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.
git clone --depth 1 https://github.com/josstei/maestro-orchestrateWrote 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/josstei/maestro-orchestrate/prompt-engineer)<a href="https://agentmods.dev/agents/josstei/maestro-orchestrate/prompt-engineer"><img src="https://agentmods.dev/badge/agents/josstei/maestro-orchestrate/prompt-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/josstei/maestro-orchestrate/prompt-engineer"><img src="https://agentmods.dev/badge/agents/josstei/maestro-orchestrate/prompt-engineer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00287 | $0.00406 |
| Opus 5.5 | $0.00115 | $0.00162 |
| Sonnet 5.5 | $0.00057 | $0.00081 |
| Haiku 4.5 | $0.00029 | $0.00041 |
Grade B, and why
prompt-engineer scanned grade B with 1 finding 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 10d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
Prompt Engineer is appropriate for structured-output prompt design with a measurement plan. What it actually says
Agent methodology loaded via MCP tool get_agent. Call get_agent(agents: ["prompt-engineer"]) to read the full methodology at delegation time.
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.
- 10d ago First seen · 39 lines · 287 tokens per session scan B 4534d96561ef
prompt-engineer is an agent published in the GitHub repository josstei/maestro-orchestrate (484 stars, last pushed 6d ago), licensed Apache-2.0. It adds 287 tokens to every session and 406 once invoked, about $0.0011 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-07.
Other agents, from other repositories
ai-eng-warden
AI Engineering review of code touching LLM interactions, prompt construction, context management, agent architecture, and AI-specific security. Fires on diffs that modify prompt templates, gate specs, agent role specs, model parameters, retrieval/RAG code, or token budget logic. Strict mode - REVISE blocks commits.…
critic-security
Review code for OWASP-style security issues (injection, authn/authz, secrets, supply chain, LLM-specific) in parallel with other critics during /vdd-multi. For full audits producing a formal docs/audit/ report, use security-auditor instead.
architecture-reviewer
Review docs/ARCHITECTURE.md for Data Model correctness, Security (OWASP), Scalability, and YAGNI. Spawn after architect to gate the Architecture→Planning boundary.
prompt-engineer-pm
Owns the AI product's PROMPT discipline — versioning, registry, prompt-as-code, prompt review, prompt-vs-fine-tune decisions. The PM-side architect for everything the product sends to a model. NOT to be confused with query-refiner-pm (which refines USER queries TO great-pm).
ai-ml-engineer
Builds, trains, and optimizes machine learning models and AI tools — model selection, prompts, evaluation, and integration.
prompt-reviewer
LLM prompt-engineering expert for the review-panel skill. Spawned when the diff touches LLM/API prompts, prompt templates, or inline model instructions in application code (system/user prompts, few-shot templates, prompt-string builders). Reviews prompting quality, output contracts, context economy, injection surface…