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/rjmurillo/ai-agents/observabilitynpx skills add rjmurillo/ai-agents --skill observabilitygit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rjmurillo/ai-agents/observability)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/observability"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/observability.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00042 | $0.01531 |
| Opus 5 | $0.00021 | $0.00766 |
| Sonnet 5 | $0.00008 | $0.00306 |
| Haiku 4.5 | $0.00004 | $0.00153 |
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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Observability Skill
Query structured JSONL event logs to understand agent behavior, debug failures, and analyze performance.
Triggers
| Trigger Phrase | Operation |
|---|---|
query agent logs |
Run query_logs.py with filters |
find slow tool calls |
Run with --slow threshold |
show agent errors |
Run with --errors-only |
summarize session performance |
Run with --output summary-sessions |
analyze tool usage |
Run with --output summary-tools |
When to Use
Use this skill when:
- Debugging why an agent chose a particular tool or approach
- Finding slow tool calls that degrade agent performance
- Analyzing error patterns across agent sessions
- Comparing tool usage across sessions or agents
- Tracing decisions from orchestrator through sub-agents
Use direct log file inspection instead when:
- Checking a single known event in a small log
- The log file has fewer than 10 events
Event Schema
Logs use JSONL format (one JSON object per line). See schema.json for the full JSON Schema.
Event Types
| Type | Purpose | Key Fields |
|---|---|---|
| session_start | Agent invocation begins | agent, session_id |
| session_end | Agent invocation completes | agent, session_id |
| tool_call | Tool invocation with timing | tool.name, tool.duration_ms, tool.success |
| decision | Reasoning captured alongside action | decision.action, decision.reasoning |
| metric | Numeric measurement | metric.name, metric.value, metric.unit |
| error | Error occurrence | error.message, error.category, error.recoverable |
Example Events
{"timestamp":"2026-03-30T10:00:00Z","event_type":"session_start","session_id":"sess-001","agent":"implementer","message":"Session started"}
{"timestamp":"2026-03-30T10:00:01Z","event_type":"tool_call","session_id":"sess-001","agent":"implementer","level":"INFO","tool":{"name":"Read","duration_ms":45,"success":true,"input_summary":"src/main.py"},"message":"Read source file"}
{"timestamp":"2026-03-30T10:00:02Z","event_type":"decision","session_id":"sess-001","agent":"implementer","level":"INFO","decision":{"action":"Edit existing function","reasoning":"Function exists, modifying is safer than rewriting","alternatives_considered":["Rewrite from scratch","Create wrapper"]}}
{"timestamp":"2026-03-30T10:00:10Z","event_type":"error","session_id":"sess-001","agent":"implementer","level":"ERROR","error":{"message":"Test failed: assertion error in test_parse","category":"test_failure","recoverable":true}}
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/distributed-systems-fallacies.md 3.7 KB
- references/otel-migration-reference.md 3.5 KB
- references/otel-semantic-conventions.md 3.4 KB
- references/prometheus-recording-rules.md 3.4 KB
- references/three-pillars-reference.md 2.9 KB
- schema.json 3.1 KB
- scripts/query_logs.py 8.7 KB runs code
- tests/test_query_logs.py 10 KB runs code
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 · 155 lines · 42 tokens per session scan A d07aa90e79ff
observability is a skill published in the GitHub repository rjmurillo/ai-agents (45 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 1,531 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 2026-09-03.
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