bullshit-detector
Fact-check and hype-audit content. Extracts the discrete claims from a video, article, tweet, or PDF, verifies each against…
Turn a BS report (or any analysis result) into ready-to-paste posts for X/Twitter, LinkedIn, Facebook, Reddit, Hacker News, or a newsletter issue — plus a branded image carousel (PNGs + PDF) for visual platforms. Use when the user wants to share, post, publish, or promote a
$ npx -y skills add SerhiiKorniienko/bullshit-detector --skill share --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/shareContext preview
The summary Claude sees to decide when to auto-load this skill.
Turn a BS report (or any analysis result) into ready-to-paste posts for X/Twitter, LinkedIn, Facebook, Reddit, Hacker News, or a newsletter issue — plus a branded image carousel (PNGs + PDF) for visual platforms. Use when the user wants to share, post, publish, or promote a
name: share description: Turn a BS report (or any analysis result) into ready-to-paste posts for X/Twitter, LinkedIn, Facebook, Reddit, Hacker News, or a newsletter issue — plus a branded image carousel (PNGs + PDF) for visual platforms. Use when the user wants to share, post, publish, or promote a report, asks for "a thread", "a LinkedIn post", "a carousel", or "format this for X".
Take a finished report and produce platform-native content, ready to paste. Nothing generic: each platform gets its own format, length, and link etiquette.
1. **Locate the report.** A file the user points at, the report from this conversation, or — if there is none — offer to run the `bullshit-detector` skill first. 2. **Ask which platforms** if not stated. Default set: X thread + LinkedIn post. 3. **Write the posts** following the per-platform specs in [PLATFORMS.md](PLATFORMS.md) exactly — hooks, length limits, link placement. Output each as a separate fenced block the user can copy verbatim. 4. **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).
{
"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.
Read a real report → — 60 claims from a 2.7M-view "the AI bubble is popping" video, 44 of them individually searched, every verdict linked. Agent skills that fact-check the internet.
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