agentic-kpi-tracking
Track and measure agentic coding KPIs for ZTE progression. Use when measuring workflow effectiveness, tracking Size/Attempts/Streak/Presence metrics, or assessing readiness for autonomous operation.
Best use case
agentic-kpi-tracking is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Track and measure agentic coding KPIs for ZTE progression. Use when measuring workflow effectiveness, tracking Size/Attempts/Streak/Presence metrics, or assessing readiness for autonomous operation.
Teams using agentic-kpi-tracking should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agentic-kpi-tracking/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agentic-kpi-tracking Compares
| Feature / Agent | agentic-kpi-tracking | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Track and measure agentic coding KPIs for ZTE progression. Use when measuring workflow effectiveness, tracking Size/Attempts/Streak/Presence metrics, or assessing readiness for autonomous operation.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Agentic KPI Tracking Skill
Guide measurement and tracking of agentic coding KPIs to assess ZTE readiness.
## When to Use
- Measuring agentic workflow effectiveness
- Tracking progress toward ZTE
- Analyzing success patterns
- Identifying improvement areas
## Core KPIs
### Summary Metrics
| Metric | Calculation | Target |
| --- | --- | --- |
| **Current Streak** | Consecutive successes (Attempts <= 2) | Higher is better |
| **Longest Streak** | Best consecutive success run | Track improvement |
| **Average Presence** | Mean attempts across all runs | Target: 1 |
| **Total Plan Size** | Sum of all plan sizes | Track scaling |
| **Total Diff Size** | Sum of all changes (added + removed) | Track throughput |
### Per-Run Metrics
| Metric | Source | Meaning |
| --- | --- | --- |
| **Attempts** | Count of plan/patch runs | 1 = perfect, higher = retries |
| **Plan Size** | Lines in plan file | Task complexity |
| **Diff Size** | Lines added + removed | Change magnitude |
| **Files Changed** | Number of files modified | Change scope |
## Calculation Methods
### Attempts Count
Only count workflow restarts:
```python
attempts_incrementing = ["adw_plan_iso", "adw_patch_iso"]
attempts = count(workflow in all_adws if workflow in attempts_incrementing)
```
Build/test/review don't increment - only full replans.
### Streak Calculation
```python
current_streak = 0
for run in reversed(runs):
if run.attempts <= 2:
current_streak += 1
else:
break
```
### Diff Statistics
```bash
git diff origin/main --shortstat
# Output: X files changed, Y insertions(+), Z deletions(-)
```
## KPI File Format
Store in `app_docs/agentic_kpis.md` or equivalent:
```markdown
# Agentic KPIs
## Summary
| Metric | Value |
| --- | --- |
| Current Streak | 5 |
| Longest Streak | 12 |
| Average Presence | 1.3 |
| Total Plan Size | 450 lines |
| Total Diff Size | 2,340 lines |
## Detail
| Date | ADW ID | Issue | Class | Attempts | Plan Size | Diff +/- | Files |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 2024-01-15 | abc123 | #45 | /bug | 1 | 35 | +45/-12 | 3 |
| 2024-01-14 | def456 | #44 | /feature | 2 | 85 | +120/-30 | 8 |
```
## Tracking Workflow
### Step 1: Gather Current Run Data
From state or git:
- ADW ID
- Issue number
- Issue classification
- Plan file path
- All workflows run (for attempts)
### Step 2: Calculate Metrics
```python
attempts = count_attempts(all_adws)
plan_size = wc_lines(plan_file)
diff_stats = parse_git_diff()
```
### Step 3: Update Detail Table
Add new row with current run data.
### Step 4: Recalculate Summary
Update all summary metrics based on full detail table.
### Step 5: Analyze Trends
- Is streak increasing?
- Is average presence decreasing?
- Are plan sizes growing (handling bigger tasks)?
## ZTE Readiness Indicators
Based on KPIs, assess ZTE readiness:
| Indicator | Threshold | Status |
| --- | --- | --- |
| Current Streak | >= 5 | Ready to try ZTE |
| Average Presence | <= 1.5 | Good efficiency |
| Recent Failures | 0 in last 10 | High confidence |
| Plan Size Trend | Increasing | Scaling up |
## Key Memory References
- @agentic-kpis.md - KPI definitions from Lesson 002
- @zte-progression.md - How KPIs relate to ZTE levels
- @zte-confidence-building.md - Using KPIs for confidence
## Output Format
Provide KPI update:
```markdown
## KPI Update
**Run:** {adw_id}
**Issue:** #{issue_number} ({issue_class})
### This Run
- Attempts: 1
- Plan Size: 45 lines
- Diff: +67/-23 (4 files)
### Updated Summary
- Current Streak: 6 (was 5)
- Longest Streak: 12 (unchanged)
- Average Presence: 1.28 (improved)
### Analysis
[Trend observations and recommendations]
```
## Anti-Patterns
- Gaming metrics (easy tasks only)
- Ignoring failures (not counting retries)
- Not tracking consistently
- Celebrating streaks over actual delivery
## Version History
- **v1.0.0** (2025-12-26): Initial release
---
## Last Updated
**Date:** 2025-12-26
**Model:** claude-opus-4-5-20251101Related Skills
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