Meter Estimate

Your objective is to estimate token and cost impact before executing an action.

Before proceeding, verify that .cocoplus/ exists. If not: output "CocoPlus not initialized in this directory. Run $pod init to begin." Then stop.

If no argument provided: read .cocoplus/flow.json to estimate the full pipeline. If argument provided (e.g., $meter estimate "run 3-stage pipeline"): estimate the described action.

Estimation Approach

For pipeline estimation:

• Count stages in flow.json
• For each stage, estimate based on persona and typical workload:
  • data-engineer: ~8,000 tokens/stage (SQL-heavy)
  • analytics-engineer: ~6,000 tokens/stage (semantic modeling)
  • data-scientist: ~12,000 tokens/stage (notebook + analysis)
  • data-analyst: ~4,000 tokens/stage (queries + reporting)
  • bi-analyst: ~3,000 tokens/stage (visualization spec)
  • data-product-manager: ~2,000 tokens/stage (documentation)
  • data-steward: ~3,000 tokens/stage (governance)
  • chief-data-officer: ~2,000 tokens/stage (review)

Apply Accuracy Learning Calibration

Read .cocoplus/meter/adjustment-factor.json if it exists:

{ "factor": N, "sample_size": N, "computed_at": "..." }

If the file exists and sample_size >= 2:

• calibrated_total = raw_total × factor
• Calibration label: (baseline: [raw_total], calibration factor: [factor]x from [sample_size] prior sessions) Else:
• calibrated_total = raw_total
• No calibration label

Write estimate to .cocoplus/meter/preflight-log.jsonl:

{ "session_id": "[current-session-id]", "estimated_tokens": [calibrated_total], "estimated_credits": [estimated_credits], "timestamp": "[ISO 8601]" }

Output:

# Pre-flight Estimate

Action: [described action or "Full pipeline execution"]