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 SerhiiKorniienko/bullshit-detector --skill sharegit clone --depth 1 https://github.com/SerhiiKorniienko/bullshit-detectorWrote 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/serhiikorniienko/bullshit-detector/share)<a href="https://agentmods.dev/skills/serhiikorniienko/bullshit-detector/share"><img src="https://agentmods.dev/badge/skills/serhiikorniienko/bullshit-detector/share/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/serhiikorniienko/bullshit-detector/share"><img src="https://agentmods.dev/badge/skills/serhiikorniienko/bullshit-detector/share.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.00857 |
| Opus 5 | $0.00046 | $0.00428 |
| Sonnet 5 | $0.00019 | $0.00171 |
| Haiku 4.5 | $0.00009 | $0.00086 |
Grade A, and why
share 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 12d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
share
Take a finished report and produce platform-native content, ready to paste. Nothing generic: each platform gets its own format, length, and link etiquette.
Workflow
- Locate the report. A file the user points at, the report from this conversation, or — if there is none — offer to run the
bullshit-detectorskill first. - Ask which platforms if not stated. Default set: X thread + LinkedIn post.
- Write the posts following the per-platform specs in PLATFORMS.md exactly — hooks, length limits, link placement. Output each as a separate fenced block the user can copy verbatim.
- Carousel (if requested or if the platform benefits): extract the report into a
slides.json(schema below) and render:
uv run <this-skill-dir>/scripts/render_carousel.py slides.json -o carousel/
First run needs a one-time browser install: uv run --with playwright playwright install chromium. Output: slide-N.png (1080×1350, works on X, LinkedIn, Instagram) + carousel.pdf (LinkedIn document post).
slides.json schema
{
"title": "Video/article title",
"source": "Author · Platform · 1.16M views",
"score": 5,
"verdict_line": "Real tools, fantasy income math",
"footer": "@their-handle · their-link (the sharer's, not the tool author's — see rules)",
"slides": [
{ "type": "hook" },
{ "type": "claim", "n": "1/12", "claim": "Quoted or paraphrased claim, ≤200 chars",
"verdict": "misleading", "evidence": "One-sentence reality, ≤160 chars" },
{ "type": "cta", "headline": "Run it on anything",
"lines": ["the sharer's own links — ask, don't assume"] }
]
}
Verdicts: confirmed / plausible / misleading / false / unverifiable / not checked — the same six the report uses, and the renderer rejects anything else rather than guessing a colour. Pick 3–4 claim slides — the spiciest verdicts with the strongest evidence, not the first four. Hook and CTA slides bookend them.
A not checked claim renders, but think before using one: it carries no verdict and no evidence by definition, so it makes a weak slide and a reader may take the empty cell for a finding.
What ships with it
3 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.
- 12d ago First seen · 54 lines · 93 tokens per session scan A 952544ca787e
share is a skill published in the GitHub repository SerhiiKorniienko/bullshit-detector (142 stars, last pushed 8d ago), licensed MIT. It adds 93 tokens to every session and 857 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
interview-transcription
Transcription, recording management, and quote extraction. Use when processing audio/video or generating timestamped transcripts.
photo-metadata
Embeds photo IPTC/EXIF/XMP metadata, caption, credit, alt text, license, AI-source label, GPS stripping, and C2PA credentials.
video-transcribe
Batch Whisper transcription of video or audio (WAV, podcasts) with a re-runnable provenance record. Use to transcribe recordings.
video-dashboard
Aggregates transcript and frame data into an interactive web dashboard. Use for content, topic, or sentiment analysis.
video-download
Collects videos from public social accounts. Use to download or scrape from Twitter, TikTok, YouTube, Instagram, or Facebook.
video-frames
Extracts and visually analyzes frames from video files. Use for frame extraction, vision analysis, on-screen text, or frame grids.