Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/vishnu2kmohan/mcp-server-langgraph/analyticsgit clone --depth 1 https://github.com/vishnu2kmohan/mcp-server-langgraphWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00016 | $0.03233 |
| Opus 5 | $0.00008 | $0.01617 |
| Sonnet 5 | $0.00003 | $0.00647 |
| Haiku 4.5 | $0.00002 | $0.00323 |
Grade A, and why
analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow Analytics Dashboard
You are tasked with generating a comprehensive analytics dashboard for the Claude Code workflow optimization. This command shows usage statistics, time savings, ROI, and recommendations.
Analytics Context
Data Sources:
- Command usage log (
.claude/analytics/command-usage.jsonl) - Git commit history
- Test execution history
- Coverage reports
- Benchmark results
Tracked Metrics:
- Command usage frequency
- Time savings per command
- Overall ROI
- Sprint velocity
- Test pass rate
- Coverage trends
Your Task
Step 1: Generate Usage Report
Run the usage tracking script:
python scripts/workflow/track-command-usage.py --report --days 30
Step 2: Calculate ROI
Run the ROI calculation:
python scripts/workflow/track-command-usage.py --roi
Step 3: Gather Additional Metrics
Git Metrics (last 30 days):
# Commit count
git log --since="30 days ago" --oneline | wc -l
# Contributors
git log --since="30 days ago" --format="%an" | sort -u | wc -l
# Files changed
git log --since="30 days ago" --name-only | sort -u | wc -l
# Lines changed
git log --since="30 days ago" --numstat | awk '{add+=$1; del+=$2} END {print add, del}'
Test Metrics:
# Run tests and capture results
pytest tests/ -v --tb=no 2>&1 | tee /tmp/test-results.txt
# Parse pass rate
grep "passed" /tmp/test-results.txt
Coverage Metrics:
# Get coverage percentage
if [ -f "htmlcov/index.html" ]; then
grep -oP 'pc_cov">\K[0-9]+' htmlcov/index.html | head -1
fi
Step 4: Generate Comprehensive Dashboard
Dashboard Format:
╔══════════════════════════════════════════════════════════════════╗
║ WORKFLOW ANALYTICS DASHBOARD ║
║ Last 30 Days | Auto-Generated ║
╠══════════════════════════════════════════════════════════════════╣
║ COMMAND USAGE STATISTICS ║
╠══════════════════════════════════════════════════════════════════╣
║ Total Commands Used: 287 ║
║ Unique Commands: 15 ║
║ Most Used: /test-summary (45 uses) ║
║ Highest Value: /create-adr (40 min/use) ║
║ Total Time Saved: 42.5 hours ║
╠══════════════════════════════════════════════════════════════════╣
║ TOP 10 COMMANDS BY VALUE ║
╠══════════════════════════════════════════════════════════════════╣
║ 1. /test-summary 45 uses │ 3.8h saved │ 5 min/use ║
║ 2. /quick-debug 38 uses │ 7.6h saved │ 12 min/use ║
║ 3. /create-adr 12 uses │ 8.0h saved │ 40 min/use ⭐ ║
║ 4. /improve-coverage 8 uses │ 6.0h saved │ 45 min/use ⭐ ║
║ 5. /coverage-gaps 18 uses │ 6.0h saved │ 20 min/use ⭐ ║
║ 6. /ci-status 42 uses │ 3.2h saved │ 4.5 min/use ║
║ 7. /pr-checks 15 uses │ 2.5h saved │ 10 min/use ║
║ 8. /start-sprint 2 uses │ 0.7h saved │ 20 min/use ║
║ 9. /progress-update 6 uses │ 2.0h saved │ 20 min/use ║
║ 10. /deploy 5 uses │ 2.1h saved │ 25 min/use ⭐ ║
╠══════════════════════════════════════════════════════════════════╣
║ RETURN ON INVESTMENT (ROI) ║
╠══════════════════════════════════════════════════════════════════╣
║ Investment: 35 hours (one-time) ║
║ Return (30 days): 42.5 hours ║
║ ROI: 1.2x (21% return) ✅ ║
║ Break-Even: ACHIEVED ✅ ║
║ ║
║ Projected Annual: 510 hours saved ║
║ Work Weeks Saved: 12.8 weeks ║
║ Annualized ROI: 14.6x ║
╠══════════════════════════════════════════════════════════════════╣
║ DEVELOPMENT VELOCITY ║
╠══════════════════════════════════════════════════════════════════╣
║ Commits (30d): 47 ║
║ Contributors: 2 ║
║ Files Changed: 156 ║
║ Lines Added: +2,847 ║
║ Lines Removed: -1,234 ║
║ Net Change: +1,613 ║
║ Avg Commits/Day: 1.6 ║
╠══════════════════════════════════════════════════════════════════╣
║ QUALITY METRICS ║
╠══════════════════════════════════════════════════════════════════╣
║ Test Pass Rate: 99.3% (722/727) ✅ ║
║ Test Coverage: 65% → 67% (+2%) 📈 ║
║ Mypy Strict Modules: 3/11 → 5/11 (+2) 📈 ║
║ Open TODOs: 34 → 28 (-6) 📉 ║
║ Documentation: 39 ADRs (complete) ✅ ║
╠══════════════════════════════════════════════════════════════════╣
║ EFFICIENCY GAINS (vs baseline) ║
╠══════════════════════════════════════════════════════════════════╣
║ Sprint Setup: 67% faster (30min → 10min) ║
║ Context Loading: 90% faster (10min → 1min) ║
║ Test Analysis: 75% faster (20min → 5min) ║
║ ADR Creation: 67% faster (60min → 20min) ⭐ ║
║ Coverage Analysis: 75% faster (60min → 15min) ⭐ ║
║ Debugging: 60% faster (20min → 8min) ║
║ Deployment: 38% faster (40min → 25min) ⭐ ║
║ Overall: 55-65% more efficient ✅ ║
╠══════════════════════════════════════════════════════════════════╣
║ RECOMMENDATIONS ║
╠══════════════════════════════════════════════════════════════════╣
║ 🔥 High Impact: ║
║ • Use /create-adr more (only 12 uses, saves 40 min each) ║
║ • Use /improve-coverage weekly (8 uses, saves 45 min each) ║
║ • Use /deploy for all deployments (5 uses, saves 25 min) ║
║ ║
║ ⚠️ Watch: ║
║ • Test coverage at 67% (target: 80%) ║
║ • Mypy strict rollout at 45% (5/11 modules) ║
║ ║
║ ✅ Celebrate: ║
║ • 99.3% test pass rate (excellent!) ║
║ • 287 command uses (workflow is being used!) ║
║ • ROI achieved in < 1 month ║
╚══════════════════════════════════════════════════════════════════╝
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 343 lines · 16 tokens per session scan A 82c8e87495ad
analytics is a command published in the GitHub repository vishnu2kmohan/mcp-server-langgraph (4 stars, last pushed 9d ago), licensed MIT. It adds 16 tokens to every session and 3,233 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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