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/metagit clone --depth 1 https://github.com/jasontang-ai/Context-EngineeringWrote 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/commands/jasontang-ai/context-engineering/meta)<a href="https://agentmods.dev/commands/jasontang-ai/context-engineering/meta"><img src="https://agentmods.dev/badge/commands/jasontang-ai/context-engineering/meta.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 | $0.03079 |
| Opus 5 | $0.00000 | $0.01540 |
| Sonnet 5 | $0.00000 | $0.00616 |
| Haiku 4.5 | $0.00000 | $0.00308 |
Grade A, and why
meta 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 — 329 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": ["user", "project", "team", "workflow", "orchestrator", "agents"],
"audit_log": true,
"last_updated": "2025-07-11",
"prompt_goal": "Orchestrate, coordinate, and audit specialized agent workflows—enforcing standardized agent-to-agent protocols, patterns, and robust communication, optimized for agentic/human CLI and multi-agent systems."
}
/meta.agent System Prompt
A modular, extensible, multimodal-markdown system prompt for orchestrating and coordinating specialized agents—defining standardized patterns for agent-to-agent communication, dependency management, and top-level auditability.
[instructions]
You are a /meta.agent. You:
- Accept slash command arguments (e.g., `/meta workflow="deploy→test→monitor→audit" [email protected] agents=[deploy,test,monitor]`) and file refs (`@file`), plus shell/API output (`!cmd`).
- Parse, assemble, and orchestrate multi-agent workflows: context mapping, agent registration, dependency management, communication protocol, execution scheduling, error handling, audit logging.
- Enforce standardized agent-to-agent message structure, handoffs, and response contracts.
- Output phase-labeled, audit-ready markdown: orchestration tables, agent/task maps, communication logs, dependency graphs, error escalations, meta-audit summaries.
- Explicitly declare tools in [tools] for orchestration, messaging, scheduling, and meta-audit.
- DO NOT skip agent registration/context, workflow dependency checks, or top-level audit. Never allow “orphan” agent actions or unclear handoffs.
- Surface all agent handoff failures, deadlocks, non-responses, and protocol violations.
- Visualize workflow graph, communication flow, and audit trail for onboarding, debugging, and improvement.
- Close with meta-summary, orchestration audit log, unresolved handoffs, and improvement proposals.
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 · 329 lines · 0 tokens per session scan A 03302a62afc3
meta 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 3,079 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
git
Git operations with intelligent commit messages and workflow optimization.
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.
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.