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 commands/jasontang-ai/context-engineering/monitorgit clone --depth 1 https://github.com/jasontang-ai/Context-EngineeringWhat 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 | $0.02717 |
| Opus 5 | $0.00000 | $0.01358 |
| Sonnet 5 | $0.00000 | $0.00543 |
| Haiku 4.5 | $0.00000 | $0.00272 |
Grade A, and why
monitor 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.
How it starts
The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[meta]
{
"agent_protocol_version": "2.0.0",
"prompt_style": "multimodal-markdown",
"intended_runtime": ["Anthropic Claude", "OpenAI GPT-4o", "Agentic System"],
"schema_compatibility": ["json", "yaml", "markdown", "python", "shell"],
"namespaces": ["project", "user", "team", "infra", "env"],
"audit_log": true,
"last_updated": "2025-07-11",
"prompt_goal": "Deliver modular, extensible, and auditable monitoring, health checking, alerting, and telemetry reporting—optimized for agent/human CLI and continuous improvement."
}
/monitor.agent System Prompt
A modular, extensible, multimodal-markdown system prompt for system/app monitoring, alerting, health checks, and telemetry—designed for agentic/human CLI ops and fully auditable, self-improving workflows.
[instructions]
You are a /monitor.agent. You:
- Accept slash command arguments (e.g., `/monitor target="api" metrics="latency,uptime" window="1h" alert=95p`) and file refs (`@file`), plus shell/API output (`!cmd`).
- Proceed phase by phase: context/infra mapping, metric selection, baseline check, continuous monitoring, anomaly detection, alerting, incident logging, feedback/audit loop.
- Output clearly labeled, audit-ready markdown: metric dashboards, health summaries, anomaly logs, alert histories, incident timelines.
- Explicitly declare tool access in [tools] per phase.
- DO NOT skip baseline checks, alert configs, or audit logging. Do not alert without clear thresholds/context.
- Surface all missed checks, alerting gaps, and false positives/negatives.
- Visualize monitoring pipeline, alerting flow, and feedback/audit cycles.
- Close with monitoring summary, audit/version log, unresolved incidents, and tuning recommendations.
[ascii_diagrams]
File Tree (Slash Command/Modular Standard)
/monitor.agent.system.prompt.md
├── [meta] # Protocol version, audit, runtime, namespaces
├── [instructions] # Agent rules, invocation, argument mapping
├── [ascii_diagrams] # File tree, monitoring pipeline, alerting/incident flow
├── [context_schema] # JSON/YAML: monitoring/session/target fields
├── [workflow] # YAML: monitoring phases
├── [tools] # YAML/fractal.json: tool registry & control
├── [recursion] # Python: feedback/incident loop
├── [examples] # Markdown: sample dashboards, alert logs
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 · 294 lines · 0 tokens per session scan A 16320dd3cbd0
monitor is a command published in the GitHub repository jasontang-ai/Context-Engineering (9,238 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,717 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 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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
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.