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calibration-report

Summarize estimate accuracy from the assistant time log.

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indigoai-us/hq-core90 stars(GitHub 90 + cccskills 0)

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Files included (2)

  • SKILL.md3.9 KB
  • agents/openai.yaml123 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

/calibration-report - Estimate Accuracy by Category

Read workspace/estimate-log/log.jsonl and summarize how well the assistant's estimates match actuals. Output a table per category showing median ratio, sample size, and a suggested inflation multiplier.

Input: $ARGUMENTS

Optional flags:

  • --category <name> — restrict to one category (release, pr, build, ...)
  • --since YYYY-MM-DD — only include entries with timestamp >= date
  • --abandon-stale — mark pending entries older than 30 days as abandoned
  • --show-misses — print the top 5 worst misses with surrounding text

Steps

  1. Read the log

    LOG=workspace/estimate-log/log.jsonl
    [ -s "$LOG" ] || { echo "No estimates logged yet."; exit 0; }
    
  2. Apply filters

    FILTERED=$(jq -c --arg cat "$CATEGORY" --arg since "$SINCE" '
      select(
        (($cat == "" or .category == $cat))
        and (($since == "" or .timestamp >= $since))
      )
    ' "$LOG")
    
  3. Compute stats per category (only kind=="estimate" AND status=="completed")

    For each category:

    • n — count
    • median_ratio — median of actual / expected
    • p25 / p75 — quartiles
    • over_count — entries where ratio > 1.2
    • under_count — entries where ratio < 0.8
    • on_count — entries within ±20%
    • suggested_multiplier — round(median_ratio * 4) / 4 (rounded to 0.25)

    Use jq + simple awk math. For median, sort then pick middle.

  4. Render report

    ESTIMATE CALIBRATION REPORT
    ===========================
    Window: <since> to <now>  |  Total entries: <n>  |  Completed: <c>  |  Pending: <p>
    
    Category    n   median_ratio   p25-p75      verdict-mix       suggest-multiply-by
    --------    --  ------------   ----------   ---------------   --------------------
    release     5   0.40           0.30-0.50    1on/0over/4under  0.5x  (you over-estimate by 2x)
    build       3   2.40           1.80-3.00    0on/3over/0under  2.5x  (you under-estimate by 2.5x)
    infra       4   1.10           0.90-1.30    3on/1over/0under  1.0x  (well calibrated)
    pr          2   0.55           0.50-0.60    0on/0over/2under  0.5x  (you over-estimate by 2x)
    ...
    
  5. Show pending count + stale flagging

    Pending: 3 entries
    Stale (>30d, no /finish-estimate): 1
      est_abc1234   "..."   estimated 14 days ago
    Run /calibration-report --abandon-stale to clear them.
    
  6. (Optional) --show-misses — print top 5 worst misses (completed entries with highest |log(ratio)|):

    Worst misses:
    1. est_xyz   release    expected 60 min, actual 5 min   12.0x over
       "going to take ~1 hr for the npm release of hq-cloud@5.7.1"
    2. ...
    
  7. Recommendation block Highlight categories with n >= 3 and |log(median_ratio)| > log(1.5) — those are systematically wrong. Recommend the agent inflate future estimates in those categories by suggested_multiplier.

Implementation note

Pure jq + awk + bash. No Python, no installs. Keep it < 100 lines.

For median in awk:

{ a[NR] = $(1) } END {
  asort(a)
  if (NR % 2) print a[(NR+1)/2]
  else print (a[NR/2] + a[NR/2+1]) / 2
}

(GNU awk has asort; BSD awk on macOS does too as of recent versions. Fall back to sort -n | awk if needed.)

Notes

  • This report is read-only by default. Only --abandon-stale mutates the log.
  • Suggested multipliers should be applied loosely — if the median says 2.5x but n=3, treat it as a rough heuristic, not a precise correction.
  • The agent should consult this report (or read log.jsonl directly) when generating a new estimate in a high-bias category, and explicitly inflate.

See also

  • /track-estimate — start tracking an estimate
  • /finish-estimate — close one out

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