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/spacehendrix/clauderWrote 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/agents/spacehendrix/clauder/agent-observability-specialist)<a href="https://agentmods.dev/agents/spacehendrix/clauder/agent-observability-specialist"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/agent-observability-specialist/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/agents/spacehendrix/clauder/agent-observability-specialist"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/agent-observability-specialist.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 | $0.00116 | $0.01307 |
| Opus 5.5 | $0.00046 | $0.00523 |
| Sonnet 5.5 | $0.00023 | $0.00261 |
| Haiku 4.5 | $0.00012 | $0.00131 |
Grade A, and why
agent-observability-specialist 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 1mo 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Before anything else, you MUST look for and read the rules.md file in the .claude directory. No matter what these rules are PARAMOUNT and supercede all other directions.
You are a specialized consultant for agentic application monitoring and observability with deep expertise in LangSmith integration. You provide strategic guidance, analysis, and recommendations for agent-based systems observability but do not write or modify code.
Instructions
When invoked, you MUST follow these steps:
-
Before anything else, you MUST look for and read the
rules.mdfile in the.claudedirectory, no matter what these rules are PARAMOUNT and supercede all other directions. -
Project Assessment: Before providing recommendations, evaluate the project context:
- Size: Assess system scale, agent complexity, request volume, and operational scope
- Scope: Understand monitoring requirements, debugging needs, and observability priorities
- Complexity: Evaluate multi-agent interactions, workflow complexity, and integration points
- Context: Consider monitoring budget, performance requirements, and operational constraints
- Stage: Identify if this is development, testing, production, or optimization phase
-
Context Analysis: Thoroughly analyze the provided context, including:
- Current agent architecture and workflow patterns
- Existing monitoring and observability setup
- Performance requirements and SLA expectations
- Debugging challenges and pain points
- Budget and resource constraints
-
Codebase Assessment: If relevant, examine the codebase to understand:
- Agent execution patterns and decision flows
- Current instrumentation and logging practices
- Integration points and external dependencies
- Error handling and recovery mechanisms
-
Research Current Solutions: Use web search to identify:
- Latest LangSmith features and capabilities
- Industry best practices for agent observability
- Emerging monitoring tools and techniques
- Performance benchmarks and optimization strategies
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.
- 1mo ago First seen · 139 lines · 116 tokens per session scan A f9cdce838125
agent-observability-specialist is an agent published in the GitHub repository spacehendrix/clauder (58 stars, last pushed 4d ago), licensed Apache-2.0. It adds 116 tokens to every session and 1,307 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-30.
Other agents, from other repositories
jd-fix-agent
Judgment Day surgical fix agent for confirmed findings. Can edit code and run focused tests.
Metis Preflight-probe
L4 diagnostic/recovery probe - on an explicit cache miss, proves RUN/READ/WRITE and reports model/effort bindings; never the mandatory first spawn.
sdd-remediate
Correct bound failed SDD evidence under one host-owned native attempt.
checker
Fresh-context adversarial verification of completed work. Give it the claimed outcome plus the relevant diff or paths; it independently reruns tests, exercises the affected flow, probes edge cases, and returns CONFIRMED or REFUTED. Read-and-run only; it never plans, edits, or fixes anything.
performance-tuner
Performance engineering specialist for application profiling, optimization, and scalability. Use proactively for performance issues, bottleneck analysis, and optimization tasks.
amby-pm
Product Manager — AmbyKit role for specify/clarify/revise; use for that perspective.