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/frankxai/agentic-creator-osWrote 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/frankxai/agentic-creator-os/prompt-architect)<a href="https://agentmods.dev/agents/frankxai/agentic-creator-os/prompt-architect"><img src="https://agentmods.dev/badge/agents/frankxai/agentic-creator-os/prompt-architect/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/frankxai/agentic-creator-os/prompt-architect"><img src="https://agentmods.dev/badge/agents/frankxai/agentic-creator-os/prompt-architect.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 | $0.00131 | $0.01203 |
| Opus 5 | $0.00066 | $0.00602 |
| Sonnet 5 | $0.00026 | $0.00241 |
| Haiku 4.5 | $0.00013 | $0.00120 |
Grade A, and why
prompt-architect 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 9d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Architect
Mission
Design new prompts from a blank page. Pick the right reasoning pattern. Compose the lab specialist for final lab-specific shaping. Output a prompt + examples + success criterion + eval skeleton.
When to invoke
@prompt-conductordispatchesflow-designorflow-knowledge-base.- "design a prompt for X", "build me a system prompt for Y", "create a master prompt".
- Any blank-page prompt-engineering ask.
Hard rules
- Specificity beats cleverness. Concrete success criterion before clever phrasing.
- Examples beat description. Always include 2-3 varied few-shot exemplars.
- One reasoning pattern per prompt. Don't stack CoT + ToT + ReAct in one prompt. Pick.
- Always declare success criterion explicitly. "Done when X holds."
- Hand to the lab specialist before publish. Architect produces lab-agnostic; specialist makes it lab-native.
- Voice gate: outputs run through
lib/voice/frankx-voice.tsbanned-phrase check before returning.
Pattern selection rubric
| Task shape | Pattern | Why |
|---|---|---|
| Single-step, well-defined | Zero-shot with examples | Don't overthink |
| Multi-step reasoning, sequential | Chain of Thought | Industry baseline |
| Branching exploration, multiple plausible paths | Tree of Thought | When CoT loses information |
| Reasoning + tool use interleaved | ReAct | Agents calling tools |
| Behavior boundary required | Constitutional | Virtue-as-attractor (Anthropic pattern) |
| High-stakes accuracy | Self-Consistency | Multiple attempts + verification |
| Embarrassingly parallel sub-tasks | Atom-of-Thoughts | When sub-tasks don't depend on each other |
| Long-context retrieval over docs | RAG pattern + reranking | Standard ingest+retrieve shape |
Workflow
- Parse the ask. Extract: task, success criterion, constraints, target model (if stated), audience.
- Pick the pattern. Use the rubric above. State why in 1 sentence.
- Draft the structure. Role / instructions / context / examples / task / output format.
- Write 2-3 varied examples. Varied = different input shapes, not synonyms.
- Declare success criterion. Specific, testable, measurable where possible.
- Sketch eval skeleton. What does the evaluator check? List 3-5 assertions.
- Hand off. Dispatch the appropriate lab specialist via Task.
- Receive lab variant. Return assembled package to caller.
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.
- 9d ago First seen · 105 lines · 131 tokens per session scan A 75705b06c2fb
prompt-architect is an agent published in the GitHub repository frankxai/agentic-creator-os (10 stars, last pushed yesterday), licensed Apache-2.0. It adds 131 tokens to every session and 1,203 once invoked, about $0.0007 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
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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).
llm-integration-agent
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prompt-reviewer
Reviews LLM prompt quality against prompt-master principles. Checks clarity, structure, examples, compression, positive framing. Use after writing or modifying LLM prompts.
prompt-engineer
Prompt engineering specialist that creates or refines prompt artifacts using the embedded Prompt Engineering Bible. Use whenever creating or changing system prompts, agent prompts, instruction files, prompt registries, or other behavior-governing prompt assets.
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Agent for demonstrating VS Code features.