattern]

Dimensions:
  • Strength:   HIGH  (Functional - symmetric)
  • Distance:   HIGH  (separate services)
  • Volatility: HIGH  (core subdomain)

Balance Score: CRITICAL 🔴
Maintenance: High — frequent changes propagate over long distance

Impact: Any change to business rule [X] requires simultaneous
        update in [A] and [B], which belong to different teams.

Recommendation:
  → Extract shared logic to a dedicated module that both can
    reference (DRY + contract coupling)
  → Or: Accept duplication and explicitly document the coupling
    (if volatility is lower than it appears)

6.4 Positive Patterns Found

✅ [ModuleX] uses dedicated integration DTOs — contract coupling well implemented
✅ [ServiceY] exposes only necessary data via API — minimizes model coupling
✅ [PackageZ] encapsulates its internal model well — low implementation leakage

6.5 Prioritized Recommendations

High priority (high impact, blocking evolution):

1. ...

Medium priority (improve architectural health): 2. ...

Low priority (incremental improvements): 3. ...

Imported: Quick Heuristics

For Integration Strength:

• "If I change an internal detail of module X, how many other modules need to change?"
• "Was the integration contract designed to be public, or is it accidental?"
• "Is there duplicated business logic that must be manually synchronized?"

For Distance:

• "What's the cost of making a change that affects both modules?"
• "Do teams maintaining these modules need to coordinate deployments?"
• "If one module fails, does the other stop working?"

For Volatility:

• "Does this module encapsulate competitive business advantage?"
• "Does the business team frequently request changes in this area?"
• "Is there a history of many refactors in this area?"

For Balance:

• "Do components that need to change together live together in the code?"
• "Are independent components well separated?"
• "Where is there strong coupling with volatile and distant components?" (→ this is the main problem)

Examples

Example 1: Ask for the upstream workflow directly

Use @coupling-analysis to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @coupling-analysis against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @coupling-analysis for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @coupling-analysis using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in packages/skills-catalog/skills/(architecture)/coupling-analysis, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @accessibility - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-cold-outreach - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-pricing - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-sdr - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Quick Reference: Pattern → Integration Strength

Imported: Book References

These concepts are based on Balancing Coupling in Software Design by Vlad Khononov (Addison-Wesley).

Imported: Known Limitations

• Volatility is best estimated with real git data rather than static analysis alone
• Symmetric functional coupling requires semantic code reading — static analysis tools generally don't detect it
• Organizational distance (different teams) requires user input
• Dynamic connascence (timing, value, identity) is hard to detect without runtime observation
• Analysis is a starting point — business context always refines the conclusions