Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/nexus-labs-automation/agent-observabilitynpx agentmods add commands/nexus-labs-automation/agent-observability/auditWrote 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/audit)<a href="https://agentmods.dev/commands/nexus-labs-automation/agent-observability/audit"><img src="https://agentmods.dev/badge/commands/nexus-labs-automation/agent-observability/audit.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 | $0.00007 | $0.01260 |
| Opus 5 | $0.00003 | $0.00630 |
| Sonnet 5 | $0.00001 | $0.00252 |
| Haiku 4.5 | $0.00001 | $0.00126 |
Grade A, and why
audit 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 8d 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/audit Command
Scan existing agent codebase for telemetry coverage and anti-patterns.
Arguments
path: Optional path to audit (defaults to current directory)
Workflow
Step 1: Launch Codebase Analyzer
Launch codebase-analyzer agent to:
- Detect agent framework(s)
- Find existing observability SDKs
- Map agent architecture
- Identify entry points and key files
Step 2: Load Audit References
Based on detection, load:
references/methodology/agent-observability-tiers.mdreferences/frameworks/{framework}.mdreferences/vendors/{vendor}.md(if vendor detected)references/anti-patterns/
Step 3: Evaluate Coverage
Check each instrumentation area:
| Area | Priority | Status | Check |
|---|---|---|---|
| SDK Initialization | P0 | Vendor SDK configured? | |
| LLM Call Tracing | P0 | Model calls have spans? | |
| Tool Call Tracing | P0 | Tool executions tracked? | |
| Error Capture | P0 | Errors logged with context? | |
| Token Tracking | P1 | Input/output tokens recorded? | |
| Cost Attribution | P1 | Cost calculated per call? | |
| Agent Identity | P1 | Agent name/type in spans? | |
| Multi-Agent Links | P1 | Parent-child relationships? | |
| Memory/RAG Spans | P2 | Retrieval tracked? | |
| Human-in-Loop | P2 | Approval workflows traced? | |
| Evaluations | P2 | Quality metrics captured? | |
| Session Context | P2 | User/session attached? |
Step 4: Anti-Pattern Scan
Launch instrumentation-reviewer agent to check for:
Critical:
- Full prompt/response logging
- Secrets in traces
- Missing parent spans
- Blocking telemetry calls
Important:
- No token tracking
- Missing error context
- High cardinality attributes
- Inconsistent naming
Step 5: Generate Audit Report
Output comprehensive report with:
- Coverage scorecard
- Gaps ranked by priority
- Anti-patterns with file:line refs
- Recommended fixes
- Quick wins vs. larger efforts
Output Format
## Agent Observability Audit: [Project Name]
### Executive Summary
- **Framework:** [LangGraph]
- **Vendor:** [Langfuse v2.1]
- **Coverage Score:** [65%] (based on tier completion)
- **Critical Issues:** [2]
- **Recommended Actions:** [5]
### Existing Telemetry
| SDK/Vendor | Version | Location | Status |
|------------|---------|----------|--------|
| Langfuse | 2.1.0 | requirements.txt | Active |
| OpenTelemetry | 1.20 | pyproject.toml | Partial |
### Coverage Assessment
#### Tier 0: Foundation
| Check | Status | Notes |
|-------|--------|-------|
| SDK Init | PASS | langfuse.init() in main.py:12 |
| Root Span | FAIL | No agent-level span |
| Error Capture | PASS | Exception handler present |
#### Tier 1: Core Tracing
| Check | Status | Notes |
|-------|--------|-------|
| LLM Spans | PARTIAL | Only OpenAI, missing Anthropic |
| Tool Spans | FAIL | Tools not instrumented |
| Agent Loop | PASS | @observe decorator on run() |
#### Tier 2: Context
| Check | Status | Notes |
|-------|--------|-------|
| Token Tracking | PASS | usage in span attributes |
| Cost Attribution | FAIL | No cost calculation |
| User Context | PARTIAL | user_id but no session |
#### Tier 3: Multi-Agent
| Check | Status | Notes |
|-------|--------|-------|
| Parent-Child | FAIL | Child agents orphaned |
| Handoff Logging | FAIL | No delegation tracking |
#### Tier 4: Evaluation
| Check | Status | Notes |
|-------|--------|-------|
| Quality Scores | FAIL | Not implemented |
| Feedback Capture | FAIL | Not implemented |
### Coverage Score
[##########..........] 50% Overall
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.
- 8d ago First seen · 189 lines · 7 tokens per session scan A 27a76b6a2af8
audit is a command published in the GitHub repository nexus-labs-automation/agent-observability (7 stars, last pushed 8mo ago), licensed MIT. It adds 7 tokens to every session and 1,260 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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.