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 librefang/librefang-registry --skill creatorgit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/creator)<a href="https://agentmods.dev/skills/librefang/librefang-registry/creator"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/creator/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/librefang/librefang-registry/creator"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/creator.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 | $0.00026 | $0.02365 |
| Opus 5.5 | $0.00010 | $0.00946 |
| Sonnet 5.5 | $0.00005 | $0.00473 |
| Haiku 4.5 | $0.00003 | $0.00236 |
Grade A, and why
media-generation-skill 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.
This is a copy
100% identical to media-generation-skill — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Media Generation Expert Knowledge
Tool Reference
image_generate
Generate images from text prompts via OpenAI or MiniMax.
Parameters:
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
prompt |
string | yes | — | Text description of the desired image |
provider |
string | no | auto | openai or minimax |
model |
string | no | provider default | gpt-image-1, dall-e-3, image-01 |
width |
int | no | 1024 | Image width in pixels |
height |
int | no | 1024 | Image height in pixels |
count |
int | no | 1 | Number of images (1-4) |
quality |
string | no | auto |
low, medium, high, auto |
seed |
int | no | random | Reproducibility seed |
Provider-specific notes:
- OpenAI gpt-image-1: Best for photorealistic and creative images. Supports inpainting hints in prompt. Sizes: 1024x1024, 1792x1024, 1024x1792.
- OpenAI dall-e-3: Good quality, may revise your prompt (check
revised_promptin response). Only generates 1 image per call. - MiniMax image-01: Fast generation, good for illustrations and concept art. Supports arbitrary aspect ratios.
Result: Returns images array with url fields pointing to /api/uploads/{id}.
text_to_speech
Convert text to spoken audio.
Parameters:
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
text |
string | yes | — | Text to speak (max ~4096 chars per call) |
provider |
string | no | auto | openai or minimax |
model |
string | no | provider default | tts-1, tts-1-hd, speech-2.8-hd |
voice |
string | no | alloy |
Voice selection (see table below) |
speed |
float | no | 1.0 | Playback speed (0.25 - 4.0) |
format |
string | no | mp3 |
mp3, wav, flac, opus, aac |
OpenAI voices:
| Voice | Character |
|---|---|
alloy |
Neutral, balanced |
echo |
Male, warm |
fable |
Storytelling, expressive |
nova |
Female, friendly |
onyx |
Deep male, authoritative |
shimmer |
Warm female, gentle |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 250 lines · 26 tokens per session scan A c5bb0ed8ec79
media-generation-skill is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 2,365 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to media-generation-skill, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
nano-banana-pro-openrouter
Deterministic OpenRouter image generation adapter for Nano Banana Pro / Gemini image models. Use as skillexec when a meta-skill needs local image files and structured IMAGEREADY records without spawning an LLM agent.
skill-creator-linter
Internal tool (not user-invocable). Called by meta-skill-creator as a DAG step (kind: agent) to lint a candidate meta-skill SKILL.md against G1 (parse + reference check + xmlescape grep + structural lint) and G2 (scheduler dry-run with stub executors). Deterministic, sub-second, no LLM. Returns JSON diagnostics.
clinicaltrials-database
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
openalex-database
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly…
glycoengineering
Analyze and engineer protein glycosylation. Scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and access curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). For glycoprotein engineering, therapeutic antibody optimization, and vaccine design.
paper-abstract-author
Write the abstract after the paper body has been revised, using the final claims and evidence.