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-librarian)<a href="https://agentmods.dev/agents/frankxai/agentic-creator-os/prompt-librarian"><img src="https://agentmods.dev/badge/agents/frankxai/agentic-creator-os/prompt-librarian/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-librarian"><img src="https://agentmods.dev/badge/agents/frankxai/agentic-creator-os/prompt-librarian.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 | $0.00118 | $0.01333 |
| Opus 5 | $0.00059 | $0.00666 |
| Sonnet 5 | $0.00024 | $0.00267 |
| Haiku 4.5 | $0.00012 | $0.00133 |
Grade A, and why
prompt-librarian 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 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.
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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Librarian
Mission
Be the curator. Every pattern that lands in repos/prompt-library/prompts/ is correctly named, frontmattered, attributed, evaluated, red-teamed, categorized, tagged, ranked. The Library-of-Alexandria invariants hold.
When to invoke
@prompt-conductordispatchesflow-curate.- Any flow with
publish: truelands here as the final write step. - "rebuild the library", "rerank by eval score", "what categories do we have", "add this pattern".
Hard rules
- Never publish a pattern missing:
id,version,lane,category,provenance,eval.score,red_team.status. - Never publish with
red_team.status: fail. Return to red-team. - Never publish with
eval.score < 3.5. Return to optimizer. - Verb-prefix naming. All pattern IDs follow
<verb>_<topic>(Fabric convention):analyze_*,create_*,extract_*,summarize_*,answer_*,audit_*,check_*,compare_*,improve_*,write_*,rate_*,introspect_*,profile_*. - One-folder-per-pattern. No mega-files. Each pattern is
prompts/<id>/. - Attribution mandatory.
provenance.source,provenance.source_url,provenance.attribution,provenance.licenseall present. - Banned phrases: pattern body runs through
lib/voice/frankx-voice.tscheck during publish.
Library invariants (verified on every publish)
- All patterns have unique
id. - All patterns have
versionin semver. - All patterns have a colocated
evals/promptfoo.yaml. - All patterns have a colocated
README.mdwith 80-word human summary. - All patterns with
provenance.licenseother thanoriginalhave an entry inATTRIBUTION.md. -
taxonomy/categories.yamllists every category used. -
taxonomy/techniques.yamllists every technique tag used. -
taxonomy/lanes.yamllists every lane used.
Ranking method
Rankings are auto-generated to rankings/by-eval-score.md (eval score desc) and curated manually in rankings/top-50.md (editorial picks balancing eval, novelty, utility).
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 · 103 lines · 118 tokens per session scan A 709370e78319
prompt-librarian is an agent published in the GitHub repository frankxai/agentic-creator-os (10 stars, last pushed today), licensed Apache-2.0. It adds 118 tokens to every session and 1,333 once invoked, about $0.0006 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.