metrics-tokens

You perform deep analysis of token usage efficiency. You compare AIWG workflow token consumption against the MetaGPT 124 tokens/line benchmark (REF-013), identify high-cost operations, and surface optimization opportunities.

Triggers

Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):

• "how efficient are my tokens" → efficiency ratio vs MetaGPT baseline
• "am I above the baseline" → threshold status check
• "where are tokens being wasted" → per-step breakdown with recommendations
• "token ratio" → tokens/line ratio calculation

Trigger Patterns Reference

aiwg metrics-tokens

aiwg metrics-tokens --session current

aiwg metrics-tokens --threshold

aiwg metrics-tokens --by-step

aiwg metrics-tokens --optimize

Behavior

When triggered:

1. Determine scope:

   • Default: current or most recent session

   • --session <name>

     : named session

   • --all

     : aggregate across all sessions

2. Load token data:

   • Read

     .aiwg/ralph/sessions/*/metrics.json

     for raw token counts
   • Apply estimation heuristic: 4 chars per token (aligned with

     src/metrics/token-counter.ts

     )

3. Compute efficiency metrics:

   • Tokens/line ratio for session output

   • vsBenchmark

     : percentage vs MetaGPT 124 tokens/line (negative = better)

   • vsBaseline

     : percentage vs typical LLM 200 tokens/line (negative = better)
   • Threshold status: green (≤124), yellow (125–150), red (>150)

4. Run the command:

   # Default efficiency report
   aiwg metrics-tokens
   
   # Current session
   aiwg metrics-tokens --session current
   
   # Per-step breakdown
   aiwg metrics-tokens --by-step
   
   # With optimization suggestions
   aiwg metrics-tokens --optimize
   
   # JSON output
   aiwg metrics-tokens --json
   

Benchmark Reference

The MetaGPT 124 tokens/line benchmark comes from REF-013 (research corpus). It represents a validated efficiency target for AI-assisted software workflows. AIWG tracks against this benchmark to make token costs legible and comparable across sessions.

Comparison points:

Report Format

Standard Efficiency Report

Token Efficiency — Session: sdlc-review-20260401-143022
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Token Counts
  Input:    42,310 tokens
  Output:   18,940 tokens
  Total:    61,250 tokens

Content Metrics
  Characters:     245,000
  Non-blank lines:    548
  Total lines:        621

Efficiency
  Tokens/line:    112
  vs MetaGPT:     -9.7%  (better than 124 tokens/line benchmark)
  vs LLM baseline: -44%  (well below 200 tokens/line typical)
  Status:         green

Threshold: green — at or below MetaGPT benchmark

Per-Step Breakdown (

--by-step

)

Token Efficiency by Step
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Step                    Tokens    Lines  Tokens/Line  Status
──────────────────────  ────────  ─────  ───────────  ──────
architecture-designer   18,200    168    108          green
security-architect      14,600    132    111          green
test-architect          13,100    119    110          green
technical-writer        15,350    129    119          green  ← highest volume
                        ──────────────────────────────────
Total                   61,250    548    112          green

Optimization Report (

--optimize

)

Optimization Suggestions
━━━━━━━━━━━━━━━━━━━━━━━━

Status: green — no critical optimizations needed.

Opportunities (optional):
  1. technical-writer (119 tok/line) — near benchmark ceiling.
     Consider: scope the synthesis prompt to final merge only,
     avoid re-reading full drafts.

  2. architecture-designer (18,200 tokens) — highest absolute cost.
     Consider: pass only the relevant SAD section, not the full doc.

Efficiency Calculation

Token efficiency uses the estimation and comparison logic from

src/metrics/token-counter.ts

tokens          = ceil(characters / 4)
tokensPerLine   = tokens / nonBlankLines
vsBenchmark     = (tokensPerLine - 124) / 124 * 100   (negative = better)
vsBaseline      = (tokensPerLine - 200) / 200 * 100   (negative = better)

Examples

Example 1: Quick efficiency check

User: "Token efficiency for this session"

Action:

aiwg metrics-tokens

Response: Efficiency report with tokens/line ratio, benchmark comparison, and green/yellow/red status.

Example 2: Identify expensive steps

User: "Which step used the most tokens?"

Action:

aiwg metrics-tokens --by-step

Response: Per-step table showing token counts, line counts, tokens/line ratio, and threshold status for each workflow step.

Example 3: Optimization pass

User: "Suggest ways to reduce token usage"

Action:

aiwg metrics-tokens --optimize

Response: Optimization suggestions targeted at steps above the green threshold, with specific prompt-scoping recommendations.

Example 4: Are we at green?

User: "Are we at green on token efficiency?"

Extraction: Threshold check

Action:

aiwg metrics-tokens --threshold

Response: "Threshold status: green — 112 tokens/line, 9.7% below the MetaGPT 124 tokens/line benchmark (REF-013)."

Clarification Prompts

If the session scope is unclear:

• "Should I analyze the current running session or the most recent completed session?"

References

• @$AIWG_ROOT/src/cli/handlers/subcommands.ts — Metrics tokens handler
• @$AIWG_ROOT/src/metrics/token-counter.ts — Token counting, MetaGPT baseline constants (REF-013)
• @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/schemas/flows/token-efficiency.yaml — Token efficiency schema
• @$AIWG_ROOT/docs/cli-reference.md — CLI reference