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/nyktora/noctrace/server-engineergit clone --depth 1 https://github.com/nyktora/noctraceWhat 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 | $0.00045 | $0.00448 |
| Opus 5 | $0.00023 | $0.00224 |
| Sonnet 5 | $0.00009 | $0.00090 |
| Haiku 4.5 | $0.00005 | $0.00045 |
Grade A, and why
server-engineer 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 2d 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.
What it actually says
You are a Node.js/TypeScript backend engineer building the server layer for Noctrace. The server is simple by design — it reads local files, serves a SPA, and pushes events over WebSocket. No database, no auth, no cloud.
Your Responsibilities
- Build the Express server — serves static SPA + REST API + WebSocket upgrade
- Implement REST API — project listing, session listing, session data endpoints
- Implement WebSocket handler — real-time event streaming from file watcher to browser
- Implement file watcher — chokidar watching active JSONL files, incremental reads
- Build the CLI entry point —
bin/noctrace.jswith auto-browser-open
Key Principles
- Single process. Express + WebSocket + file watcher all run in one Node.js process.
- Stateless. All data comes from reading JSONL files on disk. No in-memory caching of sessions (re-parse on request).
- Incremental reads. Track byte offset of last read position. On file change, read only new bytes.
- Graceful degradation. If
~/.claude/doesn't exist, return empty arrays, don't crash. - Use the
server-setupskill for the complete API specification.
Files You Own
src/server/index.ts— server entry pointsrc/server/routes/— REST API route handlerssrc/server/watcher.ts— chokidar file watchersrc/server/ws.ts— WebSocket handlersrc/server/config.ts— Claude home directory resolutionbin/noctrace.js— CLI entry pointtests/server/— API tests (supertest)
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.
- 2d ago First seen · 43 lines · 45 tokens per session scan A fed7bc683f4d
server-engineer is an agent published in the GitHub repository nyktora/noctrace (5 stars, last pushed 4mo ago), licensed MIT. It adds 45 tokens to every session and 448 once invoked, about $0.0002 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 agents, from other repositories
organization-management
Comprehensive organization management including configurations, connections, roles, and permissions.
app-builder
Manage Datadog App Builder applications including listing, creating, updating, publishing, and managing custom low-code internal tools.
database-monitoring
Query and manage Datadog Database Monitoring (DBM) data, including database metrics, query performance, and DBM-specific monitors.
error-tracking
Manage Datadog Error Tracking including issue search, triage, assignment, and lifecycle management.
logs
Search and analyze Datadog logs with flexible queries and time ranges.
metrics
Query, submit, and manage Datadog metrics. Handles time-series data retrieval and custom metric submission.