irule-datagroup

name	irule-datagroup
description	Analyse an F5 iRule for opportunities to extract inline lookup patterns into BIG-IP data-groups. Uses the static extraction engine for mechanical conversions and AI reasoning for complex patterns. Type-aware: detects IP/CIDR, integer, and string data-groups. Use when extracting data-groups from iRules, optimising iRule lookup performance, converting inline iRule patterns to data-groups, or refactoring iRule switch/if chains.
allowed-tools	Bash, Read, Edit

iRule Data-Group Analysis

Analyse an iRule for data-group extraction opportunities.

Steps

1. Read the domain knowledge from ai/prompts/irules_system.md (includes data-groups reference)
2. Read the iRule file to analyse
3. Run the AI-enhanced data-group scanner to find all candidates:

   uv run --no-dev python ai/claude/tcl_ai.py suggest-datagroups $FILE
   

   This returns structured context for each candidate:
   • Pattern type (if_chain, switch, or_chain)
   • Inferred value type (ip, integer, string)
   • Whether CIDR notation is present
   • Body shape (identical, set_mapping, return_mapping, complex)
   • Confidence level (high = mechanical, medium = needs review, low = use judgement)
4. If the tool fails (e.g. file not found or parse error), report the error clearly and suggest fixes
5. For high-confidence candidates, run the static extractor:

   uv run --no-dev python ai/claude/tcl_ai.py extract-datagroup $FILE --line <LINE>
   

   This produces both the rewritten iRule code and the tmsh data-group definition.
6. For medium/low-confidence candidates, use AI reasoning to:
   • Choose an appropriate data-group name based on the domain context
   • Decide whether to consolidate related patterns into a single data-group
   • Determine the correct class match operator (equals, contains, starts_with)
   • Handle CIDR ranges correctly (IP data-groups with network prefixes)
   • Generate the data-group definition with proper typing
7. For each extraction, provide:
   • The original inline code
   • The replacement using class match / class lookup
   • The complete tmsh data-group definition:

     ltm data-group internal <name> {
         records {
             <key> { data <value> }
         }
         type <string|ip|integer>
     }
     

   • The performance benefit explanation
8. Apply the changes to the file using the Edit tool
9. If no data-group opportunities exist, explain why the current approach is acceptable

Data-group type reference

Value type	Examples	class operator
string	"/api/v1", "example.com"	equals, starts_with, contains
ip	10.0.0.0/8, 192.168.1.1, ::1	equals (supports CIDR matching)
integer	80, 443, 8080	equals

Common patterns

Membership test (identical bodies)

# Before: if/elseif chain
if { $host eq "a.com" } { pool web_pool } elseif { $host eq "b.com" } { pool web_pool }

# After: class match
if { [class match $host equals allowed_hosts] } { pool web_pool }

Value lookup (different bodies)

# Before: switch mapping
switch -exact -- $uri {
    "/api" { pool api_pool }
    "/web" { pool web_pool }
}

# After: class lookup
pool [class lookup $uri uri_pool_map]

IP allowlist with CIDR

# Before: nested if
if { [IP::addr [IP::client_addr] equals 10.0.0.0/8] } { ... }

# After: IP data-group (handles CIDR natively)
if { [class match [IP::client_addr] equals trusted_networks] } { ... }

$ARGUMENTS