Claude Code Skills Marketplace is a collection and marketplace of skills, plugins, agents, and instructions that extend Claude Code with specialized development workflows. It is for developers who want to install existing workflows or create, validate, and package their own Claude Code skills.
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 daymade/claude-code-skills --skill fact-checkergit clone --depth 1 https://github.com/daymade/claude-code-skillsWrote 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/daymade/claude-code-skills/fact-checker)<a href="https://agentmods.dev/skills/daymade/claude-code-skills/fact-checker"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/fact-checker/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/daymade/claude-code-skills/fact-checker"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/fact-checker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00064 | $0.02042 |
| Opus 5.5 | $0.00026 | $0.00817 |
| Sonnet 5.5 | $0.00013 | $0.00408 |
| Haiku 4.5 | $0.00006 | $0.00204 |
Grade A, and why
fact-checker 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- fact-checker — 91% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fact Checker
Verify factual claims in documents and propose corrections backed by authoritative sources.
When to use
Trigger when users request:
- "Fact-check this document"
- "Verify these AI model specifications"
- "Check if this information is still accurate"
- "Update outdated data in this file"
- "Validate the claims in this section"
Workflow
Copy this checklist to track progress:
Fact-checking Progress:
- [ ] Step 1: Identify factual claims
- [ ] Step 2: Search authoritative sources
- [ ] Step 3: Compare claims against sources
- [ ] Step 4: Generate correction report
- [ ] Step 5: Apply corrections with user approval
Step 1: Identify factual claims
Scan the document for verifiable statements:
Target claim types:
- Technical specifications (context windows, pricing, features)
- Version numbers and release dates
- Statistical data and metrics
- API capabilities and limitations
- Benchmark scores and performance data
Skip subjective content:
- Opinions and recommendations
- Explanatory prose
- Tutorial instructions
- Architectural discussions
Step 2: Search authoritative sources
For each claim, search official sources:
AI models:
- Official announcement pages (anthropic.com/news, openai.com/index, blog.google)
- API documentation (platform.claude.com/docs, platform.openai.com/docs)
- Developer guides and release notes
Technical libraries:
- Official documentation sites
- GitHub repositories (releases, README)
- Package registries (npm, PyPI, crates.io)
General claims:
- Academic papers and research
- Government statistics
- Industry standards bodies
Search strategy:
- Use model names + specification (e.g., "Claude Opus 4.5 context window")
- Include current year for recent information
- Verify from multiple sources when possible
Step 3: Compare claims against sources
Create a comparison table:
| Claim in Document | Source Information | Status | Authoritative Source |
|---|---|---|---|
| Claude 3.5 Sonnet: 200K tokens | Claude Sonnet 4.5: 200K tokens | ❌ Outdated model name | platform.claude.com/docs |
| GPT-4o: 128K tokens | GPT-5.2: 400K tokens | ❌ Incorrect version & spec | openai.com/index/gpt-5-2 |
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.
- 1mo ago First seen · 297 lines · 64 tokens per session scan A 1a09e288c431
fact-checker is a skill published in the GitHub repository daymade/claude-code-skills (1,473 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 2,042 once invoked, about $0.0003 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.
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