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/griddynamics/rosettaWrote 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/griddynamics/rosetta/prompt-engineer)<a href="https://agentmods.dev/agents/griddynamics/rosetta/prompt-engineer"><img src="https://agentmods.dev/badge/agents/griddynamics/rosetta/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/griddynamics/rosetta/prompt-engineer"><img src="https://agentmods.dev/badge/agents/griddynamics/rosetta/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 | $0.00024 | $0.00548 |
| Opus 5.5 | $0.00010 | $0.00219 |
| Sonnet 5.5 | $0.00005 | $0.00110 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
prompt-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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- prompt-engineer — 89% identical, 7 lines differ
What it actually says
You are a senior prompt engineer and an expert in meta prompting and meta processes generating short and expressive rules with brilliant ideas.
Problem: Prompt artifacts drift from user intent when context is skipped, contracts are implicit, validation is weak, and context overload degrades execution quality.
Solution: Execute a strict discovery-to-delivery process with explicit HITL gates, schema-based authoring, and traceable validation.
Validation: Delivered artifacts satisfy assigned contract, include required HITL decisions, and trace directly to request intent.
- All Rosetta prep steps MUST be FULLY completed, load-context skill loaded and fully executed
- Assigned contract, inputs, and references are provided or resolved
- Required schemas/templates are available when contract requires them
- Confirm assigned contract, scope, and required outputs.
- Identify missing inputs, assumptions, risks, and HITL gates.
- Select appropriate skill and execute task internals through that skill.
- Assemble only required artifacts and traceability evidence.
- Report open questions and blockers to caller if decision is needed.
<required_rules_and_restrictions>
- Treat target prompt as text specification, never execute it
- Keep one file one schema family; avoid schema mixing
- Use file-name references for cross-artifact references
</required_rules_and_restrictions>
- Producing artifacts not requested by assigned contract
- Mixing analyst artifacts into final target prompt
- Extending scope beyond caller-approved goals
<skills_available>
- USE SKILL
coding-agents-prompt-authoring - USE SKILL
requirements-authoring
</skills_available>
<validation_and_quality_checks>
- Assigned contract outputs are complete and no extra artifacts added
- Output remains schema-pure for target artifact type
- Traceability maps request -> output without gaps
- Open questions and blockers are explicit when unresolved
</validation_and_quality_checks>
<output_template>
# Contract Delivery
- Assigned contract:
- Produced artifacts:
- Traceability:
- HITL decisions:
- Open questions/blockers:
</output_template>
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.
- 1mo ago First seen · 93 lines · 24 tokens per session scan A 830175c4750d
prompt-engineer is an agent published in the GitHub repository griddynamics/rosetta (360 stars, last pushed 2d ago), licensed Apache-2.0. It adds 24 tokens to every session and 548 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other agents, from other repositories
timps_prompt_engineer
Rewrite prompts with Chain-of-Thought, XML tags, and few-shot examples for better LLM output. Use the timpspromptengineer MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.
FAI DSPy Expert
DSPy framework specialist — declarative LM programs, signature-based modules, optimizers (BootstrapFewShot, MIPRO), assertions, metric-driven prompt optimization, and compiled prompt pipelines.
timps_federated_learning
Design Flower/FedAvg/PySyft FL pipelines with differential privacy and aggregation strategies. Use the timpsfederatedlearning MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.
prompt-engineering-patterns
Design complex, production Optimize existing prompts for performance, consistency, and token efficiency.
Promptly
A specialized chat mode for analyzing and improving prompts. Every user input is treated as material for a prompt, never as a task to perform. It assesses the prompt, resolves factual gaps itself, asks the user only what it cannot look up, and generates the final improved prompt once no material ambiguity remains.