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 btc-bottom-modelgit 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/btc-bottom-model)<a href="https://agentmods.dev/skills/star23/day1global-skills/btc-bottom-model"><img src="https://agentmods.dev/badge/skills/star23/day1global-skills/btc-bottom-model/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/btc-bottom-model"><img src="https://agentmods.dev/badge/skills/star23/day1global-skills/btc-bottom-model.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 389 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 | $0.00224 | $0.05812 |
| Opus 5 | $0.00112 | $0.02906 |
| Sonnet 5 | $0.00045 | $0.01162 |
| Haiku 4.5 | $0.00022 | $0.00581 |
Grade A, and why
btc-bottom-model 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- btc-bottom-model — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 407 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bitcoin Cycle Timing Model (BTC Market Heat Scoring System)
This skill helps you systematically assess where Bitcoin sits in its market cycle — from extreme fear (accumulation opportunity) to extreme greed (distribution/exit signal). Through a weighted evaluation of 13 on-chain, sentiment, and market indicators, it produces a 0-100 Market Heat Score and actionable buy/sell recommendations.
Use Cases
Use this skill when users ask the following types of questions:
- Has Bitcoin bottomed out / Can I buy the dip
- Is Bitcoin overheated / Should I take profits
- Where is BTC in the current cycle
- Do on-chain data support building or reducing a position
- What are long-term holders doing / Are ETFs buying or selling
- Is leverage too high / Is the market too greedy
Scoring System Overview
The model uses a weighted composite score from 0 to 100:
- 0 = Extreme Fear (historically the best buying opportunities)
- 100 = Extreme Greed (historically the best selling opportunities)
Indicators are split into two groups:
| Group | Weight | Purpose | Indicators |
|---|---|---|---|
| Daily Pulse | 32 / 100 | Fast-moving sentiment & flow signals | 4 indicators |
| Weekly Structure | 68 / 100 | Slow-moving on-chain & cycle signals | 9 indicators |
The heavier weighting on weekly/structural indicators reflects their superior track record in identifying cycle extremes.
Daily Pulse Indicators (32 points total)
For each indicator, use web_search to find the latest data, then score according to the normalization rules below. Each indicator's raw value is normalized to a 0-100 sub-score, then multiplied by its weight to get its contribution to the total.
D1: Bitcoin ETF Daily Net Flow (Weight: 12 points)
What it is: The net amount of money flowing into or out of spot Bitcoin ETFs (like BlackRock's IBIT, Fidelity's FBTC) each day. Large inflows = institutional buying pressure; large outflows = institutional selling pressure. This became one of the most important demand indicators after spot BTC ETFs launched in January 2024.
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 · 407 lines · 224 tokens per session scan A 1179df5fc9cd
btc-bottom-model is a skill published in the GitHub repository star23/Day1Global-Skills (1,048 stars, last pushed 23d ago), licensed MIT. It adds 224 tokens to every session and 5,812 once invoked, about $0.0011 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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