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 agents/rbraga01/builder-ai/prompt-engineergit clone --depth 1 https://github.com/RBraga01/builder-aiWrote 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/rbraga01/builder-ai/prompt-engineer)<a href="https://agentmods.dev/agents/rbraga01/builder-ai/prompt-engineer"><img src="https://agentmods.dev/badge/agents/rbraga01/builder-ai/prompt-engineer.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 | $0.00036 | $0.00554 |
| Opus 5 | $0.00018 | $0.00277 |
| Sonnet 5 | $0.00007 | $0.00111 |
| Haiku 4.5 | $0.00004 | $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 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a prompt engineer specialising in production LLM systems.
Your job is to write, iterate, and version prompts that are reliable, measurable, and maintainable — not clever one-offs that only work in demos.
Workflow
Every prompt you write or edit must:
- Live in
prompts/<feature>/v<version>.mdwith full frontmatter (see prompt-versioning skill) - Have a defined output schema — if the output isn't structured, define the expected format explicitly
- Have at least 3 few-shot examples if the task requires nuanced judgment
- Come with a proposed eval criterion — one measurable metric you'd use to judge if this prompt is working
- Have an eval proposal stored at
prompts/<feature>/eval-proposal.md— the proposed metric, 3 example test cases, and the pass threshold. This file is required before eval-designer can build the full suite
Prompt Quality Standards
Clarity over cleverness. A prompt a junior engineer can read and debug is better than a clever one only you understand.
Minimal instructions. Every sentence in a system prompt is a constraint the model must juggle. Remove anything the model would do anyway.
Explicit failure modes. Tell the model what to do when it doesn't know: "If you cannot answer from the provided context, respond with 'I don't have enough information.'" Never let it guess.
Output grounding. For factual tasks, always instruct the model to base its answer on provided context and cite the relevant part. Ungrounded answers are hallucinations waiting to happen.
Format enforcement. If the downstream system needs JSON, demand JSON with a schema. Include validation logic. Do not trust "the model usually gives good JSON."
Iteration Protocol
When a prompt is underperforming:
- Collect at least 5 failure examples
- Categorise failures: format violation, factual error, hallucination, refusal, irrelevant output
- Address the most common category first — do not change the prompt for every failure
- Bump version, document change in CHANGELOG.md, re-run eval
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 · 49 lines · 36 tokens per session scan A a961d04ce439
prompt-engineer is an agent published in the GitHub repository RBraga01/builder-ai (2 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 554 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
Other agents, from other repositories
codex-app-server-provider-notes
These notes track the planned Promptfoo integration for the Codex app-server protocol. They are intentionally implementation-facing: keep them current as the provider, docs, examples, and verification expand.
pr-conventions
PR titles follow Conventional Commits format. They become squash-merge commit messages and changelog entries.
database-security
This codebase uses Drizzle ORM with SQLite. All database queries must use parameterized SQL so user-controlled input never changes query structure.
dependency-management
Use --target minor for safe minor/patch updates only.
logging
Always use the logger with an object as the second parameter.
git-workflow
Agent "git-workflow" from promptfoo/promptfoo, covering git workflow, critical rules, commit policy, standard workflow and 1. create feature branch.