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 skills add frankxai/claude-skills-library --skill prompt-hubgit clone --depth 1 https://github.com/frankxai/claude-skills-libraryWrote 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/skills/frankxai/claude-skills-library/prompt-hub)<a href="https://agentmods.dev/skills/frankxai/claude-skills-library/prompt-hub"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/prompt-hub/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/skills/frankxai/claude-skills-library/prompt-hub"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/prompt-hub.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 | $0.00147 | $0.01441 |
| Opus 5.5 | $0.00059 | $0.00576 |
| Sonnet 5.5 | $0.00029 | $0.00288 |
| Haiku 4.5 | $0.00015 | $0.00144 |
Grade A, and why
prompt-hub 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.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Hub Skill
When this skill is loaded, you have access to the FrankX Prompt Hub: a 13-agent team that handles every prompt-engineering operation across Claude, GPT, Gemini, and OSS models, plus an IFS / psychometric introspection layer.
How to invoke
For any prompt-engineering ask, dispatch @prompt-conductor via the Task tool with the user's verbatim request. The Conductor maps it to one of 8 canonical flows and composes 2-5 specialists.
Do not author prompts yourself when this skill is loaded. Route through the Hub.
The 13 agents
Conductor (1)
@prompt-conductor— Opus composer; routes all asks.
Lab specialists (4) — encapsulate per-lab quirks
@prompt-claude-specialist— XML tags, prefill, extended thinking@prompt-gpt-specialist— developer role, Structured Outputs, contradiction audit@prompt-gemini-specialist— system-at-top, native grounding, "think very hard"@prompt-oss-specialist— Llama / Mistral / Qwen / R1 chat templates
Core builders (3)
@prompt-architect— designs new prompts from blank@prompt-optimizer— refines existing prompts (wraps/po)@prompt-evaluator— wraps promptfoo (MIT), scores prompts
Library curators (2)
@prompt-librarian— ownsprompt-libraryrepo; ranks, tags, attributes@prompt-harvester— bulk-imports from Fabric / awesome-* repos
Safety + psyche (3)
@prompt-red-team— adversarial probes; load-bearing publish gate@prompt-psyche-cartographer— IFS introspection, voice modes, maps not unburdens@prompt-psychometrist— IPIP-50, PVQ, ECR-R, VIA, Enneagram (lenses not verdicts)
The 8 flows
| Flow | Trigger | Sequence |
|---|---|---|
| flow-design | "design a prompt for X" | architect → lab-spec → red-team → evaluator |
| flow-optimize | "optimize this prompt" / /po |
optimizer → lab-spec → evaluator |
| flow-evaluate | "evaluate / test / score this prompt" | evaluator → red-team |
| flow-harvest | "import from Fabric / harvest awesome-*" | harvester → red-team → librarian |
| flow-curate | "rerank / rebuild library" | librarian → optimizer → evaluator |
| flow-introspect | "IFS session / part of me / journal" | cartographer (solo) |
| flow-profile | "Big Five / values map / attachment" | psychometrist → cartographer |
| flow-knowledge-base | "RAG prompts / ingestion set" | 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.
- 1mo ago First seen · 101 lines · 147 tokens per session scan A 177a2a0d5807
prompt-hub is a skill published in the GitHub repository frankxai/claude-skills-library (47 stars, last pushed today), licensed MIT. It adds 147 tokens to every session and 1,441 once invoked, about $0.0006 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 2026-09-03.
Other skills, from other repositories
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
prompt-refiner
Lints an existing user-supplied prompt for prompt-engineering defects and prompt-injection risks. Produces a diagnostic report with rule IDs, severity, line/column, quoted evidence, rationale, and optional JSON output. Does not rewrite prompts or impose prompt frameworks.
prompt-modeler
Generate structured strategic prompts with diagnostic and multiple options. Triggers on: /modelar-prompt.
prompt-library
Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.
prompt-engineering-outlines
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library.
ai-context-primer
Build the context an AI needs to do a task well — the background, constraints, examples, and format it can't guess — so you get a great result on the first try instead of a generic one you have to keep correcting. Use when asked why does AI give me generic answers, how do I give AI better context, my AI results are…