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 haskaomni/serenity-skill --skill serenity-alphagit clone --depth 1 https://github.com/haskaomni/serenity-skillWrote 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/haskaomni/serenity-skill/serenity-alpha)<a href="https://agentmods.dev/skills/haskaomni/serenity-skill/serenity-alpha"><img src="https://agentmods.dev/badge/skills/haskaomni/serenity-skill/serenity-alpha/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/haskaomni/serenity-skill/serenity-alpha"><img src="https://agentmods.dev/badge/skills/haskaomni/serenity-skill/serenity-alpha.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 | $0.00090 | $0.02404 |
| Opus 5 | $0.00045 | $0.01202 |
| Sonnet 5 | $0.00018 | $0.00481 |
| Haiku 4.5 | $0.00009 | $0.00240 |
Grade A, and why
serenity-alpha 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 11d 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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Serenity Alpha
Core Principle
Do not ask only whether the news is impressive. Ask whether an already-observable demand change can rewrite a smaller company's financial statements.
Use this skill to convert news into a testable alpha hypothesis:
news -> observed demand change -> revenue/profit transmission -> small-cap elasticity -> validation path
Treat outputs as research hypotheses, not investment advice. Verify current prices, market caps, filings, earnings calls, and news from reliable sources before naming securities or making time-sensitive claims.
Optional SEC Data Assist
For U.S.-listed companies, use SEC filings as the factual base for reported fundamentals and management disclosure when available. edgartools is a good optional helper for this step because it can retrieve company filings, XBRL financial statements, filing text, insider transactions, ownership forms, and recent 8-K disclosures.
If the environment does not already have it, install with pip install edgartools or uv pip install edgartools. The import package is edgar, not edgartools. SEC access requires an identity; set EDGAR_IDENTITY="Name [email protected]" in the environment or call from edgar import set_identity; set_identity("[email protected]") before requests.
Minimal usage pattern:
from edgar import Company
company = Company("AAPL")
filings = company.get_filings(form="10-Q")
financials = company.get_financials()
income = financials.income_statement()
Use it to support the analysis, not to replace the framework:
- Pull the latest 10-K, 10-Q, and relevant 8-K filings before judging whether a demand change has reached reported numbers.
- Use XBRL financials for historical revenue, gross profit, margin, cash flow, balance sheet, share count, and segment clues.
- Search filing text and MD&A for demand-driver terms, customer concentration, backlog/order commentary, capacity, pricing, supply constraints, and risk-factor changes.
- Check Form 4, 13D/G, or 13F data only as supporting context; do not treat ownership activity as proof of the alpha thesis.
- Keep non-SEC data separate: current price, market cap, valuation multiples, sell-side estimates, TAM, channel checks, and industry pricing usually require other current sources.
What ships with it
2 files 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.
- 11d ago First seen · 231 lines · 90 tokens per session scan A 5a2641c62ada
serenity-alpha is a skill published in the GitHub repository haskaomni/serenity-skill (632 stars, last pushed 1mo ago), licensed MIT. It adds 90 tokens to every session and 2,404 once invoked, about $0.0005 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 2026-08-30.
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