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 dividend-analysisgit 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/dividend-analysis)<a href="https://agentmods.dev/skills/yennanliu/investskill/dividend-analysis"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/dividend-analysis/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/dividend-analysis"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/dividend-analysis.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 | $0.00021 | $0.07608 |
| Opus 5.5 | $0.00008 | $0.03043 |
| Sonnet 5.5 | $0.00004 | $0.01522 |
| Haiku 4.5 | $0.00002 | $0.00761 |
Grade A, and why
dividend-analysis 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 9d 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:
- data-model-radar — 86% identical, 1,370 lines differ
How it starts
The opening of the file, as written. The whole thing — 685 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capital Allocation Analysis (Dividend, Buyback, M&A & FCF Deployment)
⚠️ Data Verification — Do This Before Any Analysis
Before running any analysis, always retrieve the latest market data for the ticker:
- Fetch current price — use web search or ask the user for the live price, 52-week range, and market cap. Never assume a price from training data.
- Confirm key figures — recent earnings, revenue, key ratios (P/E, P/S, etc.) as applicable to this skill.
- 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 live data is unavailable, display this warning before proceeding:
⚠️ 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.
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:
Data & Sources
As of: <date the figures represent, e.g. 2026-06-30>
Source: <primary docs — SEC EDGAR 10-K/10-Q, company IR, FRED, exchange data …>
Retrieval: <pasted by user | web/tool retrieval | model memory>
Confidence: <HIGH | MEDIUM | LOW>
Retrieval: model memorymust be paired withConfidence: LOW— memory is a placeholder until confirmed against a primary source.- Mixed sources: list each with its own as-of date rather than blending them.
- Sources disagree on the same figure (a closing price, a deal term): prefer the primary source (SEC filing, company release, exchange data) over an aggregator, then the most recent dated one. Show both values with their sources and dates, say which you used and why, and never average them.
- Data the user pasted is reported as
pasted by user; do not upgrade its confidence beyond what the user's own source supports.
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.
- 9d ago Changed · +1 lines 86a7abd4108f
- 25d ago Changed · -2 lines 9cfef297fea7
- 26d ago Changed · +20 lines a255e4bede49
- 1mo ago First seen · 666 lines · 21 tokens per session scan A 163aa522bf74
dividend-analysis is a skill published in the GitHub repository yennanliu/InvestSkill (251 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 7,608 once invoked, about $0.0001 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 2026-08-30.
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