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 agents/rocketride-org/rocketride-server/rocketride_observabilitygit clone --depth 1 https://github.com/rocketride-org/rocketride-serverWrote 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/rocketride-org/rocketride-server/rocketride_observability)<a href="https://agentmods.dev/agents/rocketride-org/rocketride-server/rocketride_observability"><img src="https://agentmods.dev/badge/agents/rocketride-org/rocketride-server/rocketride_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 | $0.00000 | $0.04278 |
| Opus 5 | $0.00000 | $0.02139 |
| Sonnet 5 | $0.00000 | $0.00856 |
| Haiku 4.5 | $0.00000 | $0.00428 |
Grade A, and why
ROCKETRIDE_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 4d 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 — 454 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RocketRide Observability & Tracing Integration Guide
This document describes how an external service (e.g. an "agents database") consumes runtime logs, lifecycle events, and pipeline tracing data from a RocketRide server.
It reflects the actual server implementation, not aspirational features. If a feature is not listed here, it likely does not exist in the server.
1. What RocketRide ships for observability
RocketRide does not expose OpenTelemetry, Jaeger, Prometheus /metrics,
Sentry, webhook registration, audit-log tables, or a queryable history database.
There is no SQL store of past runs to read from.
Everything is delivered live over a single channel: a WebSocket Debug Adapter Protocol (DAP) connection on which the server emits typed events. To capture historical data, your service must connect, subscribe, and write the events to its own database as they arrive.
The features that do exist:
| Feature | Surface | Granularity |
|---|---|---|
Task lifecycle events (begin / end / running / restart) |
DAP event apaevt_task |
Per task |
| Periodic full task status (counts, rates, errors, metrics, tokens) | DAP event apaevt_status_update |
Per task, periodic |
| Pipeline flow / component traces (op, lane, input/output, result, error) | DAP event apaevt_flow |
Per pipe, per op |
| Engine stdout/stderr-style log lines | DAP event with event: 'output' (forwarded as EVENT_TYPE.OUTPUT) |
Per task |
Real-time node→UI custom messages (monitorSSE) |
DAP event apaevt_sse |
Per pipe |
| File upload progress | DAP event apaevt_status_upload |
Per upload |
| Server/admin dashboard events (connection added/removed, monitor changes) | DAP event apaevt_dashboard |
Server-wide |
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.
- 4d ago First seen · 454 lines · 0 tokens per session scan A 7c795931e87c
ROCKETRIDE_OBSERVABILITY is an agent published in the GitHub repository rocketride-org/rocketride-server (7,705 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,278 tokens. 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
stefan-reviewer
Emulates Stefan’s PR review style. Proactively finds the related PR for the current branch (or asks), diffs against origin/develop by default, performs a deep system-aware review, and outputs an actionable Markdown document with line references, severity, and suggestions.
data-engineer
Adversarial data and database engineer who assumes the design is mis-normalized and indexed for a workload that does not exist. Audits schemas, migrations, queries, ORM code, document shapes, stream contracts, and pipelines against normalization, dimensional modeling, key-value access patterns, columnar and…
data-analyst
Use when the user asks to describe, profile, summarize, explore, or query tabular data without producing modified output files — e.g., "what's in this CSV", "show distributions", "what columns correlate", or ad-hoc SQL questions. Prefer data-wrangler when the user wants to clean, reshape, dedupe, join, or convert data.
Albert Einstein (Analyst)
Use for data analysis, SQL queries, CSV processing, data transformation, metrics, reports, or anything involving data. Triggers on "analyze", "query", SQL, CSV, data files, "how many", "show me the". Examples: Context: User has a CSV file and wants insights from it. user: "Analyze this CSV and show top sellers"…
haiku-importer
Cheap data import/export and bulk file-IO worker. Use for reading CSV/JSON/YAML/SQL dumps, transforming between formats, extracting fields, splitting/merging files, downloading and parsing fixtures, generating boilerplate scaffolds from a template, batch-renaming, and any mechanical "shuffle bytes around" task. Do NOT…
timps_data_wrangler
Clean and normalise messy data: CSV, JSON, PDF extracts, copy-pasted tables. Returns cleaned records, a quality score, and SQL insert hint. Use the timpsdatawrangler MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.