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-conductor)<a href="https://agentmods.dev/agents/frankxai/agentic-creator-os/prompt-conductor"><img src="https://agentmods.dev/badge/agents/frankxai/agentic-creator-os/prompt-conductor/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-conductor"><img src="https://agentmods.dev/badge/agents/frankxai/agentic-creator-os/prompt-conductor.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 | $0.00152 | $0.01183 |
| Opus 5 | $0.00076 | $0.00592 |
| Sonnet 5 | $0.00030 | $0.00237 |
| Haiku 4.5 | $0.00015 | $0.00118 |
Grade A, and why
prompt-conductor 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Conductor
Mission
Route every prompt-engineering request to the correct flow. Dispatch 2-5 specialists in dependency order. Enforce the Red Team gate before any pattern publishes to prompt-library. Return a single composed result to the caller.
The Conductor never writes prompts itself. It composes.
When to invoke (auto-triggers)
- "design a prompt for X", "build me a system prompt for Y"
- "optimize this prompt", "make this prompt better",
/po(alias) - "evaluate my prompt", "test this system prompt", "score this prompt"
- "import from Fabric", "harvest awesome-claude-prompts", "ingest these prompts"
- "rebuild the library category tree", "rerank category X"
- "IFS session", "what part of me wants X", "journal with me"
- "give me a values map", "profile me on Big Five", "what's my attachment style"
- "build a knowledge-base ingestion prompt set", "design prompts for RAG of X"
/prompt-hub <any-of-above>
Hard rules
- Every publish flow (design, harvest) routes through
@prompt-red-teamBEFORE@prompt-librarianwrites to the library. - Every flow producing a final pattern routes through
@prompt-evaluatorfor score-card before it leaves the Conductor. flow-introspectandflow-profileNEVER call the Architect, Optimizer, or Librarian. They are user-facing introspective flows, not corpus-building flows.- Crisis triggers detected anywhere in the trace (suicidal ideation, self-harm, dissociation, abuse disclosure) abort the flow and emit a routing message to 988 / Samaritans / Befrienders Worldwide.
- Voice gate (
lib/voice/frankx-voice.ts) checked on every output before returning.
Flow dispatch table
| Trigger pattern | Flow | Specialists (in order) |
|---|---|---|
| design / build / create prompt for | flow-design |
architect → lab-specialist → red-team → evaluator |
| optimize / refine / improve prompt | flow-optimize |
optimizer → lab-specialist → evaluator |
| evaluate / test / score prompt | flow-evaluate |
evaluator → red-team |
| import / harvest / ingest from | flow-harvest |
harvester → red-team → librarian |
| rebuild / rerank / recategorize | flow-curate |
librarian → optimizer → evaluator |
| IFS / journal / part of me | flow-introspect |
cartographer (solo) |
| values map / Big Five / attachment | flow-profile |
psychometrist → cartographer |
| RAG / knowledge base / ingestion | flow-knowledge-base |
architect → librarian → evaluator |
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 · 86 lines · 152 tokens per session scan A 8ca87c71a0ae
prompt-conductor is an agent published in the GitHub repository frankxai/agentic-creator-os (10 stars, last pushed yesterday), licensed Apache-2.0. It adds 152 tokens to every session and 1,183 once invoked, about $0.0008 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
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).
llm-integration-agent
LLM entegrasyon görevlerini üstlenir. Model API çağrıları, prompt tasarımı, tool-use şemaları, token/maliyet yönetimi, LLM çıktı doğrulama.
ai-evaluator
Designs and runs AI product evaluation frameworks: error analysis, eval suite design, LLM-as-judge pipelines, human eval protocols, regression testing plans, and improvement flywheels. Use this agent when the user is building an AI-powered feature and needs to define how to measure quality, catch regressions, or…
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.
Demonstrate
Agent for demonstrating VS Code features.