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 yennanliu/InvestSkill --skill fact-checkgit clone --depth 1 https://github.com/yennanliu/InvestSkillWrote 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/yennanliu/investskill/fact-check)<a href="https://agentmods.dev/skills/yennanliu/investskill/fact-check"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/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/yennanliu/investskill/fact-check"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/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 | $0.00081 | $0.06861 |
| Opus 5.5 | $0.00032 | $0.02744 |
| Sonnet 5.5 | $0.00016 | $0.01372 |
| Haiku 4.5 | $0.00008 | $0.00686 |
Grade A, and why
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 8d 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fact Check — Verify the Numbers, Cite the Sources
⚠️ Data Verification — Do This Before Any Analysis
Before verifying anything, establish what data each claim actually needs:
- Fetch current price only where a claim needs it — market claims (price, market cap, yield, multiples "today") are checked against a live quote for the report's as-of date and today's; use web search or ask the user. Reported figures, historical comparisons, and
--recomputeruns need no live quote — do not request one, and do not let its absence block them. Never assume a price from training data. - Confirm key figures — for this skill the "key figures" are the sources themselves: the filing (10-K / 10-Q / 8-K / proxy / Form 4 / 13F), the IR release, the FRED series, the exchange or issuer data, or the document the user pasted. Retrieve or request them before judging a single claim. A claim checked against memory is not checked.
- State your data source — fill in the
Data & Sourcesheader (next section) so the origin, as-of date, retrieval path, and confidence of every figure are explicit at the top of the output. - Flag stale data explicitly — if a live quote is needed for a market claim and unavailable, display this warning before proceeding, and mark those claims
❓or🕒rather than guessing:
⚠️ Live data unavailable. The following analysis uses training-data estimates which may be significantly out of date. Verify all prices and metrics before making any decisions.
The warning describes the inputs actually used: when every claim was checked against user-supplied documents, say so instead of implying training-data estimates were involved. Never silently substitute training-data estimates for current prices. When in doubt, ask the user to paste the latest quote.
📋 Data & Sources Header — Open Every Output With It
The first thing in the output is this provenance block, filled in — never left as placeholders. It is the standard documented on the Data & Accuracy page and the first thing result-validator looks for:
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.
- 8d ago Changed · +1 lines c8617eef93aa
- 24d ago Changed · +2 lines 07465d467d1e
- 25d ago First seen · 286 lines · 81 tokens per session scan A dc75e80b8e40
fact-check is a skill published in the GitHub repository yennanliu/InvestSkill (250 stars, last pushed yesterday), licensed MIT. It adds 81 tokens to every session and 6,861 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 2026-09-25.
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