eval

SkillMonitoring & ops

View and manage the evaluation harness — session metrics, baselines, trend analysis, and regression detection. The feedback loop that tells the system whether improvements actually improved things.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the eval skill

What this skill tells your AI

The instructions your AI receives, as published by kastalien-research/thoughtbox in .agents/skills/eval/SKILL.md and read by ahel’s review.

Evaluation harness: $ARGUMENTS

Commands

Parse the first word of $ARGUMENTS to determine the command:

metrics — Show current session metrics

Collect and display metrics for the current session:

  1. Count commits: git log --oneline --since="today" | wc -l
  2. Count test results: check for recent vitest output or .eval/metrics/ entries
  3. Token usage: check LangSmith state file if available
  4. Pattern usage: check .dgm/fitness.json for patterns used this session
  5. Session duration: check session start time from logs

Display as:

## Current Session Metrics

| Metric | Value | Baseline | Delta |
|--------|-------|----------|-------|
| Commits | 5 | 3.2 avg | +56% |
| Tests passing | 42/42 | 40/42 | +2 |

| Files changed | 12 | 8.5 avg | +41% |
| Patterns used | 7 | 5.3 avg | +32% |

baseline — Set or update baselines

  1. Read the last N session metric snapshots from .eval/metrics/
  2. Calculate averages for each metric
  3. Write to .eval/baselines.json
  4. Report what changed

compare — Compare sessions

Usage: compare --last N or compare --session <id>

  1. Load metric snapshots from .eval/metrics/
  2. Compare against baselines
  3. Highlight regressions (metric dropped >10% below baseline)
  4. Highlight improvements (metric improved >10% above baseline)

report — Generate weekly evaluation report

  1. Load all metrics from the past 7 days
  2. Calculate trends (improving, stable, declining)
  3. Identify top improvements and top regressions
  4. Generate recommendations based on trends

capture — Capture current session metrics

Write a metric snapshot to .eval/metrics/session-{timestamp}.json:

{
  "session_id": "<session id>",
  "timestamp": "<ISO 8601>",
  "branch": "<git branch>",
  "metrics": {
    "commits": 0,
    "tests_total": 0,
    "tests_passing": 0,
    "files_changed": 0,
    "patterns_referenced": 0,
    "assumptions_verified": 0,
    "escalations": 0,
    "spiral_detections": 0
  },
  "qualitative": {
    "session_focus": "<what the session was about>",
    "memory_usefulness": 0,
    "knowledge_gaps_found": []
  }
}

Notes

  • If .eval/baselines.json doesn't exist, skip baseline comparisons and suggest running baseline
  • Metric collection should be best-effort — missing data is noted, not an error
  • Regressions trigger a structured escalation suggestion (not automatic action)

Signals

GitHub stars
64
Forks
20
Last commit
Jul 2026
Advanced
Catalog kind
skill
Gateway key
eval-kastalien-research
Source
github.com/kastalien-research/thoughtbox