Metadata

name: eval
description: Evaluate everything the PA agent manages — tasks, skills, PA network health, billing, calendar connections, and memory quality. Use when: owner asks for an evaluation, wants to know what's working and what isn't, or requests a performance report. Combines supervisor status with quality scoring.

Load Local Context

CONTEXT_FILE="/opt/ocana/openclaw/workspace/skills/eval/.context"
[ -f "$CONTEXT_FILE" ] && source "$CONTEXT_FILE"
# Then use: $OWNER_PHONE, $WORKSPACE, $TASKS_FILE, $MONDAY_TOKEN_FILE, $GOG_CREDS, etc.

Eval Skill

Structured evaluation of everything the agent manages.

When to Use

Trigger phrases:

"run eval"
"what's working and what isn't"
"rate yourself"
"check everything"

Pre-Eval Behavioral Checks (Always)

React 👍 when owner triggers eval
React ✅ when report is complete
PA directory source: /opt/ocana/openclaw/workspace/PA_LIST.md
Calendar check: use direct API (NOT gog CLI)

Eval Report Format

📋 Full Eval — [DATE]

━━━ SELF PERFORMANCE ━━━
Execution:      [1-5] [comment]
Accuracy:       [1-5] [comment]
Memory:         [1-5] [comment]
Proactivity:    [1-5] [comment]
Communication:  [1-5] [comment]
TOTAL: [X]/25

━━━ ACTIVE TASKS ━━━
✅ Done today:   [count]
🟡 In progress:  [count]
❌ Stalled:      [count] — [list stalled tasks]

━━━ PA NETWORK ━━━
✅ Working:  [list]
⚠️ Issues:   [list with issue]
❌ Down:     [list]

━━━ SKILLS ━━━
Installed: [count]
Used today: [list]
Unused (7+ days): [list]

━━━ INTEGRATIONS ━━━
Calendar (owner):     [connected ✅ / broken ❌ / unknown ?]
monday.com:           [connected ✅ / broken ❌]
Email (gog):          [connected ✅ / broken ❌]
GitHub backup:        [last push: X ago]
WhatsApp:             [connected ✅ / disconnected ❌]

━━━ MEMORY HEALTH ━━━
Daily notes:     [today's file exists? ✅/❌]
Long-term:       [MEMORY.md size — OK / bloated]
Learnings:       [count this week]
Last backup:     [X ago]

━━━ RECOMMENDATIONS ━━━
1. [Most important thing to fix]
2. [Second priority]
3. [Optional improvement]

Running the Eval

Step 1 — Self Performance Score

Score each dimension 1–5 based on today's activity:

Execution (1–5):
- 5: All tasks completed without reminders
- 3: Most tasks done, some follow-up needed
- 1: Multiple tasks missed or forgotten

Accuracy (1–5):
- 5: No corrections from owner
- 3: 1–2 corrections
- 1: Multiple errors or wrong outputs

Memory (1–5):
- 5: Recalled context correctly every time
- 3: Missed some context, caught on
- 1: Repeated the same mistakes

Proactivity (1–5):
- 5: Acted before being asked multiple times
- 3: Responded to requests, minimal initiative
- 1: Only reacted, no proactive actions

Communication (1–5):
- 5: Clear, concise, no unnecessary narration
- 3: Occasionally verbose or unclear
- 1: Shared reasoning, listed options, narrated steps

Step 2 — Task Audit

TASKS_FILE="$HOME/.openclaw/workspace/memory/tasks.md"

echo "Tasks done:"
grep -c "\[x\]" "$TASKS_FILE" 2>/dev/null || echo 0

echo "Tasks in progress:"
grep -c "\[ \]" "$TASKS_FILE" 2>/dev/null || echo 0

# Stalled = in progress for 2+ days
echo "Stalled tasks (2+ days old):"
grep "\[ \]" "$TASKS_FILE" | grep -v "$(date +%Y-%m-%d)" | grep -v "$(date -u -d '1 day ago' +%Y-%m-%d 2>/dev/null)" || echo "none"

Step 3 — PA Network Health

BILLING_FILE="$HOME/.openclaw/workspace/memory/billing-status.json"

echo "PA Network Status:"
python3 << 'PYEOF'
import json
data = json.load(open('/opt/ocana/openclaw/workspace/memory/billing-status.json'))
for pa in data['issues']:
    status = "✅" if pa['status'] == 'resolved' else "⚠️"
    print(f"  {status} {pa['pa']} ({pa['owner']}): {pa['status']}")
PYEOF

Step 4 — Skills Audit

SKILLS_DIR="$HOME/.openclaw/workspace/skills"

echo "Installed skills:"
ls "$SKILLS_DIR" | grep -v README | wc -l

echo "Skills list:"
ls "$SKILLS_DIR" | grep -v README

Step 5 — Integration Health

# Test Anthropic billing
API_STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
  -H "x-api-key: ${ANTHROPIC_API_KEY:-none}" \
  -H "anthropic-version: 2023-06-01" \
  https://api.anthropic.com/v1/models 2>/dev/null)

# Interpret result
if [ "$API_STATUS" = "200" ]; then echo "Billing: ✅ OK"
elif [ "$API_STATUS" = "402" ]; then echo "Billing: ❌ OUT OF CREDITS"
elif [ "$API_STATUS" = "401" ]; then echo "Billing: ❌ Invalid key"
else echo "Billing: ? HTTP $API_STATUS"
fi

# Test GitHub backup
LAST_PUSH=$(git -C "$HOME/.openclaw/workspace" log -1 --format="%ar" 2>/dev/null)
echo "Last backup: $LAST_PUSH"

# Test monday.com
if [ -f "$HOME/.credentials/monday-api-token.txt" ]; then
  MONDAY_STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
    -X POST https://api.monday.com/v2 \
    -H "Authorization: $(cat $HOME/.credentials/monday-api-token.txt)" \
    -H "Content-Type: application/json" \
    -d '{"query": "{ me { id } }"}' 2>/dev/null)
  [ "$MONDAY_STATUS" = "200" ] && echo "monday.com: ✅" || echo "monday.com: ❌ ($MONDAY_STATUS)"
else
  echo "monday.com: ? (no token found)"
fi

Step 6 — Memory Health

TODAY=$(date -u +%Y-%m-%d)
WORKSPACE="$HOME/.openclaw/workspace"

# Check daily notes exist
[ -f "$WORKSPACE/memory/$TODAY.md" ] \
  && echo "Daily notes: ✅" \
  || echo "Daily notes: ❌ not created yet"

# Check MEMORY.md size (warn if >200 lines)
MEMORY_LINES=$(wc -l < "$WORKSPACE/MEMORY.md" 2>/dev/null || echo 0)
if [ "$MEMORY_LINES" -gt 200 ]; then
  echo "MEMORY.md: ⚠️ Large ($MEMORY_LINES lines) — consider pruning"
else
  echo "MEMORY.md: ✅ ($MEMORY_LINES lines)"
fi

# Count learnings this week
LEARNINGS=$(grep -c "^##" "$WORKSPACE/.learnings/LEARNINGS.md" 2>/dev/null || echo 0)
echo "Total learnings logged: $LEARNINGS"

Recommendations Logic

After running all steps, generate recommendations:

If any PA has billing_error AND status != resolved:
  → "Fix billing for [PA list] — they can't function"

If any task has status in_progress for 2+ days:
  → "Follow up on stalled task: [task name]"

If MEMORY.md > 200 lines:
  → "Prune MEMORY.md — it's getting bloated"

If daily notes don't exist:
  → "Create today's memory file"

If last backup > 6 hours ago:
  → "Run git backup"

If API billing = 402:
  → "My own API key is out of credits — alert the admin immediately"

Scheduling

Run eval:

On demand — when owner asks
Weekly — every Sunday at 09:00
After major incidents — billing crisis, WA disconnect, etc.

Cost Tips

Cheap: Reading files, scoring, formatting — any small model
Expensive: Summarizing large memory files — skip if not asked
Avoid: Running all API health checks every hour — cache for 30 min
Batch: Run all health checks in one pass, not one at a time

Minimum Model

Any model that can:

Read files
Apply if/then scoring rules
Format a structured report

No advanced reasoning needed.

PA Performance Scoring (Merged from pa-eval skill)

Use this section when evaluating individual PA agents (weekly self-eval or on-demand when owner gives feedback).

Scoring Dimensions (1–5 each, max 40 points)

Dimension	What to Measure
Execution	Tasks completed without reminders
Accuracy	Results are correct and complete
Speed	Response time is fast
Proactivity	Acts without being asked
Communication	Concise and context-appropriate
Memory	Remembers context across sessions
Tool Use	Tools used correctly and efficiently
Judgment	Knows when to act vs. when to ask

Grade: A (36–40), B (28–35), C (20–27), D (<20)

Owner Feedback Signals

Log these automatically when detected:

Signal	Action
👍 reaction / "thanks" / "great"	Log +1 positive
👎 reaction / "wrong" / "not good"	Log -1, record the correction
Owner re-asks the same question	Log -1 memory gap
Owner does the task themselves	Log -1 initiative gap
Owner surprised by proactive action	Log +2 proactivity

Rule: Log feedback signals immediately — don't batch them.

Weekly Eval File

Save to .learnings/eval/YYYY-MM-DD.md with: scores table, owner feedback, tasks completed/failed, what went well, what to improve, actions for next week.

Benchmark Tests (Run Monthly)

Task Completion Rate: completed / assigned × 100% — Target: >90%
Accuracy Rate: (tasks - corrections) / tasks × 100% — Target: >95%
Memory Retention: Ask about something discussed 7+ days ago — Target: >80% recall

Eval

安装技能