Add opt-in compact prompts mode to reduce LLM token usage

LLM analyzer prompts carry redundant content that inflates token consumption without improving detection quality:

• Line numbers are zero-padded (L001: instead of L1:), adding ~3 wasted characters per line across every file sent to the LLM.
• The meta-analyzer embeds each finding's context snippet in the prompt alongside the full line-numbered file content, duplicating the same code.
• The OverallAssessment field in MetaAnalyzerResult is generated by the LLM but never consumed downstream (the report node computes its own risk score).
• Several analyzer prompts (SSD, SDI, SQP, meta-analyzer, TP4) contain repeated phrasing, verbose formatting (horizontal rules, per-section rule-ID reminders already present in the
  summary table), and filler sentences.

Proposal: Introduce a SKILLSPECTOR_COMPACT_PROMPTS=true environment variable that activates condensed prompt variants. Default behavior stays identical for backward compatibility.
When enabled:

1. Line numbers drop zero-padding (L1: instead of L001:)
2. Meta-analyzer omits redundant finding context from the prompt
3. Meta-analyzer uses a slimmer structured output schema (no OverallAssessment)
4. All five LLM analyzer prompts use condensed variants (same semantic content, less formatting overhead)
5. BASE_ANALYSIS_PROMPT output guidelines shortened from 5 lines to 2

Estimated savings per scan: 400-800 input tokens from prompt condensation, ~375 input tokens per call from line numbering (on a 500-line file), 30-50 output tokens per meta-analyzer call from schema reduction, plus variable savings from context omission.

Locate the analyzer prompt definitions, BASE_ANALYSIS_PROMPT, MetaAnalyzerResult, and the line-number formatting described in the issue. Trace how prompt configuration is selected, then verify that the opt-in mode condenses all listed prompts and removes redundant meta-analyzer data while default behavior remains unchanged and the stated token savings are achieved.