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.
git clone --depth 1 https://github.com/nexus-labs-automation/agent-observabilityWrote 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/commands/nexus-labs-automation/agent-observability/instrument)<a href="https://agentmods.dev/commands/nexus-labs-automation/agent-observability/instrument"><img src="https://agentmods.dev/badge/commands/nexus-labs-automation/agent-observability/instrument/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/nexus-labs-automation/agent-observability/instrument"><img src="https://agentmods.dev/badge/commands/nexus-labs-automation/agent-observability/instrument.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00008 | $0.01164 |
| Opus 5 | $0.00004 | $0.00582 |
| Sonnet 5 | $0.00002 | $0.00233 |
| Haiku 4.5 | $0.00001 | $0.00116 |
Grade A, and why
instrument 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 9d 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/instrument Command
Generate a comprehensive instrumentation plan for AI agent systems.
Arguments
framework: langchain|langgraph|claude-agent-sdk|openai-agents|crewai|autogen|semantic-kernel|haystack|auto--vendor: Optional observability vendor (langfuse|langsmith|arize|weave|helicone|braintrust|datadog|opentelemetry)--path: Optional path to analyze (defaults to current directory)
Workflow
Phase 1: Discovery
If framework is auto or not specified:
- Search for framework indicators in codebase
- Identify primary agent framework
- Create initial todo list for tracking progress
Phase 2: Codebase Analysis
Launch codebase-analyzer agent:
Analyze this agent codebase to understand:
1. Agent framework and architecture
2. Existing telemetry/observability
3. Key files and entry points
4. Instrumentation gaps
Phase 3: Clarifying Questions
Ask user about:
- Critical Agent Journeys - Which agent workflows are most important?
- Cost Sensitivity - How important is token/cost tracking?
- Multi-Agent Complexity - Single agent or multi-agent orchestration?
- Vendor Preference - Preferred observability platform?
- Evaluation Needs - Do you need quality/eval metrics?
Phase 4: Plan Design
Load and apply:
skills/instrumentation-planning/SKILL.mdreferences/methodology/agent-observability-tiers.mdreferences/frameworks/{detected-framework}.md(if detected)references/vendors/{specified-vendor}.md(if specified)
Generate tiered implementation plan:
| Tier | Focus | Instrumentation |
|---|---|---|
| T0: Foundation | Day 1 essentials | SDK init, basic spans, error capture |
| T1: Core Tracing | LLM & tools | LLM call spans, tool execution spans |
| T2: Context | Attribution | Token tracking, cost calculation, user context |
| T3: Multi-Agent | Coordination | Parent-child spans, handoffs, delegation |
| T4: Evaluation | Quality | Evals, feedback loops, quality metrics |
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.
- 9d ago First seen · 175 lines · 8 tokens per session scan A b5700645ca88
instrument is a command published in the GitHub repository nexus-labs-automation/agent-observability (7 stars, last pushed 8mo ago), licensed MIT. It adds 8 tokens to every session and 1,164 once invoked, about $0.0000 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.