Borrowing it
Nothing to install: this file belongs to nexus-labs-automation/agent-observability. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nexus-labs-automation/agent-observability/main/CLAUDE.mdgit 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/instructions/nexus-labs-automation/agent-observability/claude-md)<a href="https://agentmods.dev/instructions/nexus-labs-automation/agent-observability/claude-md"><img src="https://agentmods.dev/badge/instructions/nexus-labs-automation/agent-observability/claude-md/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/instructions/nexus-labs-automation/agent-observability/claude-md"><img src="https://agentmods.dev/badge/instructions/nexus-labs-automation/agent-observability/claude-md.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 | $0.01309 | $0.01309 |
| Opus 5 | $0.00655 | $0.00655 |
| Sonnet 5 | $0.00262 | $0.00262 |
| Haiku 4.5 | $0.00131 | $0.00131 |
Grade A, and why
agent-observability CLAUDE.md 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 10d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Observability Plugin
Expert guidance for instrumenting AI agents in production. This plugin teaches best practices for observing multi-agent systems, LLM calls, tool executions, and agent workflows.
When to Use This Plugin
- Setting up observability for new agent systems
- Adding tracing to existing LangChain/LangGraph applications
- Instrumenting Claude Agent SDK or OpenAI Agents
- Auditing agent telemetry for gaps and anti-patterns
- Tracking token usage and costs across agents
- Implementing evaluation and quality metrics
Core Philosophy
Agent-Focused Observability answers three questions for every agent action:
- Intent: What was the agent trying to accomplish?
- Execution: How did the agent attempt to achieve it (LLM calls, tool use, reasoning)?
- Outcome: Did it succeed, fail, or require human intervention?
Skill Routing
| Topic | Skill | Priority |
|---|---|---|
| What to measure | instrumentation-planning |
P1 |
| LLM call tracing | llm-call-tracing |
P1 |
| Tool execution | tool-call-tracking |
P1 |
| Multi-agent workflows | multi-agent-coordination |
P1 |
| Token/cost tracking | token-cost-tracking |
P1 |
| Prompt A/B testing | prompt-versioning |
P1 |
| Safety & guardrails | guardrails-safety |
P1 |
| Agent decision tracing | decision-tracing |
P1 |
| Production eval strategy | production-eval-strategy |
P1 |
| RAG/memory | memory-rag-instrumentation |
P2 |
| Human-in-the-loop | human-in-the-loop |
P2 |
| Errors and retries | error-retry-tracking |
P2 |
| Quality metrics | evaluation-quality |
P2 |
| Sessions/conversations | session-conversation-tracking |
P2 |
Commands
| Command | Purpose |
|---|---|
/instrument |
Generate instrumentation plan for agent system |
/audit |
Scan existing telemetry and identify gaps |
Reference Loading (Context Budget)
All files <9KB - safe to load individually.
| Condition | Load | Size |
|---|---|---|
| Planning instrumentation | references/methodology/*.md |
5-8KB |
| Framework detected | references/frameworks/{framework}.md |
6-8KB |
| Vendor specified | references/vendors/{vendor}.md |
7-8KB |
| Reporting issues | references/anti-patterns/*.md |
5-9KB |
| Generating code | templates/*.py |
3-5KB |
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.
- 10d ago First seen · 129 lines · 1,309 tokens per session scan A f61a65c0dde9
agent-observability CLAUDE.md is an instructions file published in the GitHub repository nexus-labs-automation/agent-observability (7 stars, last pushed 8mo ago), licensed MIT. It adds 1,309 tokens to every session, about $0.0065 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 instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.