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 faberlens/hardened-skills --skill financial-datasets-hardenedgit clone --depth 1 https://github.com/faberlens/hardened-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/faberlens/hardened-skills/financial-datasets-hardened)<a href="https://agentmods.dev/skills/faberlens/hardened-skills/financial-datasets-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/financial-datasets-hardened/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/faberlens/hardened-skills/financial-datasets-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/financial-datasets-hardened.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 | $0.00111 | $0.02350 |
| Opus 5 | $0.00056 | $0.01175 |
| Sonnet 5 | $0.00022 | $0.00470 |
| Haiku 4.5 | $0.00011 | $0.00235 |
Grade A, and why
earnings-analysis-hardened 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.
This is a copy
89% identical to earnings-analysis — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Equity Research Earnings Update
Create professional EARNINGS UPDATE REPORTS analyzing quarterly results for companies already under coverage, following institutional standards (JPMorgan, Goldman Sachs, Morgan Stanley format).
Key Characteristics:
- Length: 8-12 pages
- Word Count: 3,000-5,000 words
- Tables: 1-3 summary tables (NOT comprehensive)
- Figures: 8-12 charts
- Turnaround: 1-2 days (within 24-48 hours of earnings)
- Audience: Clients already familiar with the company
- Focus: What's NEW - beat/miss, updated estimates, thesis impact
- Font: Times New Roman throughout (unless user specifies otherwise)
When to Use
Use when the user requests:
- "Create an earnings update for [Company] Q3 2024"
- "Analyze [Company]'s quarterly results"
- "Post-earnings report for [Company]"
- "Q1/Q2/Q3/Q4 update for [Company]"
Do NOT use if:
- User requests "initiation report" → Use different skill
- User requests "flash note" or "quick take" → Different format
- Company is not already covered → Need initiation first
Critical Requirements
1. Speed & Timeliness
- Publish within 24-48 hours of earnings release
- Focus on NEW information only
- Don't rehash company background extensively
2. Beat/Miss Analysis
- Lead with whether company beat or missed estimates
- Quantify variances (e.g., "Revenue beat by $120M or 3%")
- Explain WHY results differed from expectations
3. Summary Format
- Keep tables to 1-3 (summary only, not comprehensive)
- No full P&L/Cash Flow/Balance Sheet (just key metrics)
- Assume reader has seen initiation report
4. Citations & Source Attribution ⭐⭐⭐ MANDATORY
CRITICAL: Properly cite all data with SPECIFIC sources and CLICKABLE HYPERLINKS.
Include specific citations WITH CLICKABLE LINKS in every figure and table:
Source: Q3 2024 10-Q filed November 8, 2024; Company earnings release
[Hyperlink "10-Q" to: https://www.sec.gov/cgi-bin/viewer?accession=...]
[Hyperlink "earnings release" to: https://investor.company.com/news/q3-2024]
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.
- 9d ago First seen · 238 lines · 111 tokens per session scan A 7fa37382b18d
earnings-analysis-hardened is a skill published in the GitHub repository faberlens/hardened-skills (23 stars, last pushed 4mo ago), licensed MIT. It adds 111 tokens to every session and 2,350 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to earnings-analysis, differing in 11 lines, and is treated as a copy.
Other skills, from other repositories
risk-metrics-calculation
Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.
paypal-integration
Integrate PayPal payment processing with support for express checkout, subscriptions, and refund management. Use when implementing PayPal payments, processing online transactions, or building e-commerce checkout flows.
stripe-integration
Implement Stripe payment processing for robust, PCI-compliant payment flows including checkout, subscriptions, and webhooks. Use when integrating Stripe payments, building subscription systems, or implementing secure checkout flows.
creating-financial-models
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions.
analyzing-financial-statements
\"This skill calculates key financial ratios and metrics from financial statement data for investment analysis\".
floe-guard
Know what every AI call really costs — floe-guard meters STT + TTS + LLM + telephony per call (Pipecat, LiveKit — Python & TypeScript), keeps a live ledger of real spend, and hard-stops the next turn before it crosses a USD ceiling. Free Coverage Score + 7-day history on connect. Use when an agent's spend must be seen…