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 lenzhq/lenz-mcp --skill lenz-fact-checkgit clone --depth 1 https://github.com/lenzhq/lenz-mcpWrote 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/lenzhq/lenz-mcp/lenz-fact-check)<a href="https://agentmods.dev/skills/lenzhq/lenz-mcp/lenz-fact-check"><img src="https://agentmods.dev/badge/skills/lenzhq/lenz-mcp/lenz-fact-check/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/lenzhq/lenz-mcp/lenz-fact-check"><img src="https://agentmods.dev/badge/skills/lenzhq/lenz-mcp/lenz-fact-check.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 | $0.00168 | $0.01490 |
| Opus 5 | $0.00084 | $0.00745 |
| Sonnet 5 | $0.00034 | $0.00298 |
| Haiku 4.5 | $0.00017 | $0.00149 |
Grade A, and why
lenz-fact-check 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 10d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lenz Fact-Check
Fact-check factual claims against independent web sources using Lenz's hosted MCP tools. Lenz runs a claim through a multi-model pipeline (research → debate → adjudication) and returns a verdict with bucketed confidence. It checks claims against the open web, independent of whatever context the model was given — so it complements groundedness/faithfulness checkers, it does not replace them.
Prerequisite: the Lenz MCP must be connected
This skill drives the Lenz MCP server (https://lenz.io/mcp) and its tools:
assess_claim, verify_claim, get_verification, select_claims, ask_followup, check_usage. If those
tools are not available, do not try to fact-check by other means — tell the
user to connect Lenz first (OAuth for clients that support it, or a free API key),
per https://github.com/lenzhq/lenz-mcp, then retry.
Workflow
-
Extract the atomic claims. Break the input into discrete, individually checkable factual statements — one assertion each. Skip opinions, predictions, recommendations, and subjective statements; Lenz checks facts, not judgments. If there is no checkable factual claim, say so plainly and stop.
-
Assess each claim with
assess_claim(fast, ~10s). It returns a verdict (True / Mostly True / Mixed / Mostly False / False) and a bucketed confidence per claim. Ifassess_claimreports the claim is ambiguous with candidate readings, pick the reading that matches the user's intent (or ask which they mean), then re-assess that reading. -
Escalate to
verify_claimonly when warranted.verify_claimis a deep, sourced, ~90s investigation that costs an order of magnitude more credits thanassess_claim— reserve it for claims that are consequential (health, safety, legal, financial, reputational), came back Mixed or low-confidence fromassess_claim, or that the user explicitly wants investigated. Do not spendverify_claimon trivial or clearly-true claims. When you do escalate but the user wants speed or is short on credits, passdepth: "low"— a shallower research pass (fewer sources, faster, the same models) at half the credits. Keep the defaultstandarddepth for consequential claims, where breadth of evidence is the point.verify_claimreturns atask_id; pollget_verification(task_id)until its status iscompleted. If it returnsneeds_input(multiple claims or an ambiguity), useselect_claimsto choose which claim text(s) to run. To dig further into a finishedverify_claim, useask_followupwith itsverification_id.
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.
- 10d ago First seen · 106 lines · 168 tokens per session scan A 263b3e7e0cb2
lenz-fact-check is a skill published in the GitHub repository lenzhq/lenz-mcp (0 stars, last pushed 6d ago), licensed Apache-2.0. It adds 168 tokens to every session and 1,490 once invoked, about $0.0008 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 2026-08-31.
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