meter-accuracy

Your objective is to display CocoMeter's estimation accuracy learning data.

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

Read Accuracy Data

Read .cocoplus/meter/accuracy-history.jsonl — each line is a session record:

{ "session_id": "...", "estimated_tokens": N, "actual_tokens": N, "estimated_credits": N, "actual_credits": N, "ratio": N, "timestamp": "..." }

If the file does not exist or has fewer than 2 entries: output "Not enough sessions to compute calibration factor. Run at least 2 complete sessions with pre-flight estimates to begin accuracy learning." Then stop.

Read .cocoplus/meter/adjustment-factor.json:

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

Compute Trend

From the last 5 session ratios in accuracy-history.jsonl:

• If max − min < 0.1: trend = "Stable"
• If last ratio > first ratio by >0.1: trend = "Increasing"
• If last ratio < first ratio by >0.1: trend = "Decreasing"

Output

CocoMeter Accuracy Learning
Adjustment Factor: [factor]x  (from [sample_size] sessions)
Trend: [Stable/Increasing/Decreasing] ([±delta] over last 5 sessions)
Recent sessions: [last 5 ratios, comma-separated]
Advice: [one sentence — e.g. "Your pipelines consistently use ~[factor]x the baseline estimate."]
Pre-flight estimates are being automatically calibrated.

Anti-Rationalization

Shortcut / Temptation	Why It Fails
Show calibrated estimates without surfacing the factor	Developer cannot assess whether the calibration is trustworthy without seeing it
Use mean instead of median for adjustment factor	Mean is skewed by outlier sessions; median is more robust

Exit Criteria

• Adjustment factor and sample size are shown
• Trend is computed from last 5 sessions
• Recent session ratios are listed
• Output is shown only when at least 2 sessions of data exist