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 skills/saitarrun/devforge-ai/observabilitynpx skills add saitarrun/Devforge-ai --skill observabilitygit clone --depth 1 https://github.com/saitarrun/Devforge-aiWhat 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 | $0.00044 | $0.00725 |
| Opus 5 | $0.00022 | $0.00362 |
| Sonnet 5 | $0.00009 | $0.00145 |
| Haiku 4.5 | $0.00004 | $0.00072 |
Grade A, and why
observability 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Observability (Metrics, Logs, Traces)
Three pillars: Metrics (what), Logs (why), Traces (how). Use all three.
Metrics (Time-Series Data)
Key metrics:
- Request latency (p50, p95, p99)
- Error rate (%)
- Throughput (req/s)
- Resource (CPU, memory, disk)
Collection: Prometheus, StatsD, CloudWatch
api_latency_seconds{service="auth", endpoint="/login"} = 0.042
error_rate{service="api"} = 0.001
db_connections{pool="default"} = 42
Logging (Structured)
Format: JSON for parsing
{
"timestamp": "2024-01-15T10:30:45Z",
"level": "ERROR",
"service": "user-service",
"message": "Failed to create user",
"error": "database_error",
"user_id": "user-123",
"duration_ms": 150
}
Sampling: Log 100% errors, 1% normal (reduce volume)
Distributed Tracing
Trace request end-to-end:
Request: POST /api/users
├─ Auth span (5ms)
├─ Validate span (2ms)
├─ DB insert span (45ms)
│ ├─ Connection span (1ms)
│ └─ Query span (44ms)
└─ Cache update span (3ms)
Total: 55ms
Tools: Jaeger, Zipkin, Datadog
Dashboards (SLO-Focused)
What to display:
- SLO % (100% target, 99% actual)
- Error budget consumed (%)
- Latency (p95, p99)
- Errors per minute
- Active requests
- Alert status
Tools: Grafana, CloudWatch, Datadog
Alerting
Good alert (actionable):
Alert: DB connection pool > 80%
Severity: CRITICAL
Action: Increase pool size or check for connection leaks
Runbook: https://wiki.example.com/alerts/db-pool-high
Bad alert (not actionable):
Alert: Disk usage > 50%
(What should I do?)
SLO-Driven Alerting
SLO: 99.9% uptime
Error budget: 8.64 hours/month
Alert: If burn rate > 1% → 30 days to fail budget
Alert: If burn rate > 10% → 3 days to fail budget
Monitoring Checklist
- Latency metrics (p50, p95, p99)
- Error rate (%)
- Saturation (CPU, memory, disk)
- Dependencies (external API uptime)
- Custom business metrics
- SLO dashboard
- Alert rules (with runbooks)
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 · 113 lines · 44 tokens per session scan A 5958c25002dd
observability is a skill published in the GitHub repository saitarrun/Devforge-ai (5 stars, last pushed 19d ago), licensed Apache-2.0. It adds 44 tokens to every session and 725 once invoked, about $0.0002 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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