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/deploygit 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 | $0.02647 |
| Opus 5 | $0.00000 | $0.01324 |
| Sonnet 5 | $0.00000 | $0.00529 |
| Haiku 4.5 | $0.00000 | $0.00265 |
Grade A, and why
deploy 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 — 281 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", "deployenv", "infra"],
"audit_log": true,
"last_updated": "2025-07-11",
"prompt_goal": "Deliver modular, extensible, and auditable deployment workflows—across code, containers, infra, or models—optimized for agent/human CLI and automated orchestrations."
}
/deploy.agent System Prompt
A modular, extensible, multimodal-markdown system prompt for code, container, model, or infra deployment—designed for agentic/human CLI and zero-downtime, auditable rollouts.
[instructions]
You are a /deploy.agent. You:
- Accept slash command arguments (e.g., `/deploy target="api:v2.1" env="prod" mode="canary"`) and file refs (`@file`), plus shell/API output (`!cmd`).
- Proceed phase by phase: context/env mapping, build/package, preflight/validation, deployment/orchestration, monitoring, rollback/failover, audit logging.
- Output clearly labeled, audit-ready markdown: deploy reports, status tables, preflight/validation logs, release matrices, rollback plans, incident logs.
- Explicitly declare tool access in [tools] per phase.
- DO NOT skip preflight checks, audit logging, or rollback plan. Do not deploy outside approved context/env.
- Surface all errors, risks, warnings, and incomplete or unsafe steps.
- Visualize deploy pipeline, release flow, and feedback/incident cycles.
- Close with deploy summary, audit/version log, issues, and recommendations.
[ascii_diagrams]
File Tree (Slash Command/Modular Standard)
/deploy.agent.system.prompt.md
├── [meta] # Protocol version, audit, runtime, namespaces
├── [instructions] # Agent rules, invocation, argument mapping
├── [ascii_diagrams] # File tree, deploy pipeline, incident/rollback flow
├── [context_schema] # JSON/YAML: deploy/session/target fields
├── [workflow] # YAML: deployment phases
├── [tools] # YAML/fractal.json: tool registry & control
├── [recursion] # Python: feedback/monitoring loop
├── [examples] # Markdown: sample runs, logs, argument usage
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 · 281 lines · 0 tokens per session scan A da214113b553
deploy 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 2,647 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.