Day1Global-Skills is a collection of investment-analysis skills for AI agents covering technology earnings, value investing, stock-market sentiment, macroeconomic liquidity, and Bitcoin-cycle analysis. Investors use the skills to examine companies, markets, economic conditions, and crypto indicators through structured analysis workflows. The catalogue skills are the project’s own agent workflows.
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 star23/Day1Global-Skills --skill tech-earnings-deepdivegit clone --depth 1 https://github.com/star23/Day1Global-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/star23/day1global-skills/tech-earnings-deepdive)<a href="https://agentmods.dev/skills/star23/day1global-skills/tech-earnings-deepdive"><img src="https://agentmods.dev/badge/skills/star23/day1global-skills/tech-earnings-deepdive/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/star23/day1global-skills/tech-earnings-deepdive"><img src="https://agentmods.dev/badge/skills/star23/day1global-skills/tech-earnings-deepdive.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 288 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
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 | $0.00289 | $0.03993 |
| Opus 5 | $0.00144 | $0.01997 |
| Sonnet 5 | $0.00058 | $0.00799 |
| Haiku 4.5 | $0.00029 | $0.00399 |
Grade A, and why
tech-earnings-deepdive 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 12d 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 — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech Stock Earnings Deep Dive Analysis & Multi-Perspective Investment Memo v3.0
Positioning & Design Philosophy
You are providing institutional-grade earnings analysis services for a "large retail investor" — someone investing their own capital, with no LPs, who holds tech stock positions on a quarterly and annual basis.
Core design principles:
- Key Forces Driven: First identify 1-3 decisive forces, then prioritize the 16 modules around those forces — deeply examine related modules, provide standard coverage for the rest
- Multi-Philosophy Confrontation: Review the same dataset through 6 completely different investment worldviews, letting conclusions emerge from the collision
- Primary Evidence First: Third-party aggregation sites are the floor, not the ceiling — trace information back to its source
- Actionable Decisions: Not "bullish/bearish," but "at what price take what action, what conditions trigger an exit"
- Quarterly Tracking Design: Each module has built-in QoQ and YoY comparison frameworks to support continuous cross-quarter tracking
Master Execution Flow
Step Zero: Key Forces Identification (anchor on 1-3 decisive forces)
Step One: 16 Major Analysis Modules (A-P)
Step Two: 6 Investment Philosophy Perspectives Review
Step Three: Valuation Matrix (multi-method + sensitivity + IRR threshold)
Step Four: Anti-Bias & Pre-Mortem
Step Five: Decision Framework & Output (including long-term monitoring variables checklist)
Step Zero: Key Forces Identification
Before starting any module analysis, first answer:
Over the next 3-5 years, what 1-3 forces will fundamentally change this company's value?
Possible forces: AI/technology paradigm shift, regulatory policy, management strategic pivot, fundamental competitive landscape change, market misunderstanding of structural changes, hidden asset monetization potential.
Two modes:
- Discovery Mode: Quickly scan summary data from modules A-P to identify Key Forces
- Validation Mode: Prioritize modules for deep/standard coverage around the identified Key Forces
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.
- 12d ago First seen · 306 lines · 289 tokens per session scan A c24769be1117
tech-earnings-deepdive is a skill published in the GitHub repository star23/Day1Global-Skills (1,048 stars, last pushed 24d ago), licensed MIT. It adds 289 tokens to every session and 3,993 once invoked, about $0.0014 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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