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 citedy/adclaw --skill citedy-content-ingestiongit clone --depth 1 https://github.com/citedy/adclawWrote 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/citedy/adclaw/citedy-content-ingestion)<a href="https://agentmods.dev/skills/citedy/adclaw/citedy-content-ingestion"><img src="https://agentmods.dev/badge/skills/citedy/adclaw/citedy-content-ingestion/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/citedy/adclaw/citedy-content-ingestion"><img src="https://agentmods.dev/badge/skills/citedy/adclaw/citedy-content-ingestion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 181 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00053 | $0.03321 |
| Opus 5 | $0.00026 | $0.01661 |
| Sonnet 5 | $0.00011 | $0.00664 |
| Haiku 4.5 | $0.00005 | $0.00332 |
Grade A, and why
citedy-content-ingestion scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST https://www.citedy.com/api/agent/ingest \ Copies of this mod
1 near-identical copy found in the catalogue:
- citedy-content-ingestion — 94% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 477 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Ingestion — Skill Instructions
Connection: REST API over HTTPS
Base URL: https://www.citedy.com
Auth: Authorization: Bearer $CITEDY_API_KEY
Overview
Turn any URL into structured content your agent can use. Pass a link — the skill extracts the full text, transcript, metadata, and summary — and returns it as clean structured data ready for your LLM pipeline.
Supported content types:
- YouTube videos — full transcription via Gemini Video API (not just captions)
- Web articles — clean article text with metadata
- PDF documents — text extraction from public PDF URLs
- Audio files — transcription from MP3/WAV/M4A files
Differentiator: YouTube ingestion uses the Gemini Video API for deep video understanding — it goes beyond auto-generated captions, capturing speaker intent, visual context, and structure.
Use this skill as a standalone input node for any LLM pipeline. Feed the output directly into summarization, Q&A, article generation, or knowledge base indexing.
When to Use
Use this skill when the user:
- Asks to extract, transcribe, or summarize a URL
- Shares a YouTube video and wants the content analyzed or repurposed
- Shares a PDF link and wants the text extracted
- Wants to ingest audio content for transcription
- Is building a pipeline that needs to pull content from the web
Instructions
Setup
To authenticate, register your agent and obtain an API key:
-
Register your agent:
POST https://www.citedy.com/api/agent/register Content-Type: application/json { "agent_name": "My Agent", "contact_email": "[email protected]" } -
Approve the registration — you will receive an approval URL in the response. Open it in your browser to confirm.
-
Save your API key — after approval, the key (prefixed
citedy_agent_) is returned. Store it asCITEDY_API_KEYin your environment.
All subsequent requests use Authorization: Bearer $CITEDY_API_KEY.
What ships with it
1 file 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.
- 11d ago First seen · 477 lines · 53 tokens per session scan A 7dcf848bbdb9
citedy-content-ingestion is a skill published in the GitHub repository citedy/adclaw (36 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 3,321 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
citedy-content-ingestion
Turn any URL into structured content — YouTube videos (via Gemini Video API), web articles, PDFs, and audio files. Extract transcripts, summaries, and metadata for use in any LLM pipeline. Powered by Citedy.
multimodal-llm
Vision, audio, video generation, and multimodal LLM integration patterns. Use when processing images, transcribing audio, generating speech, generating AI video (Kling v3, Sora 2, Veo 3.1 std/lite/fast, Runway Gen-4.5 via gen4turbo), or building multimodal AI pipelines.
transcribe-tool
Audio transcription tool. Converts audio files to text with Whisper and optional LLM post-processing. Use when: transcribing meetings, podcasts, or extracting text from recorded audio files.
youtube-transcribe
Transcribe YouTube videos and playlists. Extract audio to text with visual context, generate summaries and detailed notes.
ai-core/media-generation
Image, audio, video, speech (TTS), and transcription generation using activity-specific adapters: generateImage() with openaiImage/geminiImage/byteplusImage, generateAudio() with geminiAudio/falAudio, generateVideo() with async polling (openaiVideo/geminiVideo/grokVideo/falVideo/byteplusVideo/openRouterVideo…
wegent-knowledge
Knowledge base management and search tools for Wegent. Provides capabilities to list, create, update, and search knowledge bases and documents using RAG retrieval. Use this skill when the user wants to manage knowledge bases, documents, or search for information programmatically.