Classic NAV C-AL Performance Analyzer

[!IMPORTANT] This tool targets Classic Microsoft Dynamics NAV (C-AL). It is NOT for Business Central AL. SETLOADFIELDS does not exist in Classic NAV — use the setloadfields sub-command only if you have explicitly migrated to Business Central.

Zero-dependency C-AL analysis tool using only Python standard library. Focuses on Classic NAV performance patterns.

✨ Key Features

✅ No external dependencies - Pure Python standard library
✅ No virtual environment needed - Works immediately
✅ Fast & lightweight - Simple regex-based parsing
✅ Windows compatible - No Unicode issues
✅ Offline capable - No API server required

⛔ Read-Only Rule — Never Modify Source Files

[!CAUTION] This skill is strictly read-only. It must never write to, overwrite, or modify any codeunit, table, or page source file (.cs, .c-al, or any other AL file).

Allowed actions:

Print a report to the console.

Save findings to a separate output/report file (JSON, Markdown, etc.).

If the user explicitly asks to apply a fix, copy the file first (e.g., <name>.patched.cs) and modify only the copy — never the original.

If a user asks to "fix" or "update" a codeunit, always confirm whether they want:

A report with suggested code changes (default), or

A copy of the file with the fix applied.

Never silently change source files.

Sub-Commands

1. list - Show All Codeunits

List all available .cs and .c-al files in the workspace.

2. analyze - Deep Codeunit Analysis

Analyze a specific codeunit with bottleneck detection and dependency extraction.

3. scan - Project-Wide Bottleneck Scan

Scan all codeunits for performance issues and generate ranked report.

4. optimize - Optimization Recommendations

Get actionable refactoring suggestions with code examples.

5. setloadfields - SETLOADFIELDS / SELECT-* Audit

Dedicated audit that finds every FINDSET, FINDFIRST, FIND, FINDLAST, or GET without SETLOADFIELDS and ranks them by urgency score — combining:

Column-width multiplier — wider tables (more fields) waste more network bytes per row.
Row-count multiplier — large tables amplify the cost of every unnecessary column.
Loop penalty — reads inside loops are fired N times, so the cost is multiplied.
FlowField bonus — Business Central calculates all FlowFields on a SELECT *; pinning fields with SETLOADFIELDS skips those expensive sub-queries entirely.

The table metadata JSON files can optionally include field_count (int) and has_flow_fields (bool) to enable precise scoring. If absent, the analyzer falls back to neutral multipliers.

Pre-Flight Setup (Run This First, Every Time)

Before any command, resolve two things: the Python executable and the codeunits, tables, or pages directories. Do both in one shot:

# 1. Find Python
PYTHON=$(command -v python3 || command -v python || echo "uv run python")
$PYTHON --version || { echo "Python not found. Install via: brew install python  OR  uv python install 3.12"; exit 1; }

# 2. Find the SKILL path
SKILL="$(dirname "$(dirname "$(find . -path '*codeunit-analyzer/scripts/analyze.py' | head -1)")")"

# 3. Find the codeunits, tables, or pages directories — check common locations
CODEUNITS_DIR=$(
  for candidate in codeunits data/codeunits src/codeunits .agents/data/codeunits; do
    [ -d "$candidate" ] && { echo "$candidate"; break; }
  done
)

# 4. If not found, search from project root
if [ -z "$CODEUNITS_DIR" ]; then
  CODEUNITS_DIR=$(find . -name "*.c-al" -o -name "*.cs" 2>/dev/null | head -1 | xargs dirname 2>/dev/null)
fi

# 5. Export so the script picks it up automatically
export CODEUNITS_DIR
echo "Python : $PYTHON"
echo "Skill  : $SKILL/scripts/analyze.py"
echo "Codeunits: $CODEUNITS_DIR"

If CODEUNITS_DIR is still empty, ask the user where their .cs/.c-al files are and set it manually: export CODEUNITS_DIR=/path/to/codeunits

With those three variables set, all commands become:

$PYTHON $SKILL/scripts/analyze.py list
$PYTHON $SKILL/scripts/analyze.py scan
$PYTHON $SKILL/scripts/analyze.py analyze <file>

Quick Start

No setup required beyond the pre-flight above.

# List all available codeunits
$PYTHON $SKILL/scripts/analyze.py list

# Analyze a specific codeunit
$PYTHON $SKILL/scripts/analyze.py analyze 1.cs

# Scan all codeunits for bottlenecks
$PYTHON $SKILL/scripts/analyze.py scan

# Save scan results to file
$PYTHON $SKILL/scripts/analyze.py scan -o bottlenecks.json

# Get optimization suggestions
$PYTHON $SKILL/scripts/analyze.py optimize 80.cs

# SETLOADFIELDS audit — project-wide (ranked by urgency)
$PYTHON $SKILL/scripts/analyze.py setloadfields

# SETLOADFIELDS audit — single file
$PYTHON $SKILL/scripts/analyze.py setloadfields 80.cs

# SETLOADFIELDS audit — save JSON, show top 10 tables
$PYTHON $SKILL/scripts/analyze.py setloadfields --top 10 -o slf_report.json

Usage Patterns

Pattern 1: List Files

User says:

"List all codeunits"
"Show me available files"
"What codeunits, tables, or pages can I analyze?"

Action:

$PYTHON $SKILL/scripts/analyze.py list

Output: Table showing: File name | Object name | ID

Pattern 2: Analyze Specific Codeunit or Table

User says:

"Analyze codeunit 80"
"Explain what codeunit 1.cs does"
"Analyze table 18"
"Analyze "

Action:

$PYTHON $SKILL/scripts/analyze.py analyze <filename>

Steps:

If no filename provided, run list first
Execute analysis command
Display bottlenecks sorted by severity
Show dependencies and side effects
If critical issues found, suggest running optimize

Output includes:

Codeunit overview (ID, name, procedures)
Performance bottlenecks with severity levels
Dependencies (tables referenced)
Side effects (INSERT/MODIFY/DELETE/COMMIT)

Pattern 3: Scan All Codeunits

User says:

"Scan for bottlenecks"
"Find all performance issues"
"Which codeunits are slow?"
"Performance audit"

Action:

$PYTHON $SKILL/scripts/analyze.py scan

Save to JSON:

$PYTHON $SKILL/scripts/analyze.py scan -o bottlenecks_$(date +%Y%m%d).json

Steps:

Scan all .cs and .c-al files
Detect bottlenecks in each
Sort by severity and score
Group by codeunit
Display top 10 offenders
Show critical issues first

Output includes:

Total issue count by severity
Top 10 worst codeunits (by score)
Critical issues detailed (first 5)
Optional JSON export for tracking

Pattern 4: Optimize Codeunit

User says:

"Optimize codeunit 80"
"How can I improve performance?"
"Fix bottlenecks in "
"Make it faster"

Action:

$PYTHON $SKILL/scripts/analyze.py optimize <filename>

Steps:

Analyze the file for bottlenecks
Group issues by severity
Create phased optimization plan
Show code examples for fixes
Estimate performance impact

Output includes:

Phase 1: Critical fixes (do first)
Phase 2: High priority fixes (do next)
Phase 3: Medium priority backlog
Recommendations summary
Estimated performance gains

Pattern 5: SETLOADFIELDS Audit

User says:

"Find all missing SETLOADFIELDS"
"Which tables are we doing SELECT * on?"
"We have wide tables — are we loading all columns everywhere?"
"SETLOADFIELDS performance audit"
"Where are we fetching all fields without filtering?"

Action (project-wide):

python .skills/codeunit-analyzer/scripts/analyze.py setloadfields

Action (single file):

python .skills/codeunit-analyzer/scripts/analyze.py setloadfields <filename>

Flags:

--top N — show only the N most urgent tables
--json — output raw JSON
-o <file> — save findings to a JSON file

Output includes:

Summary: total missing SETLOADFIELDS, loop occurrences, FlowField tables
Table ranked by combined urgency score (column-width × row-count × loop penalty)
Per-read findings with code fix examples

Interactive Flow

Scenario 1: User invokes with just "list"

User: /codeunit-analyzer list
→ Run: $PYTHON $SKILL/scripts/analyze.py list
→ Show table of all codeunits

Scenario 2: User wants to analyze but doesn't specify file

User: /codeunit-analyzer analyze
→ Run list first to show options
→ Ask: "Which file would you like to analyze?"
→ User selects file
→ Run: $PYTHON $SKILL/scripts/analyze.py analyze <selected_file>

Scenario 3: User provides filename directly

User: /codeunit-analyzer analyze 1.cs
→ Run: $PYTHON $SKILL/scripts/analyze.py analyze 1.cs
→ Display results immediately

Scenario 4: Generic request

User: "Find performance issues"
→ Infer command: scan
→ Run: $PYTHON $SKILL/scripts/analyze.py scan

Detected Bottleneck Patterns

The script detects these Classic NAV performance anti-patterns:

[!NOTE] Patterns are grouped by how they manifest in Classic NAV. SETLOADFIELDS (Business Central only) is intentionally excluded from the main scan.

1. N+1 Query (Critical - 150 points)

Database queries inside loops causing massive slowdown.

Example:

// ❌ Bad: Queries database for each iteration
REPEAT
  Customer.GET(SalesLine."Sell-to Customer No.");
  ...
UNTIL SalesLine.NEXT = 0;

// ✅ Good: Query once, process in memory
Customer.GET(SalesHeader."Sell-to Customer No.");
REPEAT
  ...
UNTIL SalesLine.NEXT = 0;

2. Explicit COMMIT (High - 100 points)

Manual transaction commits that break rollback behavior.

Example:

// ❌ Bad: Breaks automatic rollback
IF Customer.INSERT THEN BEGIN
  COMMIT;  // Don't do this!
  MESSAGE('Customer created');
END;

// ✅ Good: Let NAV handle transactions
IF Customer.INSERT THEN BEGIN
  MESSAGE('Customer created');
END;

3. Nested Loops with Writes (High - 120 points)

Database writes inside nested loops.

Example:

// ❌ Bad: Write operation in nested loop
REPEAT
  ItemEntry.SETRANGE("Item No.", SalesLine."No.");
  IF ItemEntry.FINDSET THEN
    REPEAT
      ItemEntry.Processed := TRUE;
      ItemEntry.MODIFY;  // Slow!
    UNTIL ItemEntry.NEXT = 0;
UNTIL SalesLine.NEXT = 0;

// ✅ Good: Use bulk operations
REPEAT
  ItemEntry.SETRANGE("Item No.", SalesLine."No.");
  ItemEntry.MODIFYALL(Processed, TRUE);  // Fast!
UNTIL SalesLine.NEXT = 0;

4. High Write Density (Medium - 60 points)

Too many individual write operations (>10 per codeunit).

Recommendation: Batch operations or use MODIFYALL/DELETEALL.

5. N+1 Queries Inside Loops (Critical)

Too many database calls inside REPEAT...UNTIL blocks — the single biggest Classic NAV performance killer.

6. LOCKTABLE Overuse (Critical / High)

Classic NAV uses page-level locking. LOCKTABLE inside a loop holds the lock for every iteration, blocking all concurrent users.

Example:

// ❌ Bad: lock held for entire loop
REPEAT
  Table.LOCKTABLE;
  Table.MODIFY;
UNTIL Table.NEXT = 0;

// ✅ Good: acquire once
Table.LOCKTABLE;
IF Table.FINDSET THEN
  REPEAT Table.MODIFY; UNTIL Table.NEXT = 0;

7. FlowField Overuse (High)

Each CALCFIELDS issues a separate SQL sub-query. 4+ calls in one procedure without caching is a Classic NAV sign of denormalisation or missing local variables.

Example:

// ❌ Bad: 4 separate sub-queries
Cust.CALCFIELDS(Balance);
Cust.CALCFIELDS('Sales (LCY)');
Cust.CALCFIELDS('Profit (LCY)');
Cust.CALCFIELDS('Balance Due');

// ✅ Good: one batched call + cache in variables
Cust.CALCFIELDS(Balance, 'Sales (LCY)', 'Profit (LCY)', 'Balance Due');
BalanceDue := Cust.'Balance Due';

8. Key / Index Misalignment (High / Medium)

Classic NAV table reads must follow the defined key order. Filtering on a non-leading key field causes a full table scan. The detector flags single-field SETRANGE filters on large tables as potential key misalignments.

Example:

// ❌ Bad: key is 'Document Type, No.' but we skip the first field
Line.SETRANGE('No.', DocNo);  // full scan!

// ✅ Good: lead with the primary key field
Line.SETRANGE('Document Type', DocType);
Line.SETRANGE('No.', DocNo);
IF Line.FINDSET THEN ...

Output Formatting

Console Output

================================================================================
[1] [CRITICAL] - N Plus One Query
================================================================================
Procedure: PostSalesOrder
Score: 150 points

Issue:
  Database query inside loop - causes N+1 query problem

Recommendation:
  Load all records at once using FINDSET, then process in memory

Code Example:
  // Use FINDSET outside loop
  IF Table.FINDSET THEN
    REPEAT
      // Process here
    UNTIL Table.NEXT = 0;

Severity Levels

[CRITICAL] - Immediate action required (150 points)
[HIGH] - Fix this sprint (100-120 points)
[MEDIUM] - Plan for next sprint (60 points)
[LOW] - Backlog item

JSON Export Format

[
  {
    "pattern": "n_plus_one_query",
    "severity": "critical",
    "score": 150,
    "procedure": "PostSalesOrder",
    "explanation": "Database query inside loop - causes N+1 query problem",
    "recommendation": "Load all records at once using FINDSET",
    "example": "// Use FINDSET outside loop\nIF Table.FINDSET THEN...",
    "codeunit": {
      "file": "80.cs",
      "object_name": "Sales-Post",
      "object_id": "80"
    }
  }
]

Environment Requirements

Minimal Setup ✨

Python: 3.10+ (standard library only)

Directory Structure

OneTrade/
├── scripts/
│   └── analyze.py          # Standalone script (zero deps)
├── data/
│   └── codeunits/          # Your .cs/.c-al files
└── .skills/
    └── codeunit-analyzer/  # This skill

That's it! No requirements.txt, no pip install, no virtual environment.

Error Handling

Common Errors

1. No codeunits found

Error: No codeunits found.
Expected directory: c:\Users\...\data\codeunits

Solution: Create the data directory and add your .cs or .c-al files.

2. File not found

Error: FileNotFoundError: '80.cs'

Solution: Run $PYTHON $SKILL/scripts/analyze.py list to see available files.

3. No bottlenecks detected

[OK] No bottlenecks detected - this codeunit is already optimized!

Solution: Great news! No action needed.

AL-Perf Integrated Anti-Patterns

The analyzer incorporates advanced source-correlated checks natively adopted from al-perf:

Missing SetLoadFields (Business Central only — not Classic NAV): Available via the setloadfields sub-command when explicitly targeting BC. Not triggered in the standard scan. Navision defaults to SELECT * across the network, bloating the buffer.
Unfiltered FindSet (High): Flags FINDSET operations lacking SETRANGE or SETFILTER, severely impacting SQL Server memory caching by pulling entire tables.
Event Subscriber Hotspots (Critical): Flags [EventSubscriber] procedures containing data-loops or heavy read operations. Because Navision Event Subscribers run globally and synchronously, any blocked loop here degrades the entire ERP execution context.

Troubleshooting

Script won't run

Check Python version:

python --version  # Should be 3.10+

Run from project root:

$PYTHON $SKILL/scripts/analyze.py list

No files listed

Check data directory:

ls data/codeunits/*.cs | wc -l

If empty, add your C-AL files to data/codeunits/.

Permission denied (Linux/Mac)

chmod +x scripts/analyze.py
$PYTHON $SKILL/scripts/analyze.py list

Performance Notes

List: < 1 second for 2000+ files
Analyze: < 2 seconds per file
Scan: Processes all files sequentially (~1-2 sec per file)
Optimize: < 2 seconds per file

Note: No caching implemented yet - each run re-parses files. This keeps the code simple and dependency-free.

Examples

Example 1: Quick list

python .skills/codeunit-analyzer/scripts/scripts/analyze.py list

Output:

Found 2414 codeunits:

File                 Object Name                                        ID
==================================================================================
1.cs                 ApplicationManagement                              1
80.cs                Sales-Post                                         80
...

Example 2: Analyze specific file

$PYTHON $SKILL/scripts/analyze.py analyze 80.cs

Output:

================================================================================
CODEUNIT: Sales-Post
================================================================================
ID: 80
Procedures: 15
File: 80.cs

================================================================================
PERFORMANCE BOTTLENECKS (5 issues)
================================================================================

Critical: 2
High: 2
Medium: 1

[Detailed bottleneck information follows...]

Example 3: Full scan

$PYTHON $SKILL/scripts/analyze.py scan -o scan_results.json

Output:

================================================================================
BOTTLENECK SCAN SUMMARY
================================================================================
Total Issues: 147
  Critical: 23
  High: 45
  Medium: 67
  Low: 12

================================================================================
TOP OFFENDERS (by total score)
================================================================================

1. Sales-Post (80.cs)
   Issues: 12 | Critical: 3 | High: 5 | Medium: 4
   Total Score: 850 points

[Top 10 worst codeunits listed...]

Full report saved to: scan_results.json

Tips

Start with scan - Get overview before drilling down
Save scan results - Track progress over time with dated files
Focus on critical - [CRITICAL] issues have biggest impact
Test after fixes - Verify changes don't break business logic
Use JSON export - Integrate with other tools or CI/CD

Limitations

This is a simplified parser using regex. It may not detect:

Complex nested logic
Dynamic table references
Indirect procedure calls
Some edge case patterns

For production use, consider the full analysis service with Pydantic schemas and comprehensive parsing.

Comparison: Standalone vs Full Service

Feature	Standalone Script	Full Service
Dependencies	None	Pydantic, Cashews, etc.
Setup	Zero	Virtual env + pip install
Speed	Fast	Faster (cached)
Accuracy	Good (regex)	Excellent (full parser)
Offline	✅ Yes	✅ Yes
Table metadata	❌ No	✅ Yes
AI explanations	❌ No	✅ Yes (with Azure)
Best for	Quick analysis	Production use

Use standalone for: Quick scans, CI/CD, no-dependency environments Use full service for: Detailed analysis, AI insights, production workflows

📄 Exporting Reports as PDF or DOCX

When the user asks for a PDF or Word document of the analysis results, use the dedicated community skills instead of building your own export:

Format	Skill	Install
Word (.docx)	`docx`	`https://skills.sh/anthropics/skills/docx`
PDF (.pdf)	`pdf`	`https://skills.sh/anthropics/skills/pdf`

Workflow:

Run the analyzer and capture the output (use --json or pipe to a file).
Hand the structured findings to the docx or pdf skill to format and export.

Trigger phrases that should prompt you to suggest / use these skills:

"export the report as a PDF"
"save this as a Word document"
"give me a .docx of the bottleneck report"
"I need a PDF of the scan results"

Related Files

Main Script: analyze.py
Documentation: .skills/README.md
Summary: .skills/SUMMARY.md
Project Docs: CLAUDE.md

Ready to use! No installation, no dependencies, just Python. 🚀

5. Page Navigation Trigger Abuse (High/Critical)

Heavy queries (GET >= 3), nested database writes (MODIFY), or PAGE.RUN calls inside OnAfterGetRecord and similar UI rendering triggers.

Recommendation: Cache records inside Temp tables or run heavy logic exclusively via user-explicit OnAction events.

codeunit-analyzer

Classic NAV C-AL Performance Analyzer

✨ Key Features

⛔ Read-Only Rule — Never Modify Source Files

Sub-Commands

1. list - Show All Codeunits

2. analyze - Deep Codeunit Analysis

3. scan - Project-Wide Bottleneck Scan

4. optimize - Optimization Recommendations

5. setloadfields - SETLOADFIELDS / SELECT-* Audit

Pre-Flight Setup (Run This First, Every Time)

Quick Start

Usage Patterns

Pattern 1: List Files

Pattern 2: Analyze Specific Codeunit or Table

Pattern 3: Scan All Codeunits

Pattern 4: Optimize Codeunit

Pattern 5: SETLOADFIELDS Audit

Interactive Flow

Detected Bottleneck Patterns

1. N+1 Query (Critical - 150 points)

2. Explicit COMMIT (High - 100 points)

3. Nested Loops with Writes (High - 120 points)

4. High Write Density (Medium - 60 points)

5. N+1 Queries Inside Loops (Critical)

6. LOCKTABLE Overuse (Critical / High)

7. FlowField Overuse (High)

8. Key / Index Misalignment (High / Medium)

Output Formatting

Console Output

Severity Levels

JSON Export Format

Environment Requirements

Minimal Setup ✨

Directory Structure

Error Handling

Common Errors

AL-Perf Integrated Anti-Patterns

Troubleshooting

Script won't run

No files listed

Permission denied (Linux/Mac)

Performance Notes

Examples

Example 1: Quick list

Example 2: Analyze specific file

Example 3: Full scan

Tips

Limitations

Comparison: Standalone vs Full Service

📄 Exporting Reports as PDF or DOCX

Related Files

5. Page Navigation Trigger Abuse (High/Critical)

More from bmsuisse/skills

autoresearch

deslop

coding-guidelines-python

init-app-stack

databricks-sql-autotuner

databricks-cli